East Asia Regional Biometric Conference 2007

Abstract Book

9-11 December 2007
University of Tokyo, Japan

This conference is organaized by
The Biometric Society of Japan
wwwsoc.nii.ac.jp/jbs/index_e.html

Table of contents
Organizing and Scientific Programme Committee ....................... 2
Notes for Presenters ...................................................................... 3
Programme over view ................................................................... 4
Sunday 9th
Monday 10th
Tuesday 11th

Scientific Programme.................................................................... 6

Opening Ceremony: Introduction to IBS and its regional activities
Invited Session: Clinical Trials
Contributed Session: Epidemiology
Contributed Session: Bioinformatics
Invited Session: Bioinformatics
Keynote Lecture
Invited Session: Epidemiology
Contributed Session: Clinical Trials 1
Contributed Session: Biostatistics in General
Contributed Session: Clinical Trails 2
Poster Session
Biometric Seminar: Global Clinical Trials

Abstracts ........................................................................................ 13
Keynote Lecture
Opening Ceremony(K:01)(O:01-05)

Invited Sessions
Contributed Sessions
Poster Session(I:01-09)(C:01-21)(P:01-12)

List of Sponsors ............................................................................. 62

Organizing and Scientific Programme Committee
Chair:
Toshiro Tango, National Institute of Public Health (Japan)
Secretary:
Kazue Yamaoka, National Institute of Public Health (Japan)
Treasurer:
Chikuma Hamada, Tokyo University of Science (Japan)
Scientific Programme Chair:Tosiya Sato, Kyoto University (Japan)
Members:
Manabu Iwasaki, Seikei University (Japan)
Hirohisa Kishino, University of Tokyo (Japan)
Masaaki Matsuura, Japanese Foundation for Cancer Research (Japan)
Yutaka Matsuyama, University of Tokyo (Japan)
Mihoko Minami, Institute of Statistical Mathematics (Japan)
Toshihiko Morikawa, Kurume University (Japan)
Yasuo Ohashi, University of Tokyo (Japan)
Hironori Sakai, Mitsubishi Pharma Corporation (Japan)
Hiroyuki Uesaka, Lilly Research Laboratories Japan (Japan)
Takashi Yanagawa, Kurume University (Japan)
Thomas Louis, IBS President, Johns Hopkins University (USA)Ashwini Mathur, IBS General Secretary, Novartis International ClinicalDevelopment Center (India)
Taerim Lee, Korean Region President, National Open University (Korea)
Seungho Kang, Korean Region Secretary, Ewha Women's University (Korea)
Byung Soo Kim, Yonsei University (Korea)
H. Sridhara, Indian Region President, University of Agricultural Sciences (India)
Ji-Qian Fang, National Group China Secretary, SunYat-Sen University of Medical
Science (China)
Ayanendranath Basu, Indian Statistical Institute (India)-2-

Notes for Presenters
Oral Session
Please come to the Ichijo Hall at least 10 minutes before your session’s starting time andmeet with your chairperson, who is supposed to confirm all speakers of the session.
An LCD projector is available for presentation. Notebook computers can be connected
to analog RGB interfaces.
Instructions for Speakers in Oral Sessions• Oral presentations are scheduled so that each speaker will have at least 15 minutes.
• An LCD projector can be used. If you would like to use OHP, please contact with
EAR-BC’07 staff.
• Your own notebook computer can be connected to an LCD projector which has ananalog RGB interface (DSUB15).
• If you don’t bring a notebook computer, please contact with EAR-BC’07 staff
members. Please prepare your presentation file which can work on Microsoft
PowerPoint 97-2003.

Poster Session
Posters will be on display at the lobby of the conference
venue. Posters may be mounted on the morning ofDecember 9th (at or before the coffee break). Your
poster has been assigned a specific “poster number” that
corresponds to a specific portion of a poster board.
Your poster is to be displayed for 2 days. In order to

115cm
Available Space

ensure that those who wish to discuss it with you have achance to do so, it is highly recommended that you be atyour poster during each of the scheduled coffee breaks.
Posters must be removed by 17:00 on December 10th.

180cm

85cm

Instructions for Presenters in Poster Session
Available space for each poster is: Width 85cm andHeight 115cm.

-3-

Programme over view
Sunday,9th
8:308:55-9:00
9:00-10:30

Registration

Opening Address
Toshiro Tango, Japanese Region President
Opening Ceremony:
Introduction to IBS and its regional activities(O:01-05)
Chair: Ashwini Mathur
Coffee Break

11:00-12:30

Invited Session: Clinical Trials(I:01-03)
Chair. Shigeyuki Matsui
Lunch Break

14:00-15:30

Contributed Session: Epidemiology(C:01-04)
Chair: Yutaka Matsuyama
Coffee Break

16:00-17:30

Contributed Session: Bioinformatics(C:05-09)
Chair: Seiya Imoto

Monday,10th
8:309:00-10:30

Registration

Invited Session: Bioinformatics(I:04-06)
Chair: Masaaki Matsuura
Coffee Break

11:00-12:00

Keynote Lecture(K:01)
Chair: Thomas Louis
Lunch Break

13:30-15:00

Invited Session: Epidemiology(I:07-09)
Chair: Tosiya Sato
Coffee Break

15:30-17:00

17:30-

Contributed Session: Clinical Trials 1(C:10-13)
Chair: Satoshi Morita
Conference Dinner at Capo PELLICANO-4-

Tuseday,11th
8:309:00-10:30

Registration

Contributed Session: Biostatistics in General(C:14-17)
Chair: Shizue Izumi
Coffee Break

11:00-12:30

Contributed Session: Clinical Trails 2(C:18-21)
Chair: Osamu Komiyama

12:30-12:35

Closing Remarks
Tosiya Sato, Scientific Programme Chair

Sunday,9th-Monday,10th
9:00-17:00

Poster Session

Other Activities
Tuseday,11th
13:30-17:00

Biometric Seminar(Sponsored by Japanese Biometric Society)-5-Scientific Programme

Opening Ceremony: Introduction to IBS and its regional activities
Chair: Ashwini Mathur (the IBS General Secretary, Indian Region)th
Sunday,9Thomas Louis (the IBS President, ENAR)
9:00-10:30“Our Future as History”
Jiqian Fang (National Group China Secretary)“Outcome Measurement for TCM and Challenge to Statistics”
H. Sridhara (Indian Region President)“Sixty Years of IBS(IR) and Future of Biometry in India”
Taerim Lee (Korean Region President)“Web Based Biostatistics Education Hub for East Asian Region”
Toshiro Tango (Japanese Region President)“ Major Activities of Biometric Society of Japan and Japanese
Biostatisticians: Past, Present and Future”

O:01
O:02
O:03
O:04
O:05

Invited Session: Clinical Trials
Chair. Shigeyuki Matsui (Kyoto University School of Public Health, Japan)
Sunday,9th
11:00-12:30

Yuantao Hao (Sun Yat-sen University, China), Jiqian Fang,
Xinyuan Song
“Non-linear Factor Analysis Model and its Application to Evaluating
the Equivalence of Questionnaires”
Satoshi Morita (Kyoto University Hospital, Japan)“Determining the Effective Sample Size of a Parametric Prior”
Masako Nishikawa (National Institute of Public Health, Japan),Toshiro Tango , Megu Ohtaki“ Simple Statistical Tests for New Composite Hypotheses in
Randomized Clinical Trial Reflecting the Relative Clinical
Importance”

-6-

I:01

I:02

I:03

Sunday,9th
14:00-15:30

Sunday,9th
16:00-17:30

Contributed Session: Epidemiology
Chair: Yutaka Matsuyama (University of Tokyo, Japan)
Ya Fang (University of Xiamen, China)“ Probability Prediction in Multistate Survival Models for Patientswith Chronic Myeloid Leukaemia”

C:01

Sachiko Tanaka (Tokyo University of Science, Japan)“ Inverse Probability Weighted Estimators in the Stratified Nested
Case-Control Sampling Methods”

C:02

Koji Yonemoto (Kyushu University, Japan), Atsushi Kawaguchi,
Yumihiro Tanizaki, Yutaka Kiyohara, Takashi Yanagawa, Young K.
Truong“Application of Functional ANOVA Models for Hazard Regression
to the Hisayama Data”

C:03

Shizue Izumi (Oita University, Japan), Yoshinori Fujii“ Estimating the Power of the Likelihood Ratio Test in the CohortBased Nested Case-Control Studies”

C:04

Contributed Session: Bioinformatics
Chair: Seiya Imoto (University of Tokyo, Japan)
Tomonori Oura (Kyoto University School of Public Health, Japan),
Shigeyuki Matsui, Koji Kawakami“ Sample Size Calculations Based on Exact Distribution of False
Discovery Proportion in Microarray Experiments”
Akihiro Hirakawa (Pharmaceuticals and Medical Devices Agency,
Japan), Yasunori Sato, Chikuma Hamada, Isao Yoshimura“A Test Statistic Based on Shrunken Sample Variance for Identifying
Differentially Expressed Genes in Microarray Data Analysis”

C:05

C:06

Yoichi M. Ito (University of Tokyo, Japan), Yasuo Ohashi“ Measurement Scale Categorization and Its Resolution. A Gene
Reduction Approach to the Microarray Data Analysis”

C:07

Takeharu Yamanaka (National Kyushu Cancer Center, Japan),
Shigeyuki Matsui“Survival Prediction Using Top Ranking Significant Genes in Cancer
Prognostic Studies with Microarrays”

C:08

Mira Park (Eulji University, Korea), Yoo-Jin Jang, Myung-Hoe Huh“ Applications of Minimal Spanning Tree and Self-Organizing Map
in Microarray Data Analysis”

C:09

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Invited Session: Bioinformatics
Chair: Masaaki Matsuura (Japanese Foundation for Cancer Research, Japan)
Monday,10th
9:00-10:30

Jae Won Lee (Korea University, Korea), JungBok Lee, Shin-Jae
Lee“ Statistical Tools for Protein Identification Based on Mass
Spectrometry Data: A review”
Seiya Imoto (University of Tokyo, Japan)“Bioinformatics Approach Towards Drug Target Gene Discovery”
Masaru Ushijima (Japanese Foundation for Cancer Research)“Analyses of OMICS Data in Medical Informatics”

I:04

I:05
I:06

Keynote Lecture
Chair: Thomas Louis (Johns Hopkins Bloomberg School of Public Health, USA)th

Monday,10
11:00-12:00

Toshiro Tango (National Institute of Public Health, Japan)“Tests for Spatial Randomness: Detection of Disease Clustering andOutbreak Threat”

K:01

Invited Session: Epidemiology
Chair: Tosiya Sato (Kyoto University School of Public Health, Japan)thMonday,10
Ayanendranath Basu (Indian Statistical Institute, India), Sumit
13:30-15:00
Gupta, Wen-tao Huang“ Tests of Hypothesis about the Kappa Statistic based on theGoodness-of-fit Approach”
Satoshi Hattori (Kurume University, Japan), Takashi Yanagawa“Mantel-Haenszel Estimators for Irregular Sparse K 2 X J Tables”

I:07

Kunihiko Takahashi (National Institute of Public Health, Japan),Toshiro Tango“Scan Statistics for Disease Clustering”

I:09

-8-

I:08

Contributed Session: Clinical Trials 1
Chair: Satoshi Morita (Kyoto University Hospital, Japan)
Monday,10th
15:30-17:00

Tuesday,11th
9:00-10:30

Shiro Tanaka (University of Tokyo, Japan), Yutaka Matsuyama,
Yasuo Ohashi“ Estimating Surrogate Endpoints Defined by Principal Causal
Effects”

C:10

Anthony Hayter (University of Denver, USA), Chen-ju Lin“Recent Advances in Stepdown Procedures for Identifying Inferiority
Among Treatments in Clinical Trials”

C:11

Takahiro Hasegawa (SHIONOGI & CO., LTD., Japan), Toshiro
Tango“Permutation Test Following Covariate-Adaptive Randomization in
Randomized Controlled Trials”

C:12

Tetsuhisa Miwa (National Institute for Agro-Environmental
Sciences, Japan)“Comparisons Between Three Treatments Including a Placebo and aControl by the Multiple Confidence Procedure”

C:13

Contributed Session: Biostatistics in General
Chair: Shizue Izumi (Oita University, Japan)
Lilia L. Ramirez-Ramirez (University of Waterloo, Canada), Mary
E. Thompson“ Statistical Inference for Outbreaks in a Population with a Contact
Network Structure”
Takashi Omori (Kyoto University School of Public Health, Japan),
Takashi Sozu, Isao Yoshimura“ Several Challenges by Biostatisticians for Developing a New
Animal Test Method”

C:14

C:15

Hyonggin An (Korea University, Korea)“Bayesian Analysis of Repeated Data with Many Zeros: Application
to the Longitudinal Adolescent Substance Abuse Study”

C:16

Tetsushi Komori (Bayer Yakuhin, Ltd., Japan), Takashi Ohmori,Tosiya Sato
“ Consistency of Signal Measures with Epidemiologic EffectMeasures”

C:17

-9-

Tuesday,11th
11:00-12:30

Contributed Session: Clinical Trails 2
Chair: Osamu Komiyama (Pfizer Japan Inc., Japan)
KyungMann Kim (University of Wisconsin-Madison, USA)“An Independent Statistical Center in Support of the Data and Safety
Monitoring Board: The Adenoma Prevention with Celecoxib (APC)
Trial”

C:18

JungBok Lee (Korea University, Korea),Byoung Cheol Jung,Hong
Euy Lim,Chol Shin
“An Improved Estimating Method of QT interval for Adjusting Heart
Rate: A Multivariate Approach”

C:19

Masayuki Henmi (Institute of Statistical Mathematics, Japan), John
B. Copas, Shinto Eguchi“ A Sensitivity Analysis Allowing for All Possible Selection
Processes of Studies in Meta Analysis”

C:20

Hiroyuki Uesaka (Eli Lilly Japan, Japan)“Sample Size Allocation to Regions in a Multiregional Trial”

C:21- 10 -

Poster Sessionth

9-10
9:00-17:00

Satoshi Teramukai (Kyoto University Hospital, Japan), Satoshi Morita“Bayesian Predictive Multi-Stage Design for Phase II Single-Arm Clinical Trials:
A Case Study”
Hyo-Il Park (Chong-ju University, Korea), Soo-Duck Lim, Joong-Jae Cho“Hypothesis Test Under the Additive Hazards Model”
Masahiko Gosho (Kowa Co. Ltd., Japan), Chikuma Hamada, Isao Yoshimura“ Study on the Criterion for Selecting the Working Correlation Structure in
Generalized Estimating Equation”
Yumiko Uematsu (Tokyo University of Science, Japan), Chikuma Hamada, Isao
Yoshimura“Statistical Method for Evaluating the Efficacy of a Drug for Osteoporosis on the
Occurrence of Bone Fractures as the Primary Endpoint”

P:01
P:02
P:03

P:04

Masayuki Yamada (Tokyo University of Science, Japan), Chikuma Hamada,Isao Yoshimura“ Study on the Effect of Random Effects Included in the Analysis Model for In
Vitro Pharmacological Data Adaptable to Four Parameter Logistic Model”

P:05

Tetsuji Tonda (Hiroshima University, Japan), Kenichi Satoh, Hiromi Kawasaki,
Takeshi Shimamoto, Teruyuki Nakayama, Kota Katanoda, Tomotaka Sobue,
Yasuto Sato, Naohito Yamaguchi, Megu Ohtaki“ Statistical Analysis of Spatial-Time Heterogeneity of Cancer Mortality Risk
Based on Growth Curve Model”

P:06

Kenichi Satoh (Hiroshima University, Japan), Hirokazu Yanagihara“Modified Cp in Multivariate Ridge Regression”
Keiichi Sugino (Mitsubishi Tanabe Pharma Corporation, Japan), Yasuo Ohashi,
Qian Gong“Multinational Clinical Trials in East Asia”
Keiko Otani (Hiroshima University, Japan), Megu Ohtaki, Keiko Hiyama,
Kenichi Satoh, Eiso Hiyama“ Two Dimensional Summarization for Affymetrix GeneChip Probe Level Data
Based on Functional States of Gene-ON/OFF”
Kazue Yamaoka (National Institute of Public Health, Japan), Toshiro Tango“Effects of Lifestyle Modification on Metabolic Syndrome: Resolution Rate andCombining Multiple Endpoints in Meta-Analysis”
Mihoko Minami (Institute of Statistical Mathematics, Japan)“ A New Feature Extraction Method from very Non-Normal Data: Analysis ofMultivariate Catch and Bycatch Data by Purse-Seine Tuna Fisheries”
Yukikazu Hayashi (Tokyo University of Science, Japan), Chikuma Hamada, Isao
Yoshimura“ Statistical Pharmacokinetic Modeling of Controlled-Release Analgesics
combining Immediate-Release and Sustained-Release Components to design anappropriate Drug Delivery”- 11 -

P:07
P:08

P:09

P:10
P:11

P:12

Tuesday, 11th
13:30-17:00

Domestic Meeting
Biometric Seminar: Global Clinical Trials.

(Sponsored by Japanese Biometric Society)- 12 -

Abstracts

Keynote Lecture(K:01)

Opening Ceremony(O:01-05)

Invited Sessions(I:01-09)

Contributed Sessions(C:01-21)

Poster Session(P:01-12)- 13 -

K:01

Tests for Spatial Randomness:
Detection of Disease Clustering and Outbreak Threat
Toshiro Tango1
1

Department of Technology Assessment and Biostatistics, National Institute of Public Health,
Saitama, Japan

In epidemiological studies, it is often of interest to evaluate whether a disease is randomly
distributed over time and/or space after adjusted for a known heterogeneity, which may provide
clues to the etiology of disease. To do this, we can apply tests for spatial randomness, or disease
clustering. On the other hand, in the aftermath of the World Trade Center attacks in September 11,
2001 and the anthrax-laden letters that followed in October 2001, a syndromic surveillance has beenpoised for deployment across USA. As a major analytical method for outbreak detection, a software
SaTScan of Kulldorff's spatial scan statistic, a test for disease clustering, has been implemented inseveral syndromic surveillance systems.
In this talk, first, I review existing tests for disease clustering and focus on two specificc tests forsome reason: one is called an index for disease clustering and the other is a scan statistic. Roles,
advantages and disadvantages of these test statistics are discussed. These tests are illustrated andcompared with several real temporal and spatial data. Second, I go on to describe the recent
development of scan statistics associated with timely detection of outbreak threat in syndromic
surveillance.

- 14 -

O:01

Our Future as History
Thomas A. Louis1
1

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

These are exciting and challenging times for the IBS and professional societies in general. The
challenges require that we actively plan for our future and consider "Our Future as History" becausethat which lies ahead becomes the present and the present becomes our history. To plan for the
future, I build on themes identified in my presidential address in Montreal, outlining our current
status and initiatives, predicting and highlighting implications that call for expansions of ourcurrent activities and creation of new ones. These will ensure that we create our desired future andthereby produce a proud legacy.

- 15 -

O:02

Outcome Measurement for TCM and Challenge to Statistics
Jiqian Fang1,Fengbin Liu 2
1

Research Center of Health Information; Department of Medical Statistics and Epidemiology,
School of Public Health, Sun Yat-Sen University 2The Teaching Hospital, Guangzhou University ofTraditional Chinese Medicine

Initially according to the philosophy of traditional Chinese medicine (TCM), the clinical effects of
TCM were evaluated bythe improvement on "ZHENGS". This sounds reasonable to TCM, butlacks common language with the modern western medicine. The "objective indexes" especially thelaboratory tests have had been adopted in evaluation of clinical effects by "modern"TCM although
there were no any relationship between the philosophy of TCM and these indexes and tests. This isreally unfair to TCM and makes some "modern" TCM people being mixed, even no longer be
subject to TCM seems to me. Good news is that QOL and PRO could be a common platform forboth of modern western medicine and TCM to work together andcompete with each other. In fact,
QOL and PRO have had been much familiar by TCM people for thousands years as part of theirtradition, only but the measurement of QOL and PRO having not been so systematic as those done
today in modern western medicine. Now some of the TCM people have been aware with this andstart to develop scientific-based instruments to measure QOL and PRO with the integration of richexperience of TCM and modern western medicine. It could be foreseen that the two systems of
medicine, TCM and modern western medicine, may continue to progress on their own track, shakehandand communicate on the common platform of QOL and PRO, for the same target of serving
the patients.However, the statistical methodology for the analyses of questionnaires on QOL and
PRO is far from mature today. There arefull of challenges to Statistics and Biostatistics. The
researchers regard the measures for the item taking "value" within finite options as a continuous
variable or a discrete variable with equal-distance integers, rather than an ordinal variable; And thena package of item response theory, factor analysis and structure equation model etc. becomes aroutine in data analysis. This is a big mistake but the non-statisticians have no way to get rid of. If
you do regard the response to such items as ordinal variables, how can you work out a series ofmultivariate analysis for the data of questionnaires as those for continuous variables? how can you
work out analysis for longitudinal observations by questionnaires? In one word, the platform of
QOL and PRO are also an arena for statisticians and bio-statisticians to play on. Do not miss it.

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O:03

Sixty Years of IBS(IR) and Future of Biometry in India
H.Sridhara 1
1

Biostatistics & Bioinformatics, Thrombosis Research Institute, India

The Biometric Society in India was established in 1947, simultaneously with the other four regions
viz. US, Briton, France and Australia. IBS(IR) is celebrating the Diamond Jubilee this year. This talktraces the history of growth and development of Biometry in India in general and the contributionsof Indian biometricians to the subject in particular. The implications of globalization on
research and development of biometry and the issues concerning that in coming years are also
discussed.

- 17 -

O:04

Web Based Biostatistics Education Hub for East Asian Region
Taerim Lee1
1

Department of Information & Statistics, Korea National Open University, Korea

e-Learning is an on-line education defined as the self paced or real time delivery of training andeducation over the internet to an end user device. In view of biostatistical education e-Learning has
many advantages such as reduction of education cost, repeated learning, customized education, andself-paced learning.
e-ABEH system for Biostatistics, Bioinformatics and Medical Informatics Education has improved
the lack of two-way communication and repetition, the main weakness of the existing face to faceclass and written text. And it has extended the opportunity of learner by operating a variety of
individual curriculum on the basis of e-learning.e-ABEH is an web based network under Far East
Asian Biometric Region consortium we call ABEH(Asian Biometric Education Hub) for
promoting cooperation among Far East Asian Biometric Region in regard to Biostatistics,
Bioinformatics , Medical Informatics and statistical consulting.
The aim of e-ABEH is to first initiate online research network that allows Far East Asian Biometric
University partners to continuously exchange Biostatistics, Bioinformatics and Medical Informatics
related academic and practical experiences.
Secondly it is to use the online research network for conducting collaborative research projects andpractical biostatistical consulting among the ABEH members. With these aims, three sub-research
themes and seven working group activity to be performed on the e-ABEH network.
This paper describes the e-learning community e-ABEH for Biostatistical education that anyone
who wants to study could study anywhere, anytime with web and multimedia system under the
Asian member regions consortium. Those e-learning contents will be constructed with thecomponents e-lecture, e-book, simulation experiment, web link for reference, computer-aided tutor
for statistical learning, self-evaluation system, and a statistical package for the practice of data
analysis and biostatistical consulting center.

- 18 -

O:05

Major Activities of Biometric Society of Japan and Japanese
Biostatisticians:Past, Present and Future.
Toshiro Tango1
1

Department of Technology Assessment and Biostatistics, National Institute of Public Health,
Saitama, JapanAt the end of 2005 Biometric Society of Japan (BSJ) cerebrated a quarter of a century of its
establishment, a 25-year period over which the number of BSJ members has increased to about 450
from 220 and it has engaged a wide range of research and education. In this talk, I shall review the
major activities of BSJ and the achievements that BSJ members have made by investigating the
temporal trend of published papers in such journals as Japanese Journal of Biometrics, Biometrics
and Statistics in Medicine. I will further investigate the Japanese achievements published in the
Encyclopedia of Statistical Sciences and the Cambridge Dictionary of Statistics. Based on theseinvestigations, I would like to discuss the conditions that, I think, could improve the present situationand infuence the future activities and status of Japanese Biostatisticians.

- 19 -

I:01

Non-linear Factor Analysis Model and its Application to Evaluating
the Equivalence of Questionnaires
Yuantao Hao1,Jiqian Fang1,Xinyuan Song2,Mick Power3
1

Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen
University, Guangzhou, China 2Department of Statistics, Hongkong Chinese University
3
Department of Clinical Psychiatry, University of Edinburgh, Edinburgh, UK

The increasing international collaboration in health-related quality of life research has created a needfor cross-culturally valid instruments for outcome assessment. According to the definition,
Equivalence concerns if there exists the same factor structure and factor loading matrix among
different language/culture versions of questionnaire from the aspect of factor analysis. Accordingly,
multi-group confirmatory factor analysis can be used to assess equivalence of questionnaires. In the
traditional confirmatory factor analysis, a set of observed variables is expressed as a linear
combination of a number of latent variables and a residual vector. There is a strong demand toextend linear model to non-linear model since non-linear relationship such as the interaction oflatent variables exists in many research fields. The difficulties of non-linear factor analysis are how
to establish the non-linear model and how to estimate parameters. Methods used by now have somedeficiencies and difficulties. In this study, the author established a generic non-linear factor analysis
model, put forward parameters estimation methods, and extended the one-sample non-linear model
to multi-sample non-linear model. At first, the author formulated a generic non-linear factor analysis
model, and then parameters were estimated by using maximization likelihood method. Owing to thenon-linear relationship of latent variables, the multiple integral of the log-likelihood function basedon the observed data does not have an explicit form. Hence, it is difficult to obtain the estimates by
maximization of the log-likelihood function directly. Data augmentation method was considered tosolve this problem. The observed data was augmented with the matrix of latent variables which wastreated as a hypothetical missing data. Then the ML estimates were obtained by EM algorithm. We
used Louis function and random sample generated in the MH algorithm to obtain standard error
estimates. BIC criterion was used on model selection in multi-group non-linear factor analysis.
Results of simulation study illustrated that the estimates were closed to their true values, and model
selection results were approximately the same as the real situation. It is implied that the proposed
method performed well. In addition, a real example is reported.

- 20 -

I:02

Determining the Effective Sample Size of a Parametric Prior
Satoshi Morita1
1

Department of Clinical Trial Design and Management, Translational Research Center, Kyoto
University Hospital, Kyoto, Japan

We present a definition for the effective sample size of a parametric prior distribution in a Bayesianmodel, and propose methods for computing the effective sample size in a variety of settings. Our
approach first constructs a prior chosen to be vague in a suitable sense, and updates this prior to
obtain a sequence of posteriors corresponding to each of a range of sample sizes. We thencompute a distance between each posterior and the parametric prior, defined in terms of thecurvature of the logarithm of each distribution, and the posterior minimizing the distance defines theeffective sample size of the prior. For cases where the distance cannot be computed analytically,
we provide a numerical approximation based on Monte Carlo simulation. We provide general
guidelines for application, illustrate the method in several standard cases where the answer seems
obvious, and then apply it to some non-standard settings.

- 21 -

I:03

Simple Statistical Tests for New Composite Hypotheses
in Randomized Clinical Trial Reflecting the Relative Clinical
Importance
Masako Nishikawa1,Toshiro Tango1,Megu Ohtaki2
1

Department of Technology Assessment and Biostatistics, National Institute of Public Health, Japan
Department of Environmetrics and Biometrics, Research Institute for Radiation Biology andMedicine, Hiroshima University, Japan

2

In confirmatory clinical trials, it is recommended that the most important endpoint (EP) should bechosen as primary. However, it is often difficult to choose only one primary EP in some therapeuticarea. Such a trial set up multiple EPs which arise multiplicity problem in general. To avoid themultiplicity problem a combined EP can be considered. O'Brien (1984) proposed an ordinary leastsquares and a generalized least squares approach for continuous variables under the assumption thatthe effects are equal across EPs. Pocock et al. (1987) extended the method to combine different
types of variables into a single EP which is asymptotically normally distributed, and they suggested
a formulation for unequal priorities to various EPs, however, there are no methods to measure therelative clinical importance quantitatively. We propose a new set of composite hypotheses taking therelative clinical importance between EPs into account for continuous variables. This formulation
leads to a new test statistic, which can measure the relative clinical importance between EPs
quantitatively and will be sensitive for such an alternative hypothesis that the effect for the more
important EP of the new treatment is greater than that of the other EP even if the new treatment issuperior to the control in both EPs. The size of the proposed test is explored by simulation. Our test
is illustrated by an example. References O`Breien. (1984) Procedures for comparing samples withmultiple endpoints. Biometrics 40, 1079 -1087.Pocock SJ., Geller NL. and Tsiatis AA. (1987) The
analysis of multiple endpoints in clinical trials. Biometrics 43, 487-498.Keywords COMPOSITE
ENDPOINT, ONE-SIDED TESTS, MULTIPLE ENDPOINTS- 22 -

I:04

Statistical Tools for Protein Identification Based on Mass
Spectrometry Data:A Review
Jae Won Lee1,JungBok Lee2,Shin-Jae Lee1,3
Department of Statistics, Korea University, Seoul, Korea 2Institute of Human Genomic Study,
College of Medicine, Korea University 3Department of Orthodontics, College of Dentistry, Seoul
National University

1

Peptide Mass Fingerprinting (PMF) through mass spectrometry is a technique used to identify
proteins by matching their constituent fragment masses (peptide masses) to the theoretical peptide
masses generated from a protein or DNA database and widely applied in proteome research. To
identify the protein from the high throughput mass spectrometric data, the database searching has
emerged as a key platform in this field and several DB searching algorithms have been developed.
Some researchers commented several factors influencing the quality of a mass spectrometry
experiment. Missed cleavages, post-translational modifications of peptides and contaminants areimportant factors affecting the results of the mass spectrometric data analysis, but these factors havean influence on the identification process rather than the quality of the MS spectra. In this talk, wewill review statistical algorithms for protein identification and evaluate the identification
performance of current DB searching algorithms by simulation study. Our simulation result will
provide the proteome researchers the insight for selecting the DB searching algorithm appropriate
for their data.

- 23 -

I:05

Bioinformatics Approach Towards Drug Target Gene Discovery
Seiya Imoto1
1

Institute of Medical Science, University of Tokyo, Tokyo, Japan

Microarray technology has produced a large volume of genome-wide gene expression profile data
under various experimental conditions such as gene disruptions, gene overexpressions, shocks,
cancer cells, etc. Along with this new data production, there have been considerable attempts to infer
gene networks from such gene expression profile data and several computational methods have been
proposed together with gene network models such as Boolean networks, differential equation
models and Bayesian networks. While the paradigm of using microarray technology with theclustering technique has made tremendous impacts on biomedical research and practice, the strategy
enhanced with computational gene network analysis has not yet been well examined for practicalapplications. In this talk, we show a computational method for identifying and validating drug target
genes from DNA microarray gene expression data. To demonstrate the whole process of the
proposed method, we analyze expression data from human endothelial cells. We generate newtime-course data that reveal the responses of human endothelial cell transcripts to treatment with theanti-hyperlipidaemia drug fenofibrate. We also generate new data from 270 gene knock-down
experiments in human endothelial cells. The fenofibrate-related gene network is estimated based on
fenofibrate time-course data and 270 gene knock-down expression data by the proposed method.
The estimated gene network reveals gene regulatory relationships related to PPARa, which is known
to be activated by fenofibrate. Our gene networks have the potential to predict the mode-of-action ofa chemical compound, discover more effective drug target and predict side-effects. Finally, theproposed computational analysis suggests that our strategy based on gene knock-down anddrug-dosed time-course microarrays will give a new way to druggable gene discovery.

- 24 -

I:06

Analyses of OMICS Data in Medical Informatics
Masaru Ushijima1
1

Genome Center, Japanese Foundation for Cancer Research, Tokyo, Japan

Bioinformatics is the science of using computers, databases, and math to organize and analyze large
amounts of biological, medical, and health information by NCI definition. Major research area in
the field of classical bioinformatics includes sequence analysis, protein structure prediction andcomparative genomics. In the 'omics' era of cancer research, high-throughput analyses of 'omics'
data such as genome, transcriptome and proteome data will have a great impact on diagnosis,
prognosis, and therapy of cancer. In this presentation, we focus on medical informatics for
personalized medicine and review recent methodologies for high-throughput analyses forgenome-wide association study using SNPs, gene expression profile analysis and mass
spectrometry data. In genome analysis, we introduce a method of association study using SNPs andhaplotypes. Haplotype block structure is identified using an ancestor-derived model and the MDL
principle. We show some examples that the appropriate haplotype block partitioning leads toincrease the power of tests in association study. In transcriptome analysis, we breifly introduce amethod of gene selection for the prediction of phenotypes using microarray datasets. Finally we
introduce our new common peak approach, using mass spectrometry datasets for predicting effects
on anticancer drugs. The original common peak method proposed by Fushiki et al. (2006) is a
simple way to select the sensible peaks that are shared with many subjects among all detected peaks
by combining a standard spectrum alignment and kernel density estimates. The key idea of our
proposed method is to apply the common peak approach to each class label separately. Hence, theproposed method gains more informative peaks for predicting class labels, while minor peaks
associated with specific subjects are deleted correctly. We used a SELDI-TOF MS data set fromlaser microdissected cancer tissues for predicting the treatment effects of neoadjuvant therapy using
an anticancer drug on breast cancer patients. The AdaBoost algorithm is adopted for patternrecognition, based on the set of candidate peaks selected by the proposed method. The analysis
gives good performance in the sense of test errors for classifying the class labels for a given feature
vector of selected peak values.

- 25 -

I:07

Tests of Hypothesis about the Kappa Statistic based on theGoodness-of-fit Approach
Ayanendranath Basu1,Sumit Gupta1,Wen-tao Huang2
1

Bayesian and Interdisciplinary Research Unit, Indian Statistical Institute, 203 B. T. Road, Kolkata
700108, India 2Tamkang University, Tamsui, Taiwan, R.O.C.

We consider the goodness of fit approach for performing tests of significance about the kappa
statistic. The approach was suggested by Donner and Eliasziw (1992; Statistics in Medicine, 11,1511-1519) who framed the parametric hypothesis testing problem about the kappa statistic (leading
to other associated small sample inference) as a multinomial goodness-of-fit testing problem, forwhich they proposed the Pearson's chi-square statistic. Basu and Basu (1995; Statistics in
Medicine,14,347-356) considered the use of some other members of the Power divergence familyof Cressie and Read (1984; J. Roy. Statist. Soc. B, 46, 440-464) for this goodness of fit testing.
However, when a specific alternative is of interest, all the standard goodness of fit tests can be quitepoor in comparison with the most powerful test within the above family; in addition the asymptotic
chi-square approximation for these statistics may be quite inadequate for small samples. Here we
consider the results of a general investigation based on exact power computations which helps us tosearch for the most powerful test for each sample size given the appropriate parameters andidentifies the randomized exact critical values. The advantages of this method are demonstrated withpower and sample size comparisons.

- 26 -

I:08

Mantel-Haenszel Estimators
for Irregular Sparse K 2 X J(J&gt2) Tables
Satoshi Hattori1,Takashi Yanagawa1
1

The Biostatistics Center, Kurume University, JapanThe Mantel-Haenszel estimator has a shortcoming that it cannot be calculated for certain sparsetables, called irregular, although it is consistent under sparse-data limiting model. In this paper, we
propose a Mantel-Haenszel-type estimation procedure for such sparse K 2 X J tables when J&gt2,
employing the idea of the projection-method by Yanagawa and Fujii (1995 JASA). The projection
method projects log-transformed Mantel-Haenszel estimators for all K 2 X 2 subtables, called naive
Mantel-Haenszel estimators, onto a linear space of log odds ratios. However, for sparse tables, all
the naive Mantel-Haenszel estimators may not be calculated. We introduce alternative naive
Mantel-Haenszel estimators using a graph representing K 2 X J tables and propose to apply the
projection-method for these alternatives. In this way, we have infinitely many reasonableprojection-method estimators. We propose a method to choose the optimal one among them bysolving a quadratic optimization problem induced by the graph, where some graph-theoretic
arguments play important roles to simplify the optimization problem. An illustration is provided
with data from a case-control study. A simulation study is conducted, which indicates that the
Mantel-Haenszel estimators are more stable than the estimators by the conditional logistic
regression for sparse K 2 X J(J&gt2) tables.

- 27 -

I:09

Scan Statistics for Disease Clustering
Kunihiko Takahashi1,Toshiro Tango1
1

Department of Technology Assessment and Biostatistics, National Institute of Public Health, JapanThe circular spatial scan statistic proposed by Kulldorff (1995, 1997) has been widely used alongwith SaTScan software in epidemiological studies for cluster detection. To detect arbitrarily shaped
clusters which cannot be detected by the circular scan statistic, some spatial scan statistics such as
those developed by Duczmal and Assuncao (2004) and Patil and Taillie (2004) have proposed. Also
Tango and Takahashi (2005) proposed a flexible spatial scan statistic along with FleXScan software.
On the other hand, early detection of disease outbreaks enables public health officials to implement
disease control and prevention measures at the earliest possible time. A time periodic geographicaldisease surveillance system based on a cylindrical space-time scan statistic proposed by Kulldorff(2001) has been used for disease surveillance. Recently, Takahashi et al have proposed a flexibly
shaped space-time scan statistic for early detection of disease outbreaks based on the flexible spatial
scan statistic.
All of these scan statistics are based on maximizing the likelihood ratio statistic. However, Tango
and Takahashi (2005) showed an example that Duczmal and Assuncao,s procedure detected quite
large and peculiar shaped clusters that had the largest likelihood ratio, which caste a doubt on thevalidity of the model selection based on maximizing the likelihood ratio. Furthermore, Tango and
Takahashi (2005) have indicated that the circular scan statistic tend to detect an unrealistically larger
cluster than expected by absorbing surrounding regions where there is no elevated risk. One ofreasons for detecting undesirable clusters is that the likelihood ratio statistic is derived only from theobserved number of cases within the window Z, and the expected number within Z under the null
hypothesis of no clustering. And the statistic ignores the variability of individual regions, risks
included in Z.
We propose several scan statistics that can take such variability into account. Some data in Japan areused to illustrate the proposed scan statistics for purely spatial and space-time clustering. Power
comparisons are also presented. These proposed scan statistics have better ability of pinpointing theassumed hot-spot cluster compared with the likelihood ratio statistic.
References:
Kulldorff, M. and Nagarwalla, N. (1995) Spatial disease clusters: detection and inference. Statistics
in Medicine, 14, 799-810.
Tango, T. and Takahashi, K. (2005) A flexibly shaped spatial scan statistic for detectingclusters.
International Journal of Health Geographics, 4, 11.

- 28 -

C:01

Probability Prediction in Multistate Survival Models
for Patients with Chronic Myeloid Leukaemia
Ya Fang1
1

Department of Preventive Medicine, College of Medicine, University of Xiamen, Xiamen, China[Objective] Multistate survival experiments include various terminal events and competing-risk
events. How to predict the probabilities with multistate survival models is of interest. The purpose ofthis study is to find an appropriate model suitable for a multistate survival experiment. [Methods]
634 patients with risk score 0 or 1 as an example were selected to illustrate the method of analysis
from 3142 CML patients who underwent bone marrow transplantation between 1989 and 1997. The
statistical methods which were used were log-rank test, Kaplan-Meier method, survival analyse andcompeting-risk method. [Results] After transplantation, there are four possible situations for apatient-- disease free, relapse but still alive, death before relapse, and death after relapse. The last
three events are considered as treatment failure. First, we deal with the two events, relapse either
death or not and death before relapse, as competing-risk causes which are the endpoints withtransplantation as the starting point. The risk of death before relapse is higher than that of the relapse,
especially in the first year after transplantation. Subsequently we analyse the effect of relapse time on
the event of death after relapse which is the endpoint with relapse as the starting point by the
Kaplan-Meier method. The result of patients with relapse time less than 12 months was much poor.
Finally, the multistate survival models are developed which are detailed and informative based on
the analysis of competing risks and Kaplan-Meier analysis. We make a further analysis on
conditional probability with the multistate survival models for patients who are disease free and still
alive at month 12 after transplantation. [Conclusions] It is possible for an individual patient topredict the four possible probabilities at any time, namely disease free, relapse but still alive, death
before relapse, and death after relapse. Also the prognoses for relapse either death or not and death
either before or after relapse may be given. Furthermore, the conditional probabilities for patients
who are disease free and still alive in a given time after transplantation can be predicted.

- 29 -

C:02

Inverse Probability Weighted Estimatorsin the Stratified Nested Case-control Sampling Methods
Sachiko Tanaka1
1

Faculty of Engineering Course, Tokyo University of Science, Tokyo, Japan

Stratified nested case-control designs are widely used in the expectation that these methods may
increase precision relative to simple nested case-control design. Although conditional logistic
regression is used for analysis of data with matching, it will be inefficient because it does not use all
subjects at risk. In this talk, we introduce the simple- and augmented- inverse probability weighted(IPW) estimator of Cox regression parameter under stratified sampling designs. We estimate
selection probability and conditional expectation of missing covariate using nonparametric methods
that incorporates information of matching factors. The performance of the proposed method isinvestigated through simulation studies. From the results of simulation studies, our proposed
methods improved showed superior efficiency to the conditional logistic model.

- 30 -

C:03

Application of Functional ANOVA Models for Hazard Regression
to the Hisayama Data
Koji Yonemoto1,Atsushi Kawaguchi2,3,Yumihiro Tanizaki1,Yutaka Kiyohara1,Takashi
Yanagawa2,Young K. Truong3
1

Department of Environmental Medicine, Graduate School of Medical Sciences, Kyushu
University, Fukuoka, Japan 2Biostatistics Center, Kurume University, Kurume, Japan 3Department
of BIostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

A methodology for modeling covariate effects on the time-to-event data is developed. The
covariates are allowed to be time-dependent and their effects are modeled using polynomial splines
in order to account for possibly non-linear effects. The methodology is applied to examine theeffects on the incidence brain infarction based on a cohort study in Hisayama, Japan. The results
indicate that at least two non-linear effects are significant (body mass index and systolic bloodpressure) and there is a time-varying drug effect. The resulting significant risk factors are assessedby the proposed method which is more flexible and hence less biased than the traditional procedures
where linear effects are imposed. These results are extremely important to the local medical
investigation. In particular, more insight has been gained by examining the non-linear effects.

- 31 -

C:04

Estimating the Power of the Likelihood Ratio Testin the Cohort-based Nested Case-control Studies
Shizue Izumi1,Yoshinori Fujii2
Department of Computer Science and Intelligent Systems, Oita University, Japan 2Faculty of
Education and Culture, University of Miyazaki, Japan

1

Cohort-based molecular epidemiologic studies have been conducted actively to ascertain the diseasemechanism with candidate biologic markers using the stored biological samples accumulated overyears. It is important to thoroughly consider what design is the best under the given study
conditions. Possible choices include a case-cohort design, a simple case-control design, a nested
case-control design, and a case only design. Some designs have pro and cons to others. A nested
case-control design is relatively efficient in estimating the effects of the target variable. Therefore,
we propose a new method to estimate the power of the likelihood ratio test in the cohort-basednested case-control studies.
In our settings of the power calculation, the cohort data are assumed to include the complete
information on the event status of the disease, the observed time, and the exposure. Frequently the
information of the confounder is missing in the cohort data so that the marginal distribution of theconfounder is obtained from other studies to randomly assign the value to the individuals of thecohort. Using such interpolated cohort data, the effects of the exposure and the confounder can be
estimated with the proportional hazard model. For the cohort-based nested case-control data, thecontrols are sampled from the risk set defined for each event time. In our method, a surrogate case isadditionally sampled from the risk set using the likelihood-based sampling weights as a replacementof the original case. Likelihood ratio test is used at the level of 0.05 to examine a null hypothesis inthat the parameter estimate is zero. The power is computed as a probability of rejecting a null
hypothesis when the alternative hypothesis is true. Our method may be fragile to the distortion of
proportional hazard assumption.
The simulation studies were conducted to examine the feasibility of our method. The event status
and the observed time in the cohort data were defined with the Weibull distribution and theexponential distribution. Five hundred nested case-control data were created to each 500 cohort
data. All computations were done with R software. As results, the mean power of our method wasclose to the power of the classic method and the R programming for the simulation studies was
fairly straightforward. As a conclusion, our proposed method is feasible and useful to estimate thepower of the likelihood ratio test in the cohort-based nested case-control studies.

- 32 -

C:05

Sample Size Calculations Based on Exact Distribution ofFalse Discovery Proportion in Microarray Experiments
Tomonori Oura1,Shigeyuki Matsui1,Koji Kawakami1
1

Department of Pharmacoepidemiology, Kyoto University School of Public Health

Microarray gene expression analysis is one of the standard tools for screening for differentiallyexpressed genes among different diagnostic or prognostic classes of disease. In order to detect
differential genes, thousands of univariate tests are usually conducted simultaneously, with controlof the false discovery rate (FDR), which is defined as the expectation of the false discovery
proportion (FDP), the number of false positive genes out of the number of genes declared positive.
To obtain a reliable list of differential genes, more attention should be paid to experimental design,
including determination of the number of samples. Many methods for sample size calculations
recently developed are based on marginal FDR (mFDR), which is the ratio of the expectation of thenumerator to that of the denominator of FDP, as an approximation of FDR. However, thisapproximation can be inaccurate for correlation among genes by virtue of co-regulation, potentiallyresulting in a serious failure in determining sample sizes.
In this paper, we propose to use an exact distribution of FDP, which allows exact control of
distributional characteristics of FDP, including the mean (i.e., FDR) and percentiles. Our methods
can accommodate block-wise gene correlation structures with various correlation coefficients andvarious effect sizes on the phenotype variable. We demonstrate that our sample size calculations areaccurate compared with previous sample size calculations (1,2), through Monte Carlo simulations andapplication to real datasets from clinical studies for multiple myeloma and lymphoma.
1. Jung SH. Sample size for FDR-control in microarray data analysis. Bioinformatics 2005; 21:3097-104
2. Shao Y, Tseng CH. Sample size calculation with dependence adjustment for FDR-control in
microarray studies. Statistics in Medicine 2007; 26: 4219-4237- 33 -

C:06

A Test Statistic Based on Shrunken Sample Variance for Identifying
Differentially Expressed Genes in Microarray Data Analysis
Akihiro Hirakawa1,Yasunori Sato2,Chikuma Hamada3,Isao Yoshimura3
Office of New Drug I, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan 2Departmentof Biostatistics, Harvard School of Public Health, USA 3Faculty of Engineering, Tokyo Universityof Science, Japan1

Choosing an appropriate test statistic for comparing the gene expression levels under different
conditions is essential for identifying differentially expressed genes. For identifying truly
differentially expressed genes, it is necessary to suppress both false positives, which are genes thatare falsely identified as differentially expressed genes, and false negatives, which are genes that are
falsely not identified as differentially expressed genes. Both false positives and false negatives
mainly arise due to the underestimation and overestimation of the variance of gene expression
levels, respectively. The Welch t-statistic leaves both the underestimation and overestimation ofvariance uncontrolled, resulting in an increased risk of both false positives and false negatives. The
t-type score proposed by Pan et al. (2003) with a correction term added to the denominator of the
Welch t-statistic can suppress false positives by controlling the underestimation of variance, but it
can not suppress the overestimation. To control the overestimation, we devised a variance stabilized
t-type score by placing shrunken sample variances of the James-Stein type in the denominator of thet-type score. The shrunken sample variances, which can utilize information across genes according
to the James-Stein shrinkage concept, can control the overestimation of variance. The variancestabilized t-type score, therefore, can suppress both false positive and false negatives. We conducteda simulation study to compare the performances of the Welch t-statistic, t-type score, and variancestabilized t-type score, demonstrating that the variance stabilized t-type score was better than or atleast as good as the t-type score. In particular, the variance stabilized t-type score outperformed thet-type score when the sample size was smaller than 5 in each group or the proportion ofdifferentially expressed genes was smaller than 5%. As a result of application using SignificanceAnalysis of Microarray (SAM) (Tusher et al., 2001) to colorectal cancer data (Provenzani et al.,2006), the variance stabilized t-type score might control the overestimation of variance.

- 34 -

C:07

Measurement Scale Categorization and its Resolution.
A Gene Reduction Approach to the Microarray Data Analysis.
Yoichi M. Ito1,Yasuo Ohashi1
1Department of Biostatistics, School of Public Health, University of Tokyo, Tokyo, Japan

In the microarray data analysis, selection of the important gene must be needed because the sample
size is usually limited in contrast to the huge number of genes. The measurement error of the
microarray data is prone to be large, therefore the subtle difference of gene expression cannot be
detected. In this study, we propose the measurement scale categorization according to both themeasurement error and the categorization error. This approach is to break continuous gene
expression measurement into some prespecified categories and by which the expression for a genecan be classified to one of a finite expression patterns. The simulation study revealed that the power
loss associated with this approach was large when the effect size is small. However this property isuseful for eliminating the genes which shows a small change. We applied this approach to a clinicaldata for primary breast cancer.

- 35 -

C:08

Survival Prediction using Top Ranking Significant Genesin Cancer Prognostic Studies with Microarrays
Takeharu Yamanaka1,Shigeyuki Matsui2
Institute for Clinical Research, National Kyushu Cancer Center, Fukuoka, Japan 2Department ofPharmacoepidemiology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

1

Microarray technology promises a sound possibility for developing patient prognostic markers atmolecular level. Primary analysis for such studies is to screen a subset of relevant, prognostic genes
from (ten of) thousands of candidates and then is followed by an investigation regarding prognostic
power of the screened subset as a supplemental analysis. However, many methods for survival
prediction actually intend to use a whole full set of genes for prediction or to use gene subsets thatare selected without regard to the primary analysis. Accordingly, no formal statistical comparison
among predictors has not been conducted under the practical limitation of significant genes being
chosen previously. Our study is thus concerned with assessing predictive accuracy of several
predictors on the condition that we focus on a selective subset of relevant genes from the primary
analysis. We evaluated several linear predictors including principal components1, partial leastsquares2, compound covariates3, and ridge regressions4. The predictive performance of thesemethods were measured firstly by using simulated data under various conditions on the total
number of top ranking genes, their correlation structures, and the degree of linkage to survival
outcome. The reliability and validity of their predictive accuracy arising from cross-validation splitof whole data into training and test sets were also evaluated. Then, we used publicly available
microarray data from cancer clinical studies and examined the applicability of the results fromsimulated data to them. Integrating these investigations has revealed that there is large predictive
heterogeneity among predictors and some of methods often fail to achieve enough performance.
We have also investigated a possible impact of the selection of top ranking genes. Primary
identification of gene subsets is a crucial step in prognostic studies although most cases simply rely
on ranking univariate Cox scores. We will report the impact of various summary statistics used forprimary screening, including log hazard ratios and empirical Bayes estimators, on predictive
performance of predictors.
References
1. Bair E and Tibshirani R (2004) PLoS Biology 2:511-522
2. Li H and Gui J (2004) Bioinformatics 20(Suppl 1):208-215
3. Matsui S (2006) BMC Bioinformatics 7:156
4. van Houwelingen HC et al (2006) Stat Med 25:3201-3216- 36 -

C:09

Applications of Minimal Spanning Tree and Self-organizing Map
in Miroarray Data Analysis.
Mira Park1,Yoo-Jin Jang2,Myung-Hoe Huh2
Department of Preventive Medicine, Eulji University, Daejon, Korea 2Department of Statistics,
Korea University

1

Self-organizimg map (SOM) is an unsupervised learning neural network method and it is useful topresent high-dimensional data to low dimensional map. However, there are several problems inapplying SOM such as determination of the size and shape, ineffectiveness in visualization, and soon. Minimal spanning tree is a tree in which each taxon is connected by a line to its most similar
neighbor. It can be used for pattern recognition of large dataset. In this study, we suggest ameasure for determining proper size of the map using minimal spanning tree. And we also propose
a method for partitioning and choosing the number of clusters. It is based on the distance between
objects connected by MST on SOM. The methods are applied to some variants of SOM, such as
subnode-SOM and PC-SOM. We also illustrate the proposed methods using two well-known
microarray data sets: the leukemia data set(Golub et al., 1999) and the lymphoma dataset(Alizadeh
et al.,2000).

- 37 -

C:10

Estimating Surrogate Endpoints
Defined by Principal Causal Effects
Shiro Tanaka1,Yutaka Matsuyama1,Yasuo Ohashi1
1Department of Biostatistics/Epidemiology and Preventive Health Sciences, School of Health
Sciences and Nursing, University of Tokyo, Tokyo, Japan

In clinical trials, comparison of treatments for the outcome of primary interest such as survival time
may require a long follow-up before yielding useful results. Because of this, there is increasing
interest in the use of surrogate endpoints to make decisions about treatment efficacy. Frangakis andRubin (2002) proposed a principal surrogate, which was defined by potential values of surrogate
endpoints. However, it is impossible to observe joint distribution of potential surrogate endpoints
without untestable assumptions. In this talk, we propose an estimation method of the principal
surrogate under monotonicity assumption, which states that there is no subject who would not havea response under test arm, but have a response under control arm. We also applied the proposed
method to a clinical trial data of advanced prostate cancer.

- 38 -

C:11

Recent Advances in Stepdown Procedures for Identifying Inferiority
among Treatments in Clinical Trials
Anthony Hayter1,Chen-ju Lin2
1

Department of Statistics and Operations Technology, University of Denver, Colorado, USA
Georgia Institute of Technology

2This talk considers the problem of identifying which treatments are not the best treatment in aone-way layout, which has many important applications in screening trials for new productdevelopment. In Hayter (2007) "A Combination Multiple Comparisons and Subset Selection
Procedure to Identify Treatments Strictly Inferior to the Best," Journal of Statistical Planning andInference, a procedure has been developed that selects a subset of the treatments containing only
treatments that are known to be strictly worse than the best treatment or treatments. In addition,
simultaneous confidence intervals are obtained which provide upper bounds on how worse eachtreatment can be compared with the best treatment. In this way the new procedure shares thecharacteristics of both subset selection procedures and multiple comparison procedures. Also, in Lin
and Hayter (2007) "A Stepdown Procedure with Feedback for Identifying Inferiority among Three
Treatments" a stepdown procedure is developed that uses feedback from the first stage to the second
stage which improves its operating characteristics. The advantages accruing from this feedback aredemonstrated.

- 39 -

C:12

Permutation Test Following Covariate-adaptive Randomizationin Randomized Controlled Trials
Takahiro Hasegawa1,Toshiro Tango2
Biostatistics Dept., SHIONOGI & CO., LTD., Osaka, Japan 2Department of Technology
Assessment and Biostatistics, National Institute of Public Health, Saitama, Japan

1

In randomized controlled trials, eligible and consenting patients are recruited and randomly
allocated to treatments. Patients are never randomly sampled from large population of patients ontreatments under study. Therefore, it is natural to apply permutation test under the randomization
model, where it is assumed that the treatment assignments is random and that the set of observed
responses is fixed under the null hypothesis. However, most of the statistical analyses are still basedon the concept of population model, where patients are assumed to be randomly sampled from
populations.
In this article, we examined the property of permutation test following Pocock-Simon's
covariate-adaptive randomization using the difference in means as a test statistic under the
randomization model in comparison with that of t test and analysis of covariance under the
population model. Furthermore, we compared the size and average power of permutation test withthat of t test and analysis of covariance via Monte Carlo simulation. When complete covariatebalance is attained, permutation test is proved to be identical to the analysis of covariance. When
there is a little covariate imbalance, the average power of permutation test is shown to be slightly
lower than that of analysis of covariance. Unless the effects of covariate is negligible, t test is shown
to be inadequate in that its size is quite lower than the nominal α level. Our results suggest thatpermutation test following Pocock-Simon's covariate-adaptive randomization can be used as a
useful alternative to traditional population-based design in a confirmatory randomized controlled
trial with important prognostic factors.

- 40 -

C:13

Comparisons between Three Treatments including a Placebo
and a Control by the Multiple Confidence Procedure
Tetsuhisa Miwa1
1

Ecosystem Informatics Division, National Institute for Agro-Environmental Sciences

Takeuchi (1973) proposed a multiple confidence procedure for multiple decision problems in his
book "Studies in Some Aspects of Theoretical Foundations of Statistical Data Analysis" (inJapanese).This procedure is based on a partition of the parameter space, and it is closely related to
the recent development of the partitioning principle for multiple comparison procedures.
In our talk we apply the multiple confidence procedure to the problem of comparing threetreatments: placebo, control and test treatments.Let µ0, µC, µT be the population means of theplacebo, control ant test treatments, respectively.Then the total parameter space is partitioned into
four disjoint regions:(1) H0(1): µC + ∆U < µT (superiority)(2) H0(2): µC − ∆L ≤ µT ≤ µC + ∆U (equivalence)(3) H0(3): µ0 < µT < µC − ∆L (effectiveness against control)(4) H0(4): µT ≤ µ0 (futility)
We construct test procedures for the null hypotheses H0(i).Then the possible decisions are obtainedby collecting all the accepted null hypotheses.

- 41 -

C:14

Statistical Inference for Outbreaks in a Population
with a Contact Network Structure.
Lilia L. Ramirez-Ramirez1,Mary E. Thompson1
1

Department of Statistics and Actuarial Sciences, University of Waterloo, Canada

For diseases that spread in a population with the direct contact of its individuals, traditional epidemic
models assume that every susceptible individual is homogeneously exposed to the agent. However
it has been observed that for some diseases, the outbreaks do not evolve uniformly throughpopulations making evident that some epidemics are heavily affected by the population connectivity
patterns in which the infective agent spreads ([1], [2] and [3]).
Recent epidemic models incorporate the heterogeneous individual exposure based on a contact
structure that is built in terms of random graphs [4]. Random graphs or networks have been widelyused to model several systems, including the World Wide Web, metabolic and protein and language,
and have been observed to model social relationships like the number of sexual partners [5],Since the basic reproductive number (*)/ replacement number (**), together with the thresholdtheorem (***) are used to plan and evaluate the control measures for outbreaks, it is important toimprove its estimation, separating it from the particular population contact network structure inwhich the data is observed. In this work we show the methodology for statistical estimation foroutbreaks in a population with a network structure, and some examples.
Special emphasis is made in the Reed-Frost model that evolves in a contact network.
Footnote(*) Average number of new infected individuals produced by a contact with one infected individual in a complete
susceptible population.
(**) Average number of new infected individuals produced by a contact with one typical infected individual(***) States the critical value of the basic reproductive number (replacement number) above which the probability that theoutbreak develops into an epidemic is greater than zero.
References[1] Bailey, N. T. J. (1975). The Mathematical Theory of Infectious Diseases. Charles Griffin & Company LTD, second
edition.
[2] Diekmann, O. and Heesterbeek, J. A. P. (2000). Mathematical Epidemilogy of Infectious Diseases: Model Building,Analysis and Interpretation. Wiley series in math. and comp. biology.
[3] Anderson, R. and May, R. M. (1991). Infectious Diseases of Humans: Dynamics and Control. Oxford Science
Publications[4] Newman, M. E. J. (2002). Spread of epidemic disease on networks. Physical Review, E, 66:016128.
[5] Liljeros, F., et al.(2001). The web of human sexual contacts. Nature, 411:907-908.

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C:15

Several Challenges by Biostatisticians for Developinga New Animal Test Method
Takashi Omori1,Takashi Sozu2,Isao Yoshimura3
Department of Biostatistics, Kyoto University School of Public Health, Japan 2The Center for
Advanced Medical Engineering and Informatics, Osaka University, Japan 3Department ofManagement Science, Faculty of Engineering, Tokyo University of Science, Japan1

To identify chemicals of skin sensitization potential is an aim in regulatory toxicology. The Local
Lymph Node Assay (LLNA) is one of the test methods for the purpose, which has been developed
and validated. Since the standard LLNA, however, requires the use of radioisotopes, the facility
which conducts the experiments of the test method is limited. Then, the development of thealternatives for the standard LLNA is expected. A modified LLNA proposed by Daicel ltd. basedon ATP content (LLNA-DA) is a candidate of alternatives for the standard LLNA (1).
For a new assay to be recognized as a test method, the validated data for intra- andinter-laboratory reproducibility and relevance is required (2). Since the LLNA-DA had no data forinter-laboratory reproducibility, two validation studies of the LLNA-DA were conducted in
2006-2007. Due to limited funding and a short time schedule for the project, there were many
challenges involved in the studies: how to collect data from each experiment laboratory, how toallocate masked chemicals for laboratory experiments, how to measure the inter-laboratory
reproducibility, how to evaluate transferability and so on. We prepared formatted data files in order
to collect data correctly and a program for random allocation that considers the degree of skinsensitization. We also developed confidence intervals and inter-laboratory variance of an index forthe judgment on skin sensitization potential, and proposed a measure for transferability.
Although these works are simple and may not be new ones for the statistical methodology, we
believe that the works by biostatisticians enable researchers in the area not only to evaluate the pureinter-laboratory reproducibility avoiding other errors but also to show successfully highperformances of the LLNA-DA.
We report these statistical challenges and results in the LLNA-DA validation studies.
Refernces
1. Yamashita, K., Idehara, K., Fukuda, N., Yamagishi, G., and Kawada, N. (2005). Development of a modified local
lymph node assay using ATP measurement as an endpoint. Alternatives to Animal Testing and Experimentation, 11,136-144.
2. OECD. (2005). Organization for Economic Co-operation and Development - OECD series on testing and assessment.
No. 34: Guidance document on the validation and international acceptance of new or updated test methods for hazard
assessment.

- 43 -

C:16

Bayesian Analysis of Repeated Data with ManyZeros: Application
to the Longitudinal Adolescent Substance Abuse Study
Hyonggin An1
1

Department of Biostatistics, Korea University, Seoul, Korea

Data with too many zeros are quite common in the longitudinal substance abuse research. Standard
statistical methods for those data such as repeated measures analysis of variance or generalized
linear mixed model may produce invalid inferences. As appropriate alternatives to standard
methods, Tooze et. al. (2002) proposed a two-part model with random effect for continuous
outcomes while Hall (2000) proposed a zero-inflated Poisson regression with random effects forcounting outcomes. The parameters in these models were estimated by maximum likelihood. In thispaper, as alternatives to maximum likelihood approaches, we will propose Bayesian estimation
methods for both the two-part model and the zero-inflated Poisson regression with random effects.
The Bayesian methods are more flexible and can be easily implemented in the existing software, R
and WinBUGS. We will also present the results from simulation study and real data analysis
comparing the standard methods and the proposed methods in the longitudinal adolescent substanceabuse study.

- 44 -

C:17

Consistency of Signal Measureswith Epidemiologic Effect Measures
Tetsushi Komori1,2,Takashi Ohmori 2,Tosiya Sato2
Bayer Yakuhin, Ltd. 2Department of Biostatistics, Kyoto University School of Public Health1

Purpose. As tools for the signal detection on large spontaneous reporting databases, several signal
measures, such as the proportional reporting ratio or the reporting odds ratio, are utilized. Whilethese signal measures have similar forms to the epidemiologic effect measures, it is anticipated thatthe actual risk to subjects cannot be known through these measures because of the nature of
spontaneous reports. The objective of this study is to investigate the conditions for the consistencyof signal measures with epidemiologic effect measures like the incidence rate ratio. Practicalmethods to examine such conditions with real data are also proposed.
Methods. Each signal measure is expressed as a function of the time dependent factors: the size ofa cohort; the proportion of subjects exposed to drug; the hazard of adverse event; the reporting
proportion of a drug-event combination. Under the proportional hazard assumption, the conditions
for the consistency of signal measures with the incidence rate ratio are examined. As a numericalexample, anaphylaxis related events among quinolones are investigated.
Results. We derived four conditions for the consistency of signal measures. Two conditions arecommon for all measures: the incidence of the other events is equal between the drug of interest andthe other drugs; the odds ratio of reporting proportion equals one. The example suggests theconditions may not hold in real data.
Conclusions. Each signal measure has its own conditions for consistency with the incidence rate
ratio. These conditions could be utilized to infer the actual risks through signal measures.

- 45 -

C:18

An Independent Statistical Center in Support of the Data and
Safety Monitoring Board: The Adenoma Prevention with Celecoxib(APC) Trial
KyungMann Kim1
1

Department of Biostatistics & Medical Informatics, University of Wisconsin-Madison, USA

The role of an independent statistical analysis center in support of data and safety monitoring boardsin the industry-modified National Institutes of Health model has been previously described ingeneral terms . The importance of the data and safety monitoring board in continuing review ofongoing clinical trials to ensure the safety of participants and the validity and integrity of the data has
now been widely recognized both by the regulatory agencies and industry sponsors. In order toensure confidentiality and integrity of interim data review, an independent statistical center is often
established by the sponsor in support of the data and safety monitoring boards. Celebrex(celecoxib) and Vioxx (rofecoxib) are both a nonsteroidal anti-inflammatory drug specificallyinhibiting cyclooxygenase-2 and were approved by the US Food and Drug Administration,
respectively, in December 1998 for treatment of arthritis and in May 1999 for treatment of arthritis
and acute pain in adults. The National Cancer Institute launched the Adenoma Prevention with
Celecoxib (APC) trial in November 1999, and shortly afterwards Merck launched the Adenomatous
Polyp PRevention On Vioxx (APPROVe) trial in February 2000. A regularly scheduled reviewof the interim safety data from the APPROVe trial by its external safety monitoring board led to a
voluntary withdrawal of Vioxx on 30 September 2004 , and this was followed by early terminationof the APC trial on 17 December 2004 . In this presentation, the APC trial will be used as an
example to illustrate the necessity and the importance of the independence of the statistical center insupport of the data and safety monitoring board.

- 46 -

C:19

An Improved Estimating Method of QT Interval for AdjustingHeart Rate: a Multivariate Approach
JungBok Lee1,Byoung Cheol Jung2,Hong Euy Lim3,Chol Shin1,3
Institute of Human Genomic Study, Collecge of Medicine, Korea University, Korea 2Departmentof Statistics, University of Seoul 3Department of Internal Medicine, College of Medicine, Korea
University

1

Prolongation of the QT interval on a electrocardiogram(ECG) is a key marker for potential cardiac
disease. There are several empirical proposals in the literature for adjusting QT intervals for heartrate(for example, Bazett, Fridericia and Sagie correction method, etc). From a statistical perspective,these approaches have been criticized as they tend to overcorrect the QT interval.
On ECG tracing QT interval is defined as the amount of time between the initiation of the QRS
complex and the conclusion of the T wave(QT interval=QRS complex + ST interval). The QTinterval is highly correlated with heart rate, the conventional correction formulas adjusted on thewhole QT interval, which made overcorrection. The QRS complex of the ECG corresponds to theinitiation of the contraction. In this period, muscle was relaxed and repolarization of cellular
membranes followed. However, we observed the QRS is less associated with heart rate than ST
interval.
Motivated by the difference in the degree of the association with heart rate, we proposed
multivariate modeling of ECG waveform and derived improved QT correction formula. We used
population based cohort study data in Korea, ongoing 10 year follow up study for a model
construction and simulation studies were performed to compare to conventional correction methods.

- 47 -

C:20

A Sensitivity Analysis allowing for All Possible Selection Processesof Studies in Meta Analysis
Masayuki Henmi1,John B. Copas2,Shinto Eguchi1
The Institute of Statistical Mathematics, Tokyo, Japan 2Department of Statistics, University of
Warwick, Coventry, UK

1Meta analysis is now a popular method to obtain more strong evidence in medical research.
However, one crucial problem in meta analysis is publication bias, that is, the process of study
selection is not usually random, so the overall result may be biased if we neglect it. A popular way todeal with publication bias is to introduce a selection function to the analysis, which describes theprobability that each study is selected for review. However, the choice of the selection function isoften problematic because it requires strong assumptions which are not verifiable from the available
data. In this paper, we consider confidenceintervals and P-values allowing for all possible selection
functionswhich satisfy the weak assumption that large studies are as likely, or more likely, to be
selected than small studies. The confidence interval and P-value are determined for each given
number of unpublished (or missing) studies, so each of these leads to a (worst-case) sensitivity
analysis by controlling for it. Using our method, we re-analyze the data used in the meta analysis ofHackshaw et al. (BMJ, 1997) on the lung cancer risk of passive smoking. The possibility of
publication bias in this example has been a matter of some dispute in the literature: our analysis
shows that although study selection would imply that the relative risk has been exaggerated, it isunlikely to be sufficientto negate the main conclusion in Hackshaw et al. (1997) that
passivesmoking does pose a health risk, albeit at a more modest level thanhas been claimed. This
talk is based on our recent paper Henmi et al. (2007).
References:
Hackshaw, A. K., Law, M. R. and Wald, N. J. (1997).
The accumulated evidence on lung cancer and environmental tobacco smoke.British Medical
Journal 315, 980-988.
Henmi, M., Copas, J. B. and Shinto, E. (2007).
Confidence intervals and P-values for meta analysis with publication bias.Biometrics 63, 475-482.

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C:21

Sample Size Allocation to Regions in a Multiregional Trial
Hiroyuki Uesaka1
1

Lilly Research Laboratories Japan, Eli Lilly JapanThe objective of a multi-regional bridging trial is to show the efficacy of a drug in various global
regions, and at the same time to evaluate the possibility of applying the overall trial results to eachregion. However, to apply overall results to a specific region, the result in that region should beconsistent with the overall results or the results of other regions. This article discusses methods ofsample size allocation to a sepcific region and regions overall by introducing statistical criteria forconsistency between regional and overall results. Specifically, three rules of sample size allocation
are discussed: (1) allocating equal size to all regions, (2) minimizing total sample size, and (3)minimizing the sample size of a specific region. Some total and regional sample sizes calculated
under each allocation rule are illustrated.

- 49 -

P:01

Bayesian Predictive Multi-stage Design for Phase II Single-arm
Clinical Trials:A Case Study
Satoshi Teramukai1,Satoshi Morita1
1

Translational Research Center, Kyoto University Hospital, Kyoto, Japan

Phase II clinical trials are an important step through the development of new therapies. These trials
are typically small to moderate size exploratory studies to assess whether a new drug or procedure issufficiently promising to be evaluated in a randomized phase III trial. Ethical concerns that a trial
must be stopped early if the experimental treatment seems to ineffective and/or unsafe have led tothe development of sequential or multi-stage designs for phase II trials.
A single-arm clinical trial for evaluating the donor-specific transfusion (DST) on living donor livertransplantation is ongoing in Kyoto University Hospital. The primary endpoint is acute rejection onrecipients within 6 months after transplantation and the "response" is defined as no acute rejection.
We use a beta distribution with hyper parameters (a=1 and b=1) as a prior distribution. The planned
maximum sample size is 35, which was determined by the feasible number of patients. The
minimum sample size is 10, which was determined based on a minimum level of information toevaluate safety. A maximal response probability threshold is 0.75, based on the investigators'
opinions. A minimal response probability threshold is 0.55, which was estimated based on ahistorical dataset deriving from 155 recipients. In stopping rule for efficacy, the cumulative response
probability is based on the maximal response number threshold being 26; If this cumulative
probability is higher than 0.80, we will recommend stopping the trial for efficacy. In Stopping rule
for inefficacy, the cumulative response probability is based on the minimal response number
threshold being 19; If this cumulative probability is higher than 0.80, we will recommend stopping
the trial for inefficacy. Six interim analyses are planned. To assess operating characteristics of thedesign, we did some simulations. The simulation results show that the Bayesian design could
provide acceptable type I error rate and moderate power from the frequentist point of view.
In recent years, Bayesian approaches have been applied to complex statistical modeling inbiomedical field and several approaches to study designs and monitoring have been proposed
mainly in cancer clinical trials. Recently, the CDRH of U.S. FDA released draft guidance forindustry named "Guidance for the Use of Bayesian Statistics in Medical Device Clinical Trials".
This guidance shows how Bayesian approaches take more places in clinical trials and it should havea major impact on future clinical development strategies for medical products including devices anddrugs.

- 50 -

P:02

Hypothesis Test under the Additive Hazards Model
Hyo-Il Park1,Soo-Duck Lim2,Joong-Jae Cho2
Department of Statistics, Chong-ju University, Chong-ju, Korea 2Chungbuk National University

1

In this research, we derive test statistics using the nonparametric likelihood principle under the
additive hazards model for the right censored data. We will note that the proposed test statistics are
the weighted or generalized log-rank statistics. The derivation of the corresponding variance will bebased on the permutation principle. Then we illustrate our procedure with an example.

- 51 -

P:03

Study on the Criterion for Selecting the Working Correlation
Structure in Generalized Estimating Equation
Masahiko Gosho1,Chikuma Hamada2,Isao Yoshimura2
Clinical Data Science Dept., Kowa Co. Ltd., Tokyo, Japan 2Tokyo University of Science, Tokyo,
Japan

1

We often handle data that includes repeated observations of one response variable that are correlated
to each other for the same individual. Liang and Zeger (1986) proposed the generalized estimating
equation (GEE) method to analyze such data, which can be used without strict assumptions on the
joint distribution of repeated observations.
The primary role of the GEE method is to estimate parameters in the distribution, and it is necessary
to specify the working correlation structure. When an inadequate structure is chosen, the variance of
estimators increases, as compared to the case when the ideal structure is selected, i.e., the relative
efficiency descends as pointed out by Sutradhar and Das (2000). Consequently, providing an
objective criterion for selecting an appropriate working correlation structure is important and useful.
In this paper, we propose one particular criterion for selecting a proper working correlation
structure, based on a test statistic for testing the hypothesis that the covariance matrix is a given
matrix.
As a result of our evaluation of the proposed criterion by a simulation experiment in the scenario
of a two-group comparison, we confirmed that the criterion could enable the selection of the mostappropriate correlation structure with a substantially high probability. In fact, the achieved
proportion of selecting the true correlation structure fell within the range of 30.8% and 99.1%,
depending on the sample size, number of time points, and true correlation coefficients. Interestingly,
even when the true correlation structure was not selected with the proposed criterion, the descent of
the relative efficiency, which is defined by the ratio between the variance of estimators given by thetrue correlation structure and that by the proposed criterion, was at most as small as 2%. Therefore,
the proposed criterion appeared to be satisfactory for the analysis of actual data. We also examined
the practicability of the proposed criterion by applying it to clinical trial data and confirmed that ityields a reasonable result.
References
Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models.
Biometrika 73, 13-22.
Sutradhar, B. C. and Das, K. (2000). On the accuracy of efficiency of estimating equation approach.
Biometrics 56, 622-625.

- 52 -

P:04

Statistical Method for Evaluating the Efficacy of a Drugfor Osteoporosis on the Occurrence of Bone Fractures
as the Primary Endpoint
Yumiko Uematsu1,Chikuma Hamada1,Isao Yoshimura1
1

Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan

Consider clinical trials for evaluating the efficacy of a test drug for osteoporosis compared to acontrol drug with the occurrence of bone fractures (events) as the primary endpoint. The events are
generally identified by periodical checks conducted almost once a year; consequently, the time toevent is interval-censored. The duration of trial is usually required to be 2 or 3 years to obtainadequate events. The sample size is usually 1,000 or more, considering dropouts due to such a long
duration trial.According to our experiences of clinical trials, the results by two analysis methods, i.e.,
Poisson regression test (for the number of events) and log-rank test (for the time to the firstoccurrence of events) provided considerably different results, which motivated us to examine which
was more appropriate for our data through Monte-Carlo simulation experiments.The probabilistic
models used in the simulation experiments were set by referring to the two clinical trials. The
number of bone fractures showed overdispersion beyond the Poisson rule such that negative
binomial (Poisson-gamma) distribution was assumed for generating simulation data. Assuming theinterindividual Poisson parameters change with a gamma distribution, we set the parameter valuesof gamma distribution. For the comparison, we used type I error rates and powers as the indices,where the Poisson regression test was adjusted by the estimated magnitude of dispersion. The
number of repetitions in the simulation was 10,000 and 1,000 for type I errors and powers,
respectively.Concerning type I error rates for a nominal 5% significance level, slightly liberal values
such as 5.2% or 5.4% were observed in the Poisson regression test with adjustment of
overdispersion, whereas they were below 5% in the log-rank test. Concerning powers, the former
achieved a considerably higher value than the latter, the difference being the order of 10%.Since theincrease of power of the Poisson regression test was sufficiently greater than the negligible
increase of type I error rates, we recommend the Poisson regression test with adjustment of
overdispersion rather than the log-rank test, to compare the drug efficacy on the occurrence of bone
fractures between treatment groups. References:Metcalfe, C. and Thompson, S. G. (2006). The
importance of varying the event generation process in simulation studies of statistical methods forrecurrent events. Statistics in Medicine, 25,165-179.Keywords: osteoporosis, bone fracture, Poisson
regression, statistical method, adjustment of overdispersion, log-rank test, negative binomial
distribution- 53 -

P:05

Study on the Effect of Random Effects included in the Analysis
Model for In Vitro Pharmacological Data
Adaptable to Four Parameter Logistic Model
Masayuki Yamada1,2,Chikuma Hamada1,Isao Yoshimura1
1

Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan 2Planning &Management Dept., Kiseei Pharmaceutical Co., LTD., Tokyo, Japan

Our interest lies in the estimation of parameters in a 4 parameter logistic model assumed on the dataobtained in in vitro pharmacological experiments with the objective to identify theconcentration-response relationship. The functional form is essentially assumed to be f(d) =-β3+(β4-β3)/(1+(β4 exp10 (β2 (β1-d))/ β3), where d is the concentration and β1 and β4 are the 50%
inhibition dose (IC50) and the maximum response, respectively. We considered the cases where a
response is observed on individuals at all of the certain number of doses and the parameters arevariable depending on individuals as the true state of nature.Two issues were addressed in this study;
the one is to identify an appropriate estimation method and the other is to determine the model for
analysis which might not be the true model. We took a standard two-stage method (STS), a firstorder approximation method (FOA), a Laplacian approximation method (LAP), a Monte Carlointegration method (MCI), and an adaptive Gaussian quadrature method (GAU) as the methods forparameter estimation to be compared. Eight cases to assume random effects on parameters wereconsidered as the model for analysis.Estimation methods and models for analysis were comparedusing Monte-Carlo simulation experiments. The performance was evaluated through the convergent
rate, bias, and discrepancies of 2.5 and 97.5 percentiles from the true value.The result of simulation
showed that STS and FOA achieved a high proportion of convergence in the sequential
approximation for estimation. LAP, MCI and GAU achieved a considerable proportion of
convergence only when few random effects were assumed in the model for analysis. In a model
which assumed random effects on the parameters for β1 and β4, the performance of interested
parameter β1 was similar without FOA. The convergent rates in parameter estimation of this model
were 95% or more at any methods. For this model, the performance of other parameters was able toimprove more than STS. Moreover, LAP, MCI, and GAU were able to be estimated the same
performance in this model. The result of simulation experiments suggested that LAP whichassumed random effects on the parameters for β1 and β4 should be used in the analysis irrespectiveof the existence of true random effects. Reference1. Finney, D.J. (1983). Clinical Chemistry 29,
1562-1566.2. Steimer, J.L., Mallet, A., Golmard, J.L., and Boisvieux, J. F. (1984). Drug Metabolism
Reviews 15,265-292.
- 54 -

P:06

Statistical Analysis of Spatial-time Heterogeneity of Cancer
Mortality Risk based on Growth Curve Model
Tetsuji Tonda1,Kenichi Satoh1,Hiromi Kawasaki2,Takeshi Shimamoto1,Teruyuki Nakayama3,Kota
Katanoda4,Tomotaka Sobue4,Yasuto Sato5,Naohito Yamaguchi5,Megu Ohtaki1
1

Department of Environmetrics and Biometrics, Research Institute for Radiation Biology andMedicine, Hiroshima University 2Graduate School of Health Science, Hiroshima University 3Oita
University of Nursing and Health Sciences 4Research Center for Cancer Prevention and Screening,
National Cancer Center 5Graduate School of Medicine, Tokyo Women's Medical University

It is considered that disease risk, which is usually quantified by incidence or mortality, may havesome regional chronological change patterns due to change of environmental factors such as
exposure-level and custom of residents. For cross-sectional studies, the Poisson regression model
was commonly used. The random effect models were also proposed in consideration of regional
heterogeneity. The purpose of our study is to develop a reasonable statistical method for analyzing
regional heterogeneity on the time trend of cancer mortality risk and searching factors which
contribute to such heterogeneity. In this paper we propose a statistical model for describing theregression relationship between the responses with continuous type of error obeyed normal
distribution for basics and the explanatory variables by combining the Poisson model andVonesh-Carter-Ohtaki type growth curve model ([4],[5]). The proposed model was applied toprefecture-specific mortality data of large bowel cancer during the period from 1975 to 2002 in
Japan, where the response and the main explanatory variables were logarithmic transformed
standardized mortality ratio and calendar-time, respectively. Some geographical, demographical,
sociological and environmental factors were selected as covariates. From the result of analysis, themale's mortality risk was significantly increasing, stopped increasing at 1993 and has been flat from1993 onward. On the other hand, the female's mortality risk was increasing and begun to decreasing
at 1991, and has been decreasing significantly from 1991 onward. Further, metropolitan or areas
having low sunshine exposure were high mortality risk for both sexes. In particular, it seems that the
gap between the metropolitan and rural area are decreasing across the years. The effect of sunshine
exposure on mortality risk has been thought that the sunshine exposure produces a vitamin D in theskin and vitamin D reduces the risk of cancer ([1],[2],[3]).
References[1] Boscoe, F. P. and Schymura, M. J. (2006). Solar ultraviolet-B exposure and cancer incidence and mortality in the UnitedStates, 1993-2002. BMC Cancer, Art.No.264.
[2] Garland, C. F. and Garland, F. C. (2006). Do sunlight and vitamin D reduce the likelihood of colon cancer?. Int. J.
Epidemiol., 217-220.
[3] Mizoue, T. (2004). Ecological study of solar radiation and cancer mortality in Japan. Health Physics, 532-538.
[4] Ohtaki, M. (1994). Growth curve models with linear structure for location and variance parameters. Japanese Journal of
Biometrics, 59-66.
[5] Vonesh, E. F. and Carter, R. L. (1987). Efficient inference for random-coefficient growth curve models with unbalanced
data. Biometrics, 617-628.

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P:07

Modified Cp in Multivariate Ridge Regression
Kenichi Satoh1,Hirokazu Yanagihara2
1

Research Institute for Radiation Bialogy and Medicine, Hiroshima University, Hiroshima, Japan
Graduate School of Science, Hiroshima University, Hiroshima, Japan2

The method of ridge regression, proposed by Hoerl and Kennard (1970), attempts to overcome theproblems of multicollinearity between explanatory variables. The basic idea is shrinkage of theparameter space by using a ridge parameter and it is a continuous dimension reduction of
explanatory variables. In the sense, variable selection problem on ridge regression is more flexible
than discrete variable selection on existing linear regression. The best ridge parameter might beselected or optimized by minimizing Cross-Validation type criterion based on mean square error,
which is an estimator of underlying risk function. However those CV type estimators, for example
GCV (Li, 1985), have constant bias when the number of sample size is small. Therefore a better
variable selection criterion on rige regression is desired.
Cp-statistics (Mallows, 1973) is one of the most popular variable selection criteria on linear
regression. Fujikoshi and Satoh (1997) modified it and proposed MCp as an unbiased estimator on
multivariate linear regression under normally distributed error. Davies, Neath and Cavanaugh(2006) proved that MCp was a minimum variance unbiased estimator. In this paper MCp is
extended to multivariate ridge regression and a new variable selection criterion is proposed. Its
properties are investigated analytically and numerically.

References[1] Hoerl, A. E. and Kennard, R. W. (1970) Ridge regression: biased estimation for nonorthogonal
problems, Thechnometrics, 12, 55-68.
[2] Li, K-C (1985) From stein's unbiased risk estimates to the method of generalized crossvalidation, The Annals of Statistics, 13, 1352-1377.
[3] Mallows, C. L. (1973) Some comments on Cp, Technometrics, 15, 661-675.
[4] Fujikoshi, Y. and Satoh, K. (1997) Modified AIC and Cp in Multivariate Linear Regression,
Biometrika, 84, 707-716.
[5] Davies, S. L., Neath, A. A. and Cavanaugh, J. E. (2006) Estimation optimality of corrected AIC
and modified Cp in linear regression, International Statistical Review, 74, 161-168.

- 56 -

P:08

Multinational Clinical Trials in East Asia
Keiichi Sugino1,Yasuo Ohashi2,Qian Gong2,Chen Yao3
1

Development Project Management & Planning Department, Mitsubishi Tanabe Pharma
Corporation, Osaka, Japan 2University of Tokyo, Japan 3Peking University First Hospital, China

Background With the merge and acquisition of pharmaceutical industries, many pharmaceutical
giants have been extending their cooperation worldwide. The internationalization and globalizationof new development becomes inevitable, and multinational clinical trials (MNCTs) in east Asia are
increasing. There are, however, still few reports on the actual status. The purpose of this paper is toanalyze the status of MNCTs in east Asia by using a clinical trial registry database. Methods"ClinicalTrials.gov" provides regularly updated information about federally and privately supported
clinical research in human volunteers. It is the biggest clinical trial registry database all over theworld. The worldwide information on clinical trials was collected by using "ClinicalTrials.gov", andall data about east Asia were picked up from it. Results The numbers of clinical trials, which arerecruiting patients now, were 321 in Japan, 260 in China, 99 in Hong Kong, 526 in Taiwan, and 283
in Korea, respectively. Among them, the numbers of clinical trials supported by industry were 196
in Japan, 137 in China, 78 in Hong Kong, 166 in Taiwan, and 205 in Korea. In China, the partnersof MNCTs supported by industry were from the US in 49 trials, from Hong Kong in 45, from
Taiwan in 42, from Korea in 42, and from Japan in 4. In Japan, the partners of MNCTs supported byindustry were from the US (12), China (4), Hong Kong (5), Taiwan (8), Korea (10), and so on. For
Korea and Taiwan, most of the trials (81) were conducted in collaboration with each other, besides
in collaboration with the US (Korea 92 and Taiwan 77). As mentioned above, the numbers of
MNCTs were the least in Japan Compared with China, Korea and Taiwan. Conclusion MNCTs
have spread throughout most countries in east Asia, where the patient population is large and thetrial cost is relatively low. East Asia is a market worthy to be developed. Japan has being left behindin the flow until now. But it is expected that MNCTs conducted by Japanese company will increase
rapidly, because the Japanese guidance of MNCT was published in September of 2007 andJapanese government is now encouraging MNCTs.

- 57 -

P:09

Two Dimensional Summarization for Affymetrix GeneChip Probe
Level Data based on Functional States of Gene-ON/OFF
Keiko Otani1,Megu Ohtaki1,Keiko Hiyama1,Kenichi Satoh1,Eiso Hiyama1
1

Reseach Institute for Radiation Biology and Medicine, Hiroshima University,Hiroshima

Motivation
The Affymetrix GeneChip (PM, MM) probe pair is designed with the intention of measuring
non-specific binding. The removal of non-specific binding or cross-hybridization signal from a PM
measurement is a key issue in the analysis of microarray data. Many researchers have pointed outthat direct subtraction of MM from PM is unlikely to be useful, because MM contains mostly
target-specific signal as well as PM. As for methods for summarizing probe intensities in a single
index, numerous model-based algorithms have been proposed, however the most algorithms are toocomplicated with many parameters having poor biological implication. Biologists who want toanalyze GeneChip microarray data might be bewildered with the availability of so many procedureswith varying results.
PurposeTo provide a model-based algorithm for summarizing prove level data into indexes: ( summarized
intensity, probability ON)
Material
Human Genome U95 data set was used for analysis. It consists of a series of genes spkle-in at
known concentration.
Statistical analyses(1) Summarization of intensity
A principal component analysis was applied to the data set to explore the most important aspect ofdata. A gene expression intensities adjusted by probe affinity, was mostly explained by the firstprincipal component. The contribution of PM variables to the first component was almost the sameeach other. The same went for MM variables.
(2) Probability ON/FF
Assuming two separate functional states of a gene: ON/OFF, we gave a mathematical basis for thepair of measurements (PM, MM) based on the hypothesis that PM and MM have the same
distribution when a gene is in the OFF state. Let X be the number of pairs in a probe set satisfying
MM&gtPM. The probability of a gene being ON under the condition X=x was estimated.
Conclusion
A feature of gene expression was summarized into a pair of indexes: one was a summarized
intensity of probe set using a principal component analysis and the other was the probability a gene
being ON using the number of times MM&gtPM per probe set. The validity of proposedsummarization method was examined using Human Genome U95 data sets. The probability a gene
being ON/OFF is useful to discriminate specific bindings from non specific bindings.

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P:10

Effects of Lifestyle Modification on Metabolic Syndrome:
Resolution Rate and Combining Multiple Endpoints
in Meta-Analysis
Kazue Yamaoka1,Toshiro Tango1
1

Department of Technology Assessment and Biostatistics, National Institute of Public Health,
Saitama, JapanThe metabolic syndrome, also called insulin resistance syndrome or syndrome X, is a high-riskstate for diabetes and cardiovascular disease. It is a combination of medical disorders related to theconditions includes type 2 diabetes, obesity, high blood pressure, and a poor lipid profile withelevated LDL cholesterol, low HDL cholesterol, and elevated triglycerides. It has been identified asa target for lifestyle modification to reduce risk of cardiovascular disease; however, the effects oflifestyle modification are still uncertain. In this paper we consider a meta-analysis on the effect oflifestyle modification on metabolic syndrome and its component. When several clinical trials reportmultiple outcomes, meta-analyses ordinarily analyze each outcome separately. We analyze theseveral outcomes jointly with several models; compare the results with the multivariate
meta-analysis as well as the standard univariate approaches examined by fixed-effects model,
random-effects model and Bayesian model with non-informative priors using MCMC.
This meta-analysis provided evidence of the efficacy of lifestyle education for individuals of
metabolic syndrome patients in reducing RD of the resolution rate. The risk of metabolic syndromein the lifestyle education intervention group was reduced approximately by 30% [RD= 0.29,
95%CI: 0.16 to 0.42]) compared to the control intervention group by the random-effects model. As
for each component, 5 components excluding TG were significantly reduced in the lifestyleeducation groups compared to their control groups by the simple meta-analysis, however, only bradpressure index (SBP and DBP) denoted significant effects by the multiple meta-analysis.
Lifestyle education was effective for reducing both prevalence of metabolic syndrome as well
as related abnormality for metabolic syndrome in metabolic syndrome subjects and may be a useful
tool in reducing metabolic syndrome.

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P:11

A New Feature Extraction Method from very Non-normal Data:
Analysis of Multivariate Catch and Bycatch Data
by Purse-seine Tuna Fisheries
Mihoko Minami1
1

The Institute of Statistical Mathematics

We propose a new feature extraction method for very non-normal data that generalizes principal
component analysis in the same manner as generalized linear model. As a specific model, we
introduce generalized PCA with a Tweedie distribution and analyze multivariate catch and bycatch
data in purse-seine tuna fisheries.

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P:12

Statistical Pharmacokinetic Modeling of Controlled-Release
Analgesics Combining Immediate-Release and Sustained-Release
Components to Design an Appropriate Drug Delivery
Yukikazu Hayashi1,2,Chikuma Hamada1,Isao Yoshimura1
1

Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan 2ClinicalDevelopment Division, ACRONET Corp., Tokyo, Japan

In pharmacokinetics, which investigates the processes of drug on in vivo absorption, distribution,
metabolism and elimination, the method for estimating pharmacokinetic parameters to the basic
compartment models has been established. However, as for formulations with multiple absorption
processes, modeling and estimation methods for pharmacokinetic parameters have not still been
sufficiently examined.
Actually WinNonlin, the standard software widely used forpharmacokinetic analysis does not cover such a method. On the other hand, clinically there ismuch demand for sustained-release analgesics, which control the release rate of drug, combining
two-strata; immediate and sustained release components. Sustained-release analgesics that is, they
can raise blood drug concentration rapidly and can retain over a specific level of blood concentration
for a long time are strongly required to patients. Thus, it is necessary to investigate an appropriate
pharmacokinetic model and an estimating method for its parameters in controlled-releaseformulation. Also, regarding the ratio of combination for the immediate and sustained release
components, it has been required to investigate the clinically most appropriate ratio based on thestatistical model in the drug development. In this study, the pharmacokinetic compartment model
for controlled-release type analgesics was applied, followed by calculating estimated values ofblood concentration from the model formula, and the fitness of the model to measured values wasinvestigated. Based on the compartment model, the pharmacokinetic parameters were estimated.
Due to a concern about interindividual difference in the compartment model, some random effects
was added using a non-linear mixed effect model. Also, it was investigated whether or not thebasis for deciding the combination ratio of the immediate and sustained release components had
been practical to the actual data. In order to do that, firstly, time changes of blood concentrations of
the immediate release component only and the sustained release component only, which were thesame dosage, were derived by a simulation based on the calculated pharmacokinetic parameters.
Then, the basis for deciding the combination ratio of the immediate and sustained release
components was explored. Secondly, it was validated whether the basis had been put into practicein the actual data using the compartment model in controlled-release type formulation. Moreover,it was investigated whether the more appropriate ratio could have been considered by changing theimmediate and sustained release components-combining ratio. These details will be presented in
the conference.

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List of Sponsors

Acknowledgements
The organizers are grateful to the following institution and companies who
support the conference:
Acronet Co.,Ltd.
AJINOMOTO CO., INC.
Alcon Japan Ltd.
Arm Co.,Ltd.
Bayer Yakuhin, Ltd.
Chugai Clinical Research Center Co., Ltd
DAIICHI SANKYO COMPANY, LIMITED
Dainippon Sumitomo Pharma Co., Ltd.
Eisai Co., Ltd.
Eli Lilly Japan K.K.
EPS Co., Ltd
Janssen Pharmaceutical K.K.
Japan Tobacco inc.
Kowa Company, Ltd.
Mitsubishi Tanabe Pharma Corporation
Nippon Organon K.K.
NIPPON SHINYAKU CO., LTD.
Novartis Pharma K.K.
ONO PHARMACEUTICAL CO., LTD.
OTSUKA PHARMACEUTICAL CO., LTD.
Pfizer Japan Inc.
POLA PHARMA INC.
sanofi-aventis K.K.
Santen Pharmaceutical Co., Ltd.
SANWA KAGAKU KENKYUSHO CO.,LTD.
SEIKAGAKU CORPORATIONSHIONOGI & CO., LTD.
Statcom Co.,Ltd.
TAIHO PHARMACEUTICAL CO., LTD.
The Chemo-Sero-Therapeutic Research Institute
Wyeth K.K.

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