bluuebunny/crossref_metadata_2025 huggingface dataset:
Employing atmospheric pressure photoionization in liquid chromatography/tandem mass spectrometry to minimize ion suppression and matrix effects for the quantification of venlafaxine and O‐desmethylvenlafaxine
Authors: Theron Hendrik B. , van der Merwe Marthinus J. , Swart Kenneth J. , van der Westhuizen Jan H.
Published: April 2007

During the development of a method for quantitative determination of venlafaxine and its major metabolite O‐desmethylvenlafaxine, elevated concentrations of the analyte as well as co‐eluting matrix compounds caused ion suppression. This ion suppression was inconsistent and therefore influenced the reproducibility of detection. The use of atmospheric pressure photoionization (APPI) in the positive mode was investigated as a tool to circumvent this problem. Employing APPI resulted in negligible ion suppression and increased linearity of the concentration range. A selective, sensitive and rapid liquid chromatography/tandem mass spectrometry method for the determination of venlafaxine and its major metabolite O‐desmethylvenlafaxine in human plasma was developed. The analyte was extracted from plasma into tert‐butyl methyl ether followed by back extraction into 2% formic acid. An Agilent 1100 high‐performance liquid chromatography (HPLC) system, employing reversed‐phase chromatography on a cyano column, coupled to an Applied Biosystems API 3000 triple quadrupole mass spectrometer set to multiple reaction monitoring (MRM) mode, was used for separation and detection of the analytes. The method was validated between 2.36–605 ng per mL with a mean recovery of approximately 88% for both parent compound and metabolite analytes. APPI technology was employed to improve the reproducibility of detection enabling rapid, selective and sensitive quantification of venlafaxine and O‐desmethylvenlafaxine in human plasma samples. Copyright © 2007 John Wiley & Sons, Ltd.
Application of polarity switching in the identification of the metabolites of RO9237
Authors: Fitch William L. , He Limin , Tu Ya‐Ping , Alexandrova Ludmila
Published: April 2007

Polarity switching mass spectrometry is an efficient way to collect structural data on drug metabolites. The value of this approach is illustrated with the in vitro metabolism of RO9237. Metabolites are identified by positive and negative electrospray ionization (ESI) full scan mass spectrometry, MS/MS and MS3 using unlabelled and 14C‐radiolabelled versions of the drug. Comparison of the relative detectability of these metabolites by +ESI and −ESI shows that neither ESI mode is universal. It is advantageous to screen for metabolites using both positive and negative ionization modes. This is especially true for phase II metabolism which tends to make molecules more polar and often more acidic. Identification of phase II metabolites also benefits greatly from MS3 experiments because the conjugating groups typically are cleaved in MS/MS and information on the core structure is only obtained in MS3. A special case of phase II metabolism is the generation of glutathione (GSH) conjugates from reactive metabolites. The detection of GSH conjugates also benefits from generating both positive and negative ESI mass spectral data. Copyright © 2007 John Wiley & Sons, Ltd.
Estimating the distribution of times from HIV seroconversion to aids using multiple imputation
Authors: Taylor Jeremy M. G. , Muñoz Alvaro , Bass Sue M. , Saah Alfred J. , Chmiel Joan S. , Kingsley Lawrence A.
Published: April 2007

Multiple imputation is a model based technique for handling missing data problems. In this application we use the technique to estimate the distribution of times from HIV seroconversion to AIDS diagnosis with data from a cohort study of 4954 homosexual men with 4 years of follow‐up. In this example the missing data are the dates of diagnosis with AIDS. The imputation procedure is performed in two stages. In the first stage, we estimate the residual AIDS‐free time distribution as a function of covariates measured on the study participants with data provided by the participants who were seropositive at study entry, Specifically, we assume the residual AIDS‐free times follow a log‐normal regression model that depends on the covariates measured at enrolment on the seropositive participants. In the second stage we impute the date of AIDS diagnosis for the participants who seroconverted during the course of the study and are AIDS‐free with use of the log‐normal distribution estimated in the first stage and the covariates from each seroconverter's latest visit. The estimated proportions developing AIDS within 4 and within 7 years of seroconversion are 15 and 36 per cent respectively, with associated 95 per cent confidence intervals of (10, 21) and (26,47) per cent.

We discuss the Bayesian foundations of the multiple imputation technique and the statistical and scientific assumptions.
Relative survival and the estimation of net survival: Elements for further discussion
Authors: Estève J. , Benhamou E. , Croasdale M. , Raymond L.
Published: April 2007

The methods of calculation of survival corrected for independent cause of death are discussed, and a maximum likelihood method is proposed and illustrated by survival of colon cancer patients in Geneva. The methods which are at present favoured for doing such calculations are subject to various biases when estimating net survival if the populations are heterogeneous for life expectancy. The proposed maximum likelihood approach would eliminate these biases by enabling relevant adjustment for covariates which influence survival. The routine use of such methods would permit better comparison of survival within and between populations.
On a test of non‐random segregation under linkage for autosomal recessive diseases
Authors: Pal Nabendu , Majumder Partha P.
Published: April 2007

To test the null hypothesis of random segregation of marker haplotypes from an unaffected parent to affected offspring for a one‐locus autosomal recessive disease, Majumder proposed a test statistic which was shown to be locally most powerful against the alternative hypothesis of non‐random segregation due to linkage between the disease and marker loci. The test procedure relied on a chi‐squared approximation to the null distribution of the test statistic. In the present study, we show that the chi‐squared approximation is poor for most practical situations and derive the exact null distribution of a function of the test statistic. We illustrate the method using published data on tuberculoid leprosy and HLA. We show that the test procedure is invariant, and extend the method to the case of a two‐locus recessive disease.
Sequential stopping rules in clinical trials
Authors: McPherson Klim
Published: April 2007

This paper reviews aspects of the development of sequential analysis of clinical trial data in medicine and suggests simple strategies for progress. The emphasis is on the pragmatic and ethical requirements of aspects of the design of phase III trials and in circumstances of genuine uncertainty characterized by much clinical experimentation. In particular consideration is given to the consequences of determining sample sizes from incorrect estimates of treatment effects. Armitage's work on sequential trials is traced to simple group sequential procedures based on repeated significance tests to minimize expected sample sizes in a wide class of experimental situations.
The summarizing of clinical experiments by significance levels
Authors: Anscombe F. J.
Published: April 2007

For a controlled clinical experiment in which two alternative treatments are compared, the statistical report often culminates in a significance test of the null hypothesis of no difference between the treatments, and significance at the 5 per cent level is taken as positive evidence of difference. It is argued that such an experiment serves primarily an inferential purpose; it is not a simple decision procedure, although its effect on practice may be considered in relation to ethical issues. Statistical inference should not be identified with testing this null hypothesis, despite the emphasis on such tests by R. A. Fisher in his work on design of experiments. This null hypothesis often has no interest or credibility.
HIV infection dynamics and intervention experiments in linked risk groups
Authors: Van Druten J. A. M. , Reintjes A. G. M. , Jager J. C. , Heisterkamp S. H. , Poos M. J. J. C. , Coutinho R. A. , Dijkgraaf M. G. W. , Ruitenberg E. J.
Published: April 2007

A multigroup transmission model is described to study the spread of the human immunodeficiency virus (HIV) in linked risk groups. The model permits simultaneous investigation of the increase in the number of persons infected with HIV in three main risk groups: promiscuous homo/bisexual men (two levels of promiscuity), intravenous drug users (men and women) and promiscuous heterosexual men and women.

Three modes of transmission are considered: anal and vaginal intercourse and needle sharing. The high dimensional parameter space is connected with the HIV incidence through a transmission matrix. This matrix summarizes the annual effective contact rates due to the three transmission modes. The model helps to clarify which data are needed to disentangle the relative contribution of the within‐ and between‐group transmission routes. The model can be used to perform theoretical experiments. Given a specification of a baseline, the potential use of the model is illustrated by a simulation describing the effect of blocking the transmission through needle sharing. Reference is made to the HIV/AIDS epidemic in The Netherlands.

The results of the study show that the model can play a part in the classification of HIV prevalence patterns in linked risk groups, in the study of the interaction between the main risk groups and in the theoretical evaluation of intervention measures.
Long term survival analysis: Breast cancer and age at diagnosis
Authors: Marubini E. , Mezzanotte G. , Boracchi P. , Veronesi U.
Published: April 2007

The role of age at surgery of breast cancer patients as a determinant of relative survival after making allowance for clinical profile was investigated on a case series of 716 women followed for more than 20 years in one single institution. Multiplicative and additive models for grouped data were adopted to model the standardized mortality ratio and the excess death rate respectively. The models were fitted by the GLIM system assuming a Poisson error function. We conclude that operable breast cancer has a relatively lower impact on total mortality in older patients (65 years or more) than in younger ones and, moreover, the older patients have a more favourable course.
CTS: A clinical trials simulator
Authors: Taylor D. W. , Bosch E. G.
Published: April 2007

We describe an interactive clinical trial simulation program (CTS), which provides continuous graphical and numerical output as a simulation runs. CTS is capable of simulating trials with two treatment groups and a binary outcome with options for: (1) up to five prognostic subgroups; (2) one of three methods of randomization; (3) equal or unequal allocation of patients to treatment groups; (4) a null or alternative hypothesis; (5) common methodological problems including biased referrals, false positive and false negative outcome reports, non‐compliance, dropouts, crossovers, and co‐interventions; (6) six methods of dealing with each of these problems; (7) the type I error rate, and (8) stratified or unstratified analysis. In addition, the current results of a trial in progress may be used as the starting point of the simulation to estimate the power of the trial if carried to completion. A few thousand simulations of a user specified scenario is typically entered and executed in about 5 minutes, permitting a student or clinical investigator to explore a variety of scenarios.
A stratified Wilcoxon‐type test for trend
Authors: Pee David Yu‐Wu , Freedman Laurence S.
Published: April 2007

We propose an extension of Cuzick's non‐parametric Wilcoxon‐like trend test for situations when the observations can be grouped into strata which are thought to be related to the outcome. We illustrate its use in two examples from cancer epidemiology.
A note on interrater agreement
Authors: Hirji Karim F. , Rosove Michael H.
Published: April 2007

We investigate the properties of a measure of interrater agreement originally proposed by Rogot and Goldberg.1 Unlike commonly used measures, this measure not only adjusts for chance agreement, but it also standardizes for both perfect agreement as well as for perfect disagreement. Further, one can also use this measure to assess category specific conditional agreement, and thus apply it to situations with missing main diagonal data. We provide an asymptotic method for inference with this measure.
Hierarchical models for multicentre binary response studies
Authors: Skene A. M. , Wakefield J. C.
Published: April 2007

A three‐stage hierarchical model is proposed for two treatment, binary response studies conducted in a number of centres. The approach adopted is Bayesian. Marginal densities for second stage parameters are shown to provide useful summaries both of comparative efficacy and of the heterogeneity of treatment effects across centres. Sensitivity studies of model assumptions are illustrated.
Assessing the gain in efficiency due to matching in a community intervention study
Authors: Freedman L. S. , Green S. B. , Byar D. P.
Published: April 2007

COMMIT (Community Intervention Trial for Smoking Cessation) is a randomized study employing a matched pairs design. Pairs of communities were selected on the basis of their geographical proximity and were chosen to be matched on variables strongly expected to relate to the outcome variable, the smoking quit rate. However, quantitative information was not available to evaluate the efficiency gain from matching. We have used baseline smoking quit rates in the communities as a surrogate for the outcome measure to evaluate the gain in efficiency from the matching. Our method takes account of the possible imperfection of the surrogate as a representative of the true outcome. The method estimates an efficiency gain of at least 50 per cent using the matched design. We also evaluate the further gains in efficiency which would be made by using the baseline quit rate to balance the randomization.
Modelling forces of infection for measles, mumps and rubella
Authors: Farrington C. P.
Published: April 2007

Serological data from 8870 persons collected prior to the introduction of measles, mumps and rubella (MMR) vaccine in the UK are used to describe the rate at which individuals acquire infection by these diseases at different ages. A parsimonious model is developed and titted under various interpretations of the data, particularly concerning the probability of lifelong susceptibility to infection. It is shown that, while the force of infection curves are relatively robust in their general features, they exhibit considerable sensitivity in matters of important detail. This is true in particular of the values taken by the force of infection in older age groups. As a result, estimates of the average age at infection are highly sensitive to these interpretations. This in turn may limit the accuracy of predictions from mathematical models based on these parameters, in particular regarding the level of immunization required for eradication of disease.
Evaluation of statistics teaching given to medical undergraduates
Authors: Newcombe Robert G.
Published: April 2007

Medical undergraduates in all United Kingdom medical schools receive statistics teaching, but attempts to establish the efficacy of this teaching have been scanty. Establishment of clear educational objectives is a prerequisite to meaningful evaluation. Most medical schools have attempted to obtain feedback from their students by administering questionnaires. Examination performance can provide very pertinent information if a finely detailed marking scheme is developed, identifying issues that are generally poorly grasped. Controlled trials may be carried out within an institution but encounter problems of contamination and low power. It should eventually be possible to develop multi‐institutional evaluation methods.
How we should approach the future
Authors: Murray Gordon D.
Published: April 2007

There is little doubt that there are still major problems with the statistical aspects of published medical research. This paper argues that it is not sufficient to teach statistics to medical undergraduates, when the bulk of their courses are being taught by the very same individuals who continue to undertake and publish substandard research. Instead, any undergraduate teaching needs to advance in parallel with courses given to the clinical staff within medical schools.
Multiple comparisons in over‐the‐ounter drug clinical trials with both positive and placebo controls
Authors: D'Agostino Ralph B. , Heeren Timothy C.
Published: April 2007

Evaluations of the efficacy of over‐the‐ounter drugs using ANOVA techniques often misuse multiple comparison procedures. Studies that involve both a placebo control and established drugs as positive controls are especially prone to these problems. The most common mistake involves using a procedure which does not control the experimentwise type I error rate, usually the Duncan procedure or some version of multiple t tests. These procedures control comparisonwise type I error rate, but lack the important experimentwise error control.

The purpose of this paper is to clarify the issues involved in performing ANOVA followed by a multiple comparison procedure for over‐the‐ounter drug studies involving both placebo and positive controls.
On estimating efficacy from clinical trials
Authors: Sommer Alfred , Zeger Scott L.
Published: April 2007

We define ‘biologic efficacy’ as the effect of treatment for all persons who receive the therapeutic agent to which they were assigned. It measures the biologic action of treatment among compliant persons. In a randomized trial with one treatment and one placebo control, one can theoretically estimate efficacy by comparing persons who complete the treatment regimen with controls who similarly complete the control regimen. In practice, however, we make this comparison with reservation because a control protocol often presents a different challenge for compliance than does the treatment, so that the compliant subgroups are not comparable. Standard practice employs intent‐to‐treat comparisons in which one compares those randomized to treatment and control without reference to whether they actually received the treatment. Intent‐to‐treat comparisons estimate the ‘programmatic effectiveness’ of a treatment rather than its biologic efficacy.

This paper introduces and derives the statistical properties of an alternative estimator of biologic efficacy that avoids the potential selection bias inherent in a comparison of compliant subgroups. The method applies to randomized trials with a dichotomous outcome measure, whether or not a placebo is given to the control group. The idea is to compare the compliers in the treatment group to an inferred control subgroup chosen to eliminate selection bias. The methodology was motivated by and is illustrated in the analysis of a randomized community trial of the impact of vitamin A supplementation on children's mortality.
Empirical bayes estimates of cancer mortality rates using spatial models
Authors: Mollie Annie , Richardson Sylvia
Published: April 2007

We give results of empirical Bayes (EB) estimation of mortality rates designed to smooth observed SMR when random fluctuation of the observed deaths is important. We have specially studied the case where the prior distributions of the EB method have a spatial structure. The need for spatial modelling of cancer mortality rates in France is first shown with testing autocorrelation and fitting autoregressive spatial models, conditional (CAR) or simultaneous (SAR). A positive autocorrelation of the rates is shown for most cancer sites studied. As expected, EB estimates of mortality rates for common tumours are similar to SMRs. For rare tumours, the EB method identifies the extreme rates more clearly than SMRs by smoothing the SMRs with large variances. CAR or SAR models are adequate prior distributions for autocorrelated rates and produce quite similar rate estimates.
Bayesian methods for prevalence estimation from incomplete administrative lists
Authors: Smith Philip J.
Published: April 2007

Many studies aimed at estimating prevalence use several administrative lists from different sources in an attempt to enumerate all persons affected with the health condition of interest. Each list is ‘incomplete’ in the sense that none of them enumerates all persons affected with the health condition. Further, because the lists are drawn from different administrative sources the probability of enumeration varies from list to list. The goal is to use information from the lists to estimate the total number of affected persons in the population, but with some accounting for the different but unknown probabilities of enumeration on each list. This paper presents a Bayesian method to estimate prevalence when the probability of enumeration varies from list to list. Data from a survey of children with spina bifida illustrate the methodology.
Estimating prediction equations in repeated measures designs
Authors: Stanek Edward J. , Kline Gregory
Published: April 2007

Experimental designs with repeated measures allow response patterns over time (or dose) to be modelled and compared between different homogeneous groups. Issues in data analysis often focus on the pattern of variation of the repeated measures, the appropriateness of a univariate or multivariate analysis, and the shape of the response pattern. An aspect of analysis that is often of equal importance is the development of a regression model for response once the pattern has been characterized. Analysis of variance or multivariate growth curve results often do not include easily interpretable regression equation estimates that can be used for prediction. We present methods and tables that permit simple construction of such predictive equations for repeated measures designs when response is modelled as a polynomial over time with univariate or multivariate analyses.
The preliminary clinical evaluation of the safety and efficacy of a fertility regulating vaccine
Authors: Griffin P. D. , Jones W. R.
Published: April 2007

Since the mid 1970s, the World Health Organization's Special Programme of Research, Development and Research Training in Human Reproduction, through its Task Force on Vaccines for Fertility Regulation, has been supporting and coordinating the development of a birth control vaccine based on a synthetic peptide representing part of the beta subunit of human chorionic gonadotrophin (β‐hCG). The majority of this work has been carried out in the laboratories of Dr V. C. Stevens at Ohio State University. This hCG vaccine has recently been tested for tolerability and immunogenicity in a phase I clinical trial, and a preliminary evaluation of the vaccine's efficacy in fertile women volunteers is currently being planned. This paper describes the preclinical and clinical testing of this vaccine and the problems and challenges presented by this novel preparation. These have included the need to carry out safety and efficacy studies in the baboon, and the difficulties of designing appropriate preclinical toxicity and immunosafety studies and clinical trial protocols in the absence of previous experience with vaccines of this type.
Identifying the fertile phase of the human menstrual cycle
Authors: Royston Patrick
Published: April 2007

The identification of the human fertile phase as the time during which a woman or a couple may conceive is elusive. The fertile time depends on many factors in each individual menstrual cycle and may be said to be more of a statistical than a physiological entity. This paper reviews the application of statistical methods to three areas related to conception and the fertile phase. The first is the prediction and detection of ovulation from serial measurements, such as hormones, basal body temperature and cervical mucus, throughout the menstrual cycle. Typically, such variables increase from some baseline level to a peak around ovulation (the most fertile time), then subside to low levels in the postovulatory phase. The statistical challenge is to detect the rise (signalling the onset of potential fertility) and subsequent fall. Analytic methods considered include thresholds, Bayesian change‐point models and particularly the cumulative sum (cusum) technique which is both simple to apply and understand, and effective. The second area comprises appropriate methods of analysing and interpreting data from clinical studies of the fertile phase, especially in so‐alled natural family planning (NFP) where it is usual for women to observe several indices of potential fertility. Such studies usually try to establish the temporal relationships between markers of the fertile phase and examine the success of different combinations of markers in delineating the fertile time in comparison with a standard ‘defined’ phase, for example, the interval from three days before to two days after the peak of luteinizing hormone. The third area is the assessment of the probability of conception on certain days of the cycle, which is vital to the understanding of the fertile phase and its application to NFP. Direct estimation of such probabilities is impractical; instead, resort must be made to estimation by maximum likelihood of the parameters of specially constructed models. Suitable models are described. Finally, the need for a new prospective study of the probability of conception in relation to the markers of the fertile phase used in the symptothermal method of NFP is discussed.
Using the ratio of urinary oestrogen and progesterone metabolites to estimate day of ovulation
Authors: Baird Donna Day , Weinberg Clarice R. , Wilcox Allen J. , McConnaughey D. Robert , Musey Paul I.
Published: April 2007

We have developed a method of estimating day of ovulation using urinary ovarian hormone data. The method identifies a day of luteal transition that occurs at the shift from production of follicular oestrogen to luteal progesterone. The algorithm for identifying this shift was evaluated and judged better than specified alternatives in that it resulted in (1) a high concordance between the day of luteal transition and peaks in urinary luteinizing hormone (LH) for cycles with well‐defined peaks, (2) a low variance in the length of the luteal phase of the menstrual cycle, which presumably reflects a low measurement error in estimating day of ovulation, and (3) a high proportion of cycles for which an approximate day of ovulation could be determined. To validate the new algorithm, it was applied to an independent data set. The algorithm identified a day of luteal transition in 88 percent of these cycles, and the identified day occurred within two days of the urinary LH peak for all of the cycles with clear LH peaks. Determination of the day of luteal transition to estimate ovulation requires only first‐morning urine specimens, requires no correction for day‐to‐day variations in urine concentration, and can be applied to a mid‐ycle window of data.
Monitoring a randomized clinical trial for futility: The north‐norwegian lidocaine intervention trial
Authors: Berntsen Rolf Franck , Rasmussen Knut , Bjørnstad Jan F.
Published: April 2007

Randomized clinical trials of acute disease are usually designed as single‐look, fixed sample size trials. This methodological study compares the conventional approach with a multiple‐look, group sequential design with stopping rules for both treatment efficacy and for an inconclusive trial outcome, called trial futility. An ongoing trial on pre‐hospital prophylaxis of sudden death in acute myocardial infarction forms the basis of the analysis. The effects of introducing multiple looks (or interim analyses) and tests for futility were obtained by binomial simulation. The introduction of four looks (that is, three interim and a final analysis) resulted in a modest increase in the maximal number of patients required. This was, however, fully compensated for by the high probability of early termination in case of treatment efficacy. The addition of futility tests, enabling termination at half the maximum trial size when there is no treatment difference, resulted in only a negligible reduction of overall power. We conclude that multiple‐look, group sequential designs testing for both treatment efficacy and trial futility may improve the cost‐effectiveness of randomized trials of acute disease.
Generalization of normal discriminant analysis using fourier series density estimators
Authors: Odom‐Maryon Tamara , Langholz Bryan , Niland Joyce , Azen Stanley
Published: April 2007

In this paper we examine the efficiency of a generalization of the traditional normal linear (LDA) or quadratic (QDA) discriminant analysis. This procedure (the generalized discriminant analysis, GDA) replaces each normal density used in the traditional classification rule by a Fourier series density estimator which ‘adjusts’ the normal density if the data deviate markedly from normality (for example, heavily skewed or multimodal). We derive the GDA in both the univariate and multivariate situations. In a simulation study for the univariate situation, we evaluate the relative efficiency of the GDA. In addition, we demonstrate the performance of the GDA through a series of multivariate applications. We conclude that if the distributions of the data do not deviate markedly from normality, the GDA is as efficient as the LDA or QDA. On the other hand, if either of the distributions deviates from normality, then the GDA, which performs as a semiparametric discriminant procedure, is more efficient than the LDA or QDA.
An approach to several problems in using large databases for population‐based case‐ontrol studies of the therapeutic efficacy and safety of anti‐hypertensive medicines
Authors: Psaty Bruce M. , Koepsell Thomas D. , Siscovick David , Wahl Pat , Logerfo James P. , Inui Thomas S. , Wagner Edward H.
Published: April 2007

In this paper we discuss approaches to two distinct problems in using large computerized databases to conduct population‐based case‐ontrol studies. The first topic, concerning case ascertainment, is a methodo‐logic problem, and the second, concerning confounding by indication, is an analytic problem. The first involves attempting to ascertain all incident cases of coronary disease among enrollees of Group Health Cooperative of Puget Sound (GHC), a large health‐maintenance organization. Methodologic studies reported here have helped us improve the efficiency of using the large computerized databases for case ascertainment at GHC. The second problem involves the issue of confounding by drug indication. Drugs such as beta‐blockers have multiple indications, including the treatment of both high blood pressure and angina. These two indications may make it difficult in an observational study to determine whether beta‐blockers may prevent coronary disease in patients with high blood pressure. We discuss here our current thinking about the best analytic approach to this problem.
Using empirical bayes methods in biopharmaceutical research
Authors: Louis Thomas A.
Published: April 2007

A compound sampling model, where a unit‐specific parameter is sampled from a prior distribution and then observeds are generated by a sampling distribution depending on the parameter, underlies a wide variety of biopharmaceutical data. For example, in a multi‐centre clinical trial the true treatment effect varies from centre to centre. Observed treatment effects deviate from these true effects through sampling variation. Knowledge of the prior distribution allows use of Bayesian analysis to compute the posterior distribution of clinic‐specific treatment effects (frequently summarized by the posterior mean and variance). More commonly, with the prior not completely specified, observed data can be used to estimate the prior and use it to produce the posterior distribution: an empirical Bayes (or variance component) analysis. In the empirical Bayes model the estimated prior mean gives the typical treatment effect and the estimated prior standard deviation indicates the heterogeneity of treatment effects. In both the Bayes and empirical Bayes approaches, estimated clinic effects are shrunken towards a common value from estimates based on single clinics. This shrinkage produces more efficient estimates. In addition, the compound model helps structure approaches to ranking and selection, provides adjustments for multiplicity, allows estimation of the histogram of clinic‐specific effects, and structures incorporation of external information. This paper outlines the empirical Bayes approach. Coverage will include development and comparison of approaches based on parametric priors (for example, a Gaussian prior with unknown mean and variance) and non‐parametric priors, discussion of the importance of accounting for uncertainty in the estimated prior, comparison of the output and interpretation of fixed and random effects approaches to estimating population values, estimating histograms, and identification of key considerations in the use and interpretation of empirical Bayes methods.
A statistical procedure for the estimation of accuracy parameters in interlaboratory studies
Authors: Kristiansen Sören
Published: April 2007

Interlaboratory studies are conducted to estimate the accuracy of methods of laboratory measurements. The standard parameters used to describe this accuracy are the repeatability and the reproducibility. Usually variance components models are used to estimate these parameters. If the model assumptions are violated the resulting estimates for reproducibility and repeatability may, however, be biased. A new method of residual analysis in variance components models‐developed by the author‐may be used to detect violations of the model assumptions. If the residual analysis indicates that the model assumptions are violated, a simple robust method‐which makes fewer assumptions‐may be used for the estimation of accuracy parameters.

The application of this residual analysis is demonstrated using data of an interlaboratory study. Graphical methods play an important role in the evaluation of the residuals. The analysis of the residuals uses methods similar to those used for the analysis of Studentized residuals in the linear model.

The estimates obtained by the variance components model and the simple robust method are compared. The results of the residual analysis may be used to decide which of the two estimates can be considered more appropriate.

The necessity of residual analysis in the analysis of interlaboratory studies by variance components models is pointed out. Potential hazards inherent to residual analysis in variance components models are discussed. Conclusions for the analysis of interlaboratory studies are drawn.
Estimation of the mean and standard deviation using order statistics
Authors: Dunstan F. D. J. , Nix A. B. J.
Published: April 2007

Estimation of the mean and standard deviation of a distribution using linear combinations of the order statistics of a censored sample is discussed. Existing methods are compared with the optimal estimator of this type and new approximations to the optimal estimator are proposed. Their performances in the presence of outliers are assessed by means of simulation.
Analysis of the crossover design in the presence of residual effects
Authors: Lehmacher W.
Published: April 2007

When a residual effect is suspected in a two‐period crossover trial, an analysis of the first‐period data is often chosen instead of the potentially biased crossover analysis. This paper indicates how the usual crossover test has to be interpreted correctly and that its bias has two different consequences, namely a conservative or a liberal test decision if a positive (carryover) or a negative (withdrawal) residual effect exists. A multiple testing procedure is presented allowing for simultaneous crossover and first‐period analysis controlling the experimental error rate. This procedure together with the correct interpretation of the crossover test enables many useful applications of crossover designs.
Individual bioequivalence: What matters to the patient
Authors: Hauck Walter W. , Anderson Sharonerson
Published: April 2007

Current procedures for assessing the bioequivalence of two formulations are based on the concept of average bioequivalence. That is, they assess whether the average responses between individuals on the two formulations are similar. We show first that average bioequivalence is not sufficient to assure that an individual patient could be expected to respond similarly to the two formulations. To have such reasonable assurance requires a different notion of bioequivalence; individual (or within‐subject) bioequivalence.

Second, we propose a simple statistical procedure for assessing individual bioequivalence. This decision rule, TIER (test of individual equivalence ratios) requires the specification of the minimum proportion of subjects in the applicable population for which the two formulations being tested must be bioequivalent (a regulatory decision). The TIER rule is summarized in terms of the minimum number of subjects with bioavailability ratios falling within the specified equivalence interval necessary to be able to claim bioequivalence for given sample size and type I error. We recommend that the corresponding lower bounds (one‐sided confidence intervals) for the proportion of bioequivalent subjects be calculated.
Sample sizes for bioequivalence studies
Authors: Metzler Carl M.
Published: April 2007

In recent years a number of decision rules, based on sound statistical principles, have been proposed for deciding if a test formulation is bioequivalent to a reference formulation. The decision rule based on confidence intervals has been accepted by regulatory agencies, at least by the Food and Drug Administration of the United States. A useful property of this decision rule is that the regulatory agency need not require a certain sample size, since the level of protection against wrongly deciding bioequivalence is set by the choice of the alpha level used to compute the confidence intervals. The manufacturer claiming bioequivalence is concerned about sample size, for sample size determines the probability of falsely deciding non‐bioequivalence when the test formulation does indeed have an acceptable relative bioavailability. Curves of probability of rejecting bioequivalence have been computed for error coefficient of variation of 10, 20 and 30 per cent, for relative bioavailability from 70 to 130 per cent, and for protection levels of 90 and 95 per cent. These curves can be used for choosing the sample size for a bioequivalence study.
Random models for margins of a 2 × 2 contingency table and application to pharmacovigilance
Authors: Tubert Pascale , Begaud Bernard
Published: April 2007

The identification of new adverse drug reactions is often tricky. For a given case, the relationship between drug exposure and symptom occurrence is usually questionable. It could be investigated statistically from a series of drug‐event association cases with an independence test between the two variables. Analysing the related 2 × 2 contingency table obviously requires knowledge of its margins. However this information is often not available. We develop a calculation of the P‐value by choosing some random models for the unknown margins. Under the hypothesis of independence, a negative binomial distribution for the frequency of the drug‐event association cell is obtained, exactly or approximately. This method allows investigation of the causal relationship when some parameters, as incidence of the symptom in the population, are uncertain. An example is presented.
Analyses of black and white differentials in the age trajectory of mortality in two closed cohort studies
Authors: Manton Kenneth G. , Stallard Eric , Wing Steve
Published: April 2007

We examine the relationship of age to mortality in blacks and whites in two cohort studies, the 20‐year follow‐up of the Evans County, Georgia, Study population and the 25‐year follow‐up of the Charleston Heart Study population. We conducted analyses with two parametric forms of hazard models (Gompertz and Weibull) for total mortality experience and considered the fit of the two hazard models in each study both separately and with the data pooled. We evaluated the robustness of conclusions about differences in the age pattern of mortality for blacks and whites by comparing results from the two hazard models. Where the tests were non‐nested, we used an information‐based statistic (AIC) to compare the fit of the models to the data. Results were robust to the selection of the hazard function, that is both models provided evidence that mortality rates at younger ages were lower for whilte than black females but that mortality rates increased more rapidly with age for white females. The absolute differences and the differences in the rates of increase were in the same direction, but smaller, for males. Though both models represented the general features of mortality patterns, the information statistic suggested better performance of the Gompertz function. The Gompertz function was also less sensitive to the initial age of determination of mortality exposure.
Estimating standardized parameters from generalized linear models
Authors: Greenland Sander
Published: April 2007

Although the traditional unrestricted ('non‐parametric') estimators of directly standardized rates and rate differences remain unbiased in sparse data, they tend to suffer from instability (low precision). As a result, many authors have proposed more precise estimators based on parametric models for the rates. This paper provides a general approach for constructing estimators of standardized parameters using generalized linear models, and shows that, in some common special cases, these model‐based ('smoothed') estimators can have an exceptionally simple form.
Evaluation of statistical methods for monitoring periodontal disease
Authors: Yang Mark C. K. , Marks Ronald G. , Clark William B. , Magnusson Ingvar
Published: April 2007

Three methods for detecting the progression of periodontal disease based on the change in attachment level on teeth are compared from a statistical point of view. Regardless of gradual or burst disease progression theories, the regression method is found to be the most efficient way to detect changes. The exact false alarm rate is computed when the decision is based on the maximum regression coefficient at all sites during several visits.
Mean square error of estimates of HIV prevalence and short‐term aids projections derived by backcalculation
Authors: Rosenberg P. S. , Gail M. H. , Pee D.
Published: April 2007

We simulated multinomial AIDS incidence counts from 27 ‘representative’ AIDS epidemics that spanned a period corresponding to previous applications of backcalculation (1 January 1977 to 1 July 1987) and assessed mean square error for several back‐calculated estimators of HIV prevalence and short‐term AIDS projections. Estimators were based on flexible model selection procedures that chose the best‐fitting non‐negatively constrained model of the infection curve from a family of possible step‐function models. Selection of the best‐fitting model from a family of four‐step models each with a long last step of width of 4 or 4.5 years offered a favourable tradeoff between bias and variance when compared with selection from families of models with three steps or from families with a short last step. Five‐step models performed as well as four‐step models. Three‐step models had substantially larger mean square error in some epidemic situations. Percentage root mean square error (PRMSE) for estimates of cumulative HIV prevalence as of 1 January 1985 was less than 14 per cent over a range of hypothetical epidemics of N = 50,000 infected individuals. PRMSE for short‐term projections was less than 18 per cent. Estimates of cumulative HIV prevalence as of 1 July 1987 were substantially more uncertain and had a PRMSE of 33 per cent in the unfavourable case of a rapidly rising HIV epidemic. Estimates of cumulative HIV prevalence as of 1 July 1987 were positively biased in HIV epidemics with a rapidly decreasing recent HIV incidence rate and negatively biased in rapidly increasing HIV epidemics. Despite these uncertainties, we obtained useful estimates even for HIV epidemics with as few as 5000 infected individuals.
Misclassification of covariates
Authors: Walker Alexander M. , Lanes Stephan F.
Published: April 2007

In the context of reported data on asthma mortality, we examine two types of covariate misclassification. The first type is non‐differential misclassification, in which the proportion of subjects misclassified is invariant over exposure and disease status. Building on work by Cox and Elwood and by Blettner and Wahrendorf, we find that the range of admissible values for misclassification proportions is bounded by the observed data, and may not include any values that account for observed heterogeneity of effect estimates. The second type is differential misclassification, in which the classification error differs according to the disease or exposure classes to which study subjects belong. If the relation between exposure and the confounding variable or between the confounding variable and disease is strong, differential misclassification can produce large variations in the stratum‐specific odds ratio estimates.
Validation techniques for logistic regression models
Authors: Miller Michael E. , Hui Siu L. , Tierney William M.
Published: April 2007

This paper presents a comprehensive approach to the validation of logistic prediction models. It reviews measures of overall goodness‐of‐fit, and indices of calibration and refinement. Using a model‐based approach developed by Cox, we adapt logistic regression diagnostic techniques for use in model validation. This allows identification of problematic predictor variables in the prediction model as well as influential observations in the validation data that adversely affect the fit of the model. In appropriate situations, recommendations are made for correction of models that provide poor fit.
Statistical analysis of repeated events forming renewal processes
Authors: Aalen Odd O. , Husebye Einar
Published: April 2007

For each of several individuals a sequence of repeated events, forming a renewal process, is observed up to some censoring time. The object is to estimate the average interevent time over the population of individuals as well as the variation of interevent times within and between individuals. Medical motivation comes from gastroenterology, and concerns the occurrence of certain cyclic movements in the small bowel during the fasting state. Two statistical models are considered: one is the standard variance component model adapted to censored data, and the other is a recent intensity based model with a random proportionality factor representing interindividual variation. These models are applied to the motility data, and their advantages are discussed. The intensity based model allows simple empirical Bayes estimation of the expected interevent times for an individual in the presence of censoring.
Homogeneity of the relative risk in cohort studies
Authors: Le Chap T. , Lindgren Bruce R. , Zelterman Daniel , Umen Andrew J.
Published: April 2007

We propose a statistical method to test the homogeneity of relative risk in cohort studies. We illustrate its application to an original data set from a low‐risk group of end‐stage renal disease patients on haemodialysis. The use of the method is as a global test against the omnibus alternative.