bluuebunny/crossref_metadata_2025 huggingface dataset:
Inclusion complexes of estrone and estradiol with β‐cyclodextrin: Voltammetric and HPLC studies
Authors: Yañez Claudia , Basualdo Julio , Jara‐Ulloa Paola , Squella J. Arturo
Published: April 2007

The interaction of estrone and estradiol with β‐cyclodextrins (βCD) was investigated by differential pulse voltammetry (DPV) and high‐performance liquid chromatography (HPLC) in mixed media. The co‐solvent influence on the tendency of these estrogens to form inclusion complexes with βCD was examined. Thus, acetonitrile (MeCN) and ethanol (EtOH) were used in a mixed aqueous medium containing phosphate buffer. The association constant of the inclusion complexes (Ka) of estrone and estradiol with βCD were determined in two different media by using both voltammetric and chromatographic techniques. Estradiol was found to bind to βCD with higher affinities than estrone, irrespective of the medium. We have also found a clear influence of the co‐solvent on the Ka value, which means a competition of co‐solvent molecules with estrogens for binding to the cavity of βCD. Consequently, interaction between βCD and the steroids is weakened when acetonitrile is used. Copyright © 2007 John Wiley & Sons, Ltd.
Experimental evidence of chiral crown ether complexation with aromatic amino acids
Authors: Koźbiał Małgorzata , Poznański Jarosław
Published: April 2007

The complexation of L‐ and D‐enantiomers of phenylglycine, phenylalanine, and tryptophan with D‐mannonaphto‐crown‐6‐ether in methanol solution was studied by NMR and isothermal titration calorimetry (ITC) at 298.15 K. The total heat effects attributed to the binding phenomena were measured in the range of 1.8 to 7.7 mJ, and the complexation was found stereo‐specific. The binding topologies were estimated basing on 1H 2D‐ROESY experiments. The analysis of Job plots obtained from 1H NMR‐monitored titrations proved the coexistence of 1:1 and 1:2 (crown ether:amino acids) complexes, which thermodynamic parameters, Ks, ΔG, ΔH°, and TΔS were determined with the aid of ITC. The 1:1 complexes were found enthalpically stabilized, generally by electrostatic interactions between the charged NH group of amino acid and crown ether macrocyclic moiety, while the binding of the second amino acid molecule was driven entropically due to solvatophobic effect. Strong enthalpy–entropy compensation points towards the uniform binding mode of all complexes studied. The mode of complex formation was found solvent dependent. For phenylalanine guest studied in various solvent systems, in contrast to the aqueous media, the noticeable chiral recognition is observed in methanol solution, and the complex stoichiometry (1:2 ether:Phe) differs from the 2:1 one, determined previously for the same host‐guest system in water (J. Thermal. Anal. Cal. 2006; 83: 575–578). Copyright © 2007 John Wiley & Sons, Ltd.
Rate and product studies with 2‐adamantyl fluoroformate under solvolytic conditions
Authors: Kyong Jin Burm , Rhu Chan Joo , Kim Yong‐Gun , Kevill Dennis N.
Published: April 2007

The specific rates of solvolysis of 2‐adamantyl fluoroformate have been measured at 25.0 °C in 20 pure and binary solvents. These are well correlated using the extended Grunwald–Winstein equation, with incorporation of the NT solvent nucleophilicity scale and the YCl solvent ionizing power scale. The sensitivities (l = 2.15 ± 0.17 and m = 0.95 ± 0.07) toward the changes in solvent nucleophilicity and solvent ionizing power, and the kF/kCl values are very similar to those previously observed for solvolyses of n‐octyl fluoroformate, consistent with the addition step of an addition‐elimination pathway being rate‐determining. For aqueous ethanol, measurement of the product ratio allowed selectivity values (S) to be determined. The results are compared with those reported earlier for 2‐adamantyl chloroformate and mechanistic conclusions are drawn. Copyright © 2007 John Wiley & Sons, Ltd.
RAFT polymerization of styrenic‐based phosphonium monomers and a new family of well‐defined statistical and block polyampholytes
Authors: Wang Ran , Lowe Andrew B.
Published: May 2007

We describe herein the first example of the controlled reversible addition‐fragmentation chain transfer (RAFT) radical homo‐ and copolymerization of phosphonium‐based styrenic monomers mediated with a trithiocarbonate‐based RAFT chain transfer agent (CTA) directly in aqueous media. In the case of homopolymer syntheses the polymerizations proceed in a controlled fashion yielding materials with predetermined molecular characteristics as evidenced from the narrow molecular mass distributions (MMD) and the excellent agreement between the theoretical and experimentally determined molecular masses (MM). We also demonstrate the controlled nature of the homopolymerization of 4‐vinylbenzoic acid with the same CTA in DMSO. We subsequently prepared both statistical and block copolymers from the phosphonium/4‐vinylbenzoic acid monomers to yield the first examples of polyampholytes in which the cationic functional group is a quaternary phosphonium species. We show that the kinetic characteristics of the statistical copolymerizations are different from the homopolymerizations and proceed, generally, at a significantly faster rate although there appears to be a composition dependence on the rate. Given the inherent problems in characterizing such polyampholytic copolymers via aqueous size exclusion chromatography we have qualitatively proved their successful formation via FTIR spectroscopy. Finally, in a preliminary experiment we qualitatively demonstrate the ability of such pH‐responsive block copolymers to undergo supramolecular self‐assembly. © 2007 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 45: 2468–2483, 2007
Detection and assessment of clusters of disease: An application to nuclear power plant facilities and childhood leukaemia in Sweden
Authors: Waller Lance A. , Turnbull Bruce W. , Gustafsson Göran , Hjalmars Ulf , Andersson Bertil
Published: April 2007

We review some recent statistical methods for examining geographic patterns of disease incidence for the presence of clusters. General methods search for clusters throughout the study area and then assess the statistical significance of any clusters detected. Focused methods check for elevated incidence rates close to prespecified locations of putative sources of hazard. We apply the methods to leukaemia incidence data for children aged 0–15 years in Sweden (1980–1990), particularly in reference to locations of nuclear power facilities. Unlike some other studies, notably in the United Kingdom, we do not find any significant clusters.
Estimating the proportion of immunes in censored samples: A simulation study
Authors: Ghitany M. E. , Maller R. , Zhou S.
Published: April 2007

We review currently known results concerning the estimation of an ‘immune’ or ‘cured’ proportion, and testing for the presence of immunes, in censored survival data, suggesting that a firm theoretical foundation now exists for analysis. Two types of estimators, parametric and non‐parametric, are discussed and compared with respect to their theoretical properties, and, by simulation, with respect to their small sample behaviour. Both estimators have advantages and drawbacks, but together provide powerful tools for the perceptive analysis of survival data with, or even without, immune individuals.
A neural network model for survival data
Authors: Faraggi David , Simon Richard
Published: April 2007

Neural networks have received considerable attention recently, mostly by non‐statisticians. They are considered by many to be very promising tools for classification and prediction. In this paper we present an approach to modelling censored survival data using the input—output relationship associated with a simple feed‐forward neural network as the basis for a non‐linear proportional hazards model. This approach can be extended to other models used with censored survival data. The proportional hazards neural network parameters are estimated using the method of maximum likelihood. These maximum likelihood based models can be compared, using readily available techniques such as the likelihood ratio test and the Akaike criterion. The neural network models are illustrated using data on the survival of men with prostatic carcinoma. A method of interpreting the neural network predictions based on the factorial contrasts is presented.
Maximum likelihood estimation of the kappa coefficient from models of matched binary responses
Authors: Shoukri M. M. , Martin S. W. , Mian I. U. H.
Published: April 2007

We present an estimate of the kappa‐coefficient of agreement between two methods of rating based on matched pairs of binary responses and show that the estimate depends on the common intraclass correlation coefficient between the pairs. Via Monte Carlo simulation, we investigate power of the test of significance on kappa, and the large sample bias and variance of its maximum likelihood estimator.
Small clinical trials: Are they all bad?
Authors: Matthews John N. S.
Published: April 2007

Statistician have long argued that randomized controlled trials should be sufficiently large to achieve their purpose, and for common diseases with major public health implications this has brought many benefits. However, there are many instances where it is unrealistic to expect clinicians to provide the information on which sample sizes are calculated and undue emphasis on trial size can be counterproductive. Examples of such trials are given and some general issues discussed, including the unhelpful contribution of hypothesis testing, the need for replication and the role of the statistician.
Meta‐analysis: Weighing the evidence
Authors: Jones David R.
Published: April 2007

Use of meta‐analytical (quantitative overview) techniques is now commonplace in a wide range of medical research contexts, with a rapid rise in its frequency of use being particularly apparent in the last decade. The history of meta‐analyses in other fields, particularly psychology and educational research, is longer. Many methods have been proposed and used, from crude ‘vote counting’ of studies showing significant or non‐significant results, through methods for combination of effect size estimates based on fixed or random effects models, to general linear mixed models and Bayesian methods. The history of meta‐analysis and the advantages and disadvantages of various approaches to it are briefly reviewed in this paper, with reference to pharmaceutical product licence applications, other reviews of clinical trials and epidemiological studies, and health services research.
Controversies in cancer epidemiology
Authors: Chilvers Clair E. D.
Published: April 2007

The histories of three controversies are described: fluoridation of water supplies and the risk of cancer; whether oral contraceptives cause breast cancer; and the role of self‐examination in the prevention of cancer. For the future, some new controversies are already predictable. As well as the scientific debate around these topics, the roles of politics, the media, and pressure groups are discussed. The epidemiologist's responsibility to ensure that the correct interpretation of data is made clear to the public is considered.
Problems and prediction in survival‐data analysis
Authors: Henderson Robin
Published: April 2007

Twenty‐one years after its appearance, Cox's 1972 paper on ‘Regression models and life tables’ continues to be one of the most frequently cited publications in the scientific and medical literature. The proportional‐hazards model and partial‐likelihood technique have been applied to thousands of data sets, not always appropriately, and have motivated hundreds of theoretical studies, not always relevant. Recent developments are reviewed and continuing problems discussed, especially with respect to predictive inference. The accuracy of model‐based predictions, and how they compare with consultants' judgements are investigated by means of an example.
What do we mean by a statistical model?
Authors: Appleton David R.
Published: April 2007

Statisticians are too often satisfied by fitting data rather than investigating the process out of which the data arose. The assumptions on which they base their models may be quite unrealistic, and while it is true that a model should not be more complicated than necessary, neither should it be too simple. Ways of approaching several sets of data from different areas of clinical medicine are considered, and different attitudes to the purpose of modelling highlghted. The transition from smoothing data, through fitting curves, to modelling underlying processes is discussed.
Teaching statistics to non‐specialists
Authors: Simpson Judy M.
Published: April 2007

Teaching statistics to non‐specialists is a challenge for which most statisticians are unprepared by their own training within an academic mathematics department. Most statistics courses for medical undergraduates still focus on research statistics, whereas it would be more appropriate to concentrate on statistics relevant to clinical decision‐making about an individual patient. Teaching statistics to Master of Public Health students presents further challenges because of the wide variety of their backgrounds and the greater demands from mature postgraduates. Whatever the audience, however, the same principles apply: medical statistics should be taught as non‐mathematically as possible, only introducing formulae when absolutely necessary and explaining their components; plenty of practical applications should be given; there should be ample opportunity for practice to gain hands‐on experience using both calculators and computers (preferably with MINITAB); and tutorials should be streamed according to perceived mathematical ability, with remedial mathematics teaching available to those who need it.
Life as an academic medical statistician and how to survive it
Authors: Pocock Stuart J.
Published: April 2007

This article aims to review the professional life of academic medical statisticians. Topics covered include training opportunities, the shortage of medical statisticians, the relevance of PhD degrees, the varied working life (teaching, research, computing, refereeing etc.), research priorities across consultancy, collaborations and methodology, professional recognition and the variety of working environments. In conclusion, some tentative suggestions are made on how to survive happily in the world of academic medical statistics.
Adaptive assignment versus balanced randomization in clinical trials: A decision analysis
Authors: Berry Donald A. , Eick Stephen G.
Published: April 2007

We compare balanced randomization with four adaptive treatment allocation procedures in a clinical trial involving two treatments. The objective is to treat as many patients in and out of the trial as effectively as possible. Randomization is a satisfactory solution to the decision problem when the disease in question is at least moderately common. Adaptive procedures are more difficult to use, but might play a role in clinical research when a substantial proportion of all patients with the disease are included in the trial.
Binary regression with continuous outcomes
Authors: Suissa Samy , Blais Lucie
Published: April 2007

Clinical research often involves continuous outcome measures, such as blood cholesterol, that are amenable to statistical techniques of analysis based on the mean, such as the t‐test or multiple linear regression. Clinical interest, however, frequently focuses on the proportion of subjects who fall below or above a clinically relevant cut‐off value, as a measure of the risk of disease. The customary approach to analyse such data is to dichotomize the continuous outcome measure and use statistical techniques based on binary data and the binomial distribution. In this paper, we use a parametric approach and the framework of generalized linear models to fit various regression models, including the logistic, on the basis of the original continuous outcome. We consider the Gaussian and the three‐parameter log‐normal distributions for the continuous outcome, assessing both precision and bias under various conditions. In simulation analyses, we find that we are unable to fit some of the samples with the ‘dichotomous’ approach, but we can with the ‘continuous’ approach, and that the latter yields estimates between 25 and 85 per cent more efficient than the former. We illustrate the method, programmed using GLIM macros, with data from clinical studies of the risk of hypoxaemia during open thoracic surgery and the risk of nocturnal hypoglycaemia among diabetic children.
Age‐related standards for ordinal data: Modelling the changes in visual acuity from 2 to 9 years of age
Authors: Wade A. M. , Ades A. E. , Salt A. T. , Jayatunga R. , Sonksen P. M.
Published: April 2007

The visual acuities of 1140 children between the ages of 2 and 9 years were measured using the Sonksen—Silver Acuity System (SSAS) from a distance of 3 metres. Ordered logistic regression was used to construct smoothly changing age‐related reference ranges for the proportions of children at different ages expected to have acuities in each of three ordinal categories. Exponential functions of age fitted the data better than polynomials. The resulting reference ranges can be used to derive the ages at which a given percentage of children achieve each of the acuity cut‐offs. However, since it is unlikely that all children will be available for testing at the exact appropriate age, two possible alternatives are introduced and discussed. First, a new ordinal category that would result in a chosen outcome at a pre‐selected age is calculated. Secondly, because it may be more feasible to use the current SSAS system whilst varying the distance of measurement, age‐related standards showing the distances required to give constant pass‐rates with the usual letter‐sizes are also presented.
Non‐parametric estimation for the difference or ratio of median failure times for paired observations
Authors: Jung Sin‐Ho , Su John Q.
Published: April 2007

We propose a non‐parametric method to calculate a confidence interval for the difference or ratio of two median failure times for paired observations with censoring. The new method is simple to calculate, does not involve non‐parametric density estimates, and is valid asymptotically even when the two underlying distribution functions differ in shape. The method also allows missing observations. We report numerical studies to examine the performance of the new method for practical sample sizes.
A simulation study of estimators for rates of change in longitudinal studies with attrition
Authors: Wang‐Clow Fong , Lange Mary , Laird Nan M. , Ware James H.
Published: April 2007

Many longitudinal studies and clinical trials are designed to compare rates of change over time in one or more outcome variables in several groups. Most such studies have incomplete data because some patients drop out before completing the study. The missing data may induce bias and inefficiency in naive estimates of important parameters. This paper uses Monte Carlo methods to compare the bias and efficiency of several two‐stage estimators of the effect of treatment on the mean rate of change when the missing data arise from one of four processes. We also study the validity of confidence intervals and the power of hypothesis tests based on these estimates and their standard errors. In general, the weighted least squares estimator does relatively well, as does an analysis of covariance type estimator proposed by Wu et al. The best estimates of variance components are based on complete cases or maximum likelihood.
A comparison of methods that characterize pulses in a time series
Authors: Mauger David T. , Brown Morton B. , Kushler Robert H.
Published: April 2007

There is general recognition that some glands secrete hormones primarily as a series of pulses. One can generally classify the proposed methods of pulse identification and characterization as either (i) criterion‐based, that is, they use a criterion such as a test statistic to identify a rise and/or fall in hormone level within a moving window, or (ii) model‐based, that is, they specify a statistical model for the time‐varying portion of the signal and estimate its parameters.

Using simulated data, we compare and contrast seven criterion‐based methods and three model‐based methods. The model‐based methods perform better in estimating the parameters of interest; they are most effective with the sampling rate chosen so that there are 3–5 samples taken during the half‐life of the hormone. At higher sampling rates the methods may overidentify pulses (false positives) and at lower sampling rates they may miss pulses (false negatives), both of which lead to biased estimates for the parameters of the signal.
Methodological and statistical problems in the construction of composite measurement scales: A survey of six medical and epidemiological journals
Authors: Coste Joël , Fermanian Jacques , Venot Alain
Published: April 2007

Composite measurement scales (CMS) are increasingly used in medicine to measure complex phenomena or concepts such as disease risk and severity, physical and psychological functioning and quality of life. To investigate the methodology currently used in the construction of CMS, we examined 46 studies recently published in six major medical and epidemiological journals. Important measurement properties such as measurement level, content and construct validity and reliability are often neglected. Statistical methods, particularly multivariate methods are frequently misused; verifications of model relevance and assumptions, and cross‐validations to avoid overfitting are seldom performed. We propose recommendations for the construction and the presentation of CMS, to help authors and investigators to report and choose, respectively, measurement instruments for a complex phenomenon.
Comparison of several goodness‐of‐fit tests for the kappa statistic based on exact power and coverage probability
Authors: Basu Srabashi , Basu Ayanendranath
Published: April 2007

We present a general approach based on chi‐square goodness‐of‐fit tests for performing tests of significance about the kappa statistic. We make recommendations for the particular goodness‐of fit test to use under different situations. We also compare the test statistics in terms of the generated confidence intervals about the kappa statistic.
A random‐effects regression model for meta‐analysis
Authors: Berkey C. S. , Hoaglin D. C. , Mosteller F. , Colditz G. A.
Published: April 2007

Many meta‐analyses use a random‐effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random‐effects regression approach for the synthesis of 2 × 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random‐effects regression method performs well in the context of a meta‐analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within‐study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non‐zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta‐analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta‐analysis of continuous outcomes when covariates are present.
Association models for periodontal disease progression: A comparison of methods for clustered binary data
Authors: Have Thomas R. Ten , Landis J. Richard , Weaver Susan L.
Published: April 2007

We investigate population‐averaged (PA) and cluster‐specific (CS) associations for clustered binary logistic regression in the context of a longitudinal clinical trial that investigated the association between tooth‐specific visual elastase kit results and periodontal disease progression within 26 weeks of follow‐up. We address estimation of population‐averaged logistic regression models with generalized estimating equations (GEE), and conditional likelihood (CL) and mixed effects (ME) estimation of CS logistic regression models. Of particular interest is the impact of clusters that do not provide information for conditional likelihood methods (non‐informative clusters) on inferences based upon the various methodologies. The empirical and analytical results indicate that CL methods yield smaller test statistics than ME methods when non‐informative clusters exist, and that CL estimates are less efficient than ME estimates under certain conditions. Moreover, previously reported relationships between population‐averaged and cluster‐specific parameters appear to hold for the corresponding estimates in the presence of these clusters.
Using sign score regression models to select variables in case‐control studies
Authors: O'Gorman Thomas W.
Published: April 2007

This paper evaluates the performance of four variable selection methods suitable for case‐control studies. Two of the methods are logistic regression and the rank transformed version of it which uses the ranks of the explanatory variables in place of the original observations. The third method is based on Kendall's τb correlations. I propose a fourth method, a sign score regression model to select variables. To evaluate these four methods, I generate many data sets for a case group and a control group with the use of several different distributions and covariance matrices. I evaluate the methods on their ability to select correctly the variables related to case‐control status while not selecting the unrelated variables. Using this criterion, the sign score regression method and the τb method are more effective than the other two methods with uncorrelated or weakly correlated variables. The sign score regression method is more effective than the τb method for all simulations that use normal variables and for some that use log‐normal variables. Overall, the sign score regression method is the most effective variable selection method for data sets that have low or moderate correlations between variables.
The development of the series of U.S. cancer atlases: Implications for future epidemiologic research
Authors: Mason Thomas J.
Published: April 2007

An historic perspective on the development of the National Cancer Institute's series of cancer atlases is provided. Emphasis is placed on the role which emergent questions concerning environmental determinants of cancer played in the acquisition and utilization of data resources. Studies which were fielded as a consequence of the atlases are highlighted. The legacy of the collective effort of many persons who worked on the development of the cancer atlases – the facilitation of many current epidemiologic investigations – is discussed.
Probabilistic linkage of large public health data files
Authors: Jaro Matthew A.
Published: April 2007

Probabilistic linkage technology makes it feasible and efficient to link large public health databases in a statistically justifiable manner. The problem addressed by the methodology is that of matching two files of individual data under conditions of uncertainty. Each field is subject to error which is measured by the probability that the field agrees given a record pair matches (called themprobability) and probabilities of chance agreement of its value states (called theuprobability). Fellegi and Sunter pioneered record linkage theory. Advances in methodology include use of an EM algorithm for parameter estimation, optimization of matches by means of a linear sum assignment program, and more recently, a probability model that addresses bothmanduprobabilities for all value states of a field. This provides a means for obtaining greater precision from non‐uniformly distributed fields, without the theoretical complications arising from frequency‐based matching alone. The model includes an interative parameter estimation procedure that is more robust than pre‐match estimation techniques. The methodology was originally developed and tested by the author at the U.S. Census Bureau for census undercount estimation. The more recent advances and a new generalized software system were tested and validated by linking highway crashes to Emergency Medical Service (EMS) reports and to hospital admission records for the National Highway Traffic Safety Administration (NHTSA).
On combining dose—response data from epidemiological studies by meta‐analysis
Authors: Smith S. Jay , Caudill Samue L. P. , Steinberg Karen K. , Thacker Stephen B.
Published: April 2007

Using data from a meta‐analysis of the effects of oestrogen replacement therapy on the development of breast cancer, we compared alternative methods for combining dose—response slopes from epidemiological studies. We evaluated issues related both to summarizing data from single studies and to combining results from multiple studies. Findings related to the analysis of individual dose—response studies include: (1) a method of weighing studies that gives greater influence to dose—response slopes that conform to the linear relation of relative risk to duration can lead to large differences in calculated weights as a function of non‐linearity; (2) a regression model using a variable‐intercept resulted in a mean dose—response slope that increased as much as threefold when compared with the values obtained with a zero‐intercept model. When combining results from multiple studies, we found: (1) calculating standard errors of mean dose—response slopes by methods that allow for both among‐study and within‐study variability (a random‐effects type model) gave values different from a method that assumes homogeneity and equal within‐study precision (a fixed‐effects model); (2) the random‐effects model gives mean and standard error results most similar to a bootstrap resampling method as increasing heterogeneity is observed (however, this model could give biased mean estimates compared with the bootstrap method); (3) a components‐of‐variance model compares favourably with the bootstrap and is easier to apply than the random‐effects model. Based on these findings, we recommend the use of methods which incorporate heterogeneity to guard against underestimating the standard error. However, caution is urged because bias in point estimates can occur if extreme heterogeneity is present. Two other observations affect the interpretation of data combined from multiple studies. First, inclusion into a model of quality scores assigned by blinded reviewers had little effect on the mean dose—response slope and its standard error. Second, the number of studies required to achieve desired statistical power, varies with effect size.
Meta‐analytic approaches to dose‐response relationships, with application in studies of lung cancer and exposure to environmental tobacco smoke
Authors: Tweedie R. L. , Mengersen K. L.
Published: April 2007

This paper outlines several meta‐analytic approaches to the assessment of quantal dose—response relationships; that is, to the evaluation of an increase in the level of exposure to an agent and the associated relative risk of a disease when this is investigated over a number of different studies. Analysis is developed at two levels: first, a consistent method of evaluating the dose—response relationship is applied to each study, and second, an overall picture is obtained by comparing and combining these relationships. At the first stage, for an individual study, dose—response assessment involves choices of model and appropriate tests for trend, which are influenced by such issues as dose measurement and use of the unexposed group. At the second stage, different methods for pooling results across studies must be considered. These depend on the choices made in the first stage of analysis, with additional attention paid to heterogeneity, and possible bias due to studies included in meta‐analysis. We describe these meta‐analytic approaches for three methods of evaluating dose response. The approaches are illustrated by evaluating the relationship between lung cancer and levels of exposure to environmental tobacco smoke (ETS). The strength of this relationship has been a point of debate in recent assessment of evidence for an overall carcinogenic effect of ETS exposure. We find little indication of a consistent dose response, a result explained in terms of recent models for cancer and passive smoking developed by Darby and Pike, the current meta‐analysis results of overall risk‐ratios of current studies in Tweedie and Mengersen, and misclassification models developed by the United States Environmental Protection Agency (EPA).
A dual frame design for sampling elderly minorities and persons with disabilities
Authors: Ezzati Trena M. , Hoffman Keith , Judkins David R. , Massey James T. , Moore Thomas F.
Published: April 2007

Multiple data sources are sometimes available as potential sampling frames for population surveys, and in some situations the use of a multiple frame sample design is more advantageous than using a single sampling frame. The use of multiple sampling frames, however, has variance and bias implications, as well as sampling, data collection, and logistical considerations. These issues are addressed for a proposed dual frame sampling approach in the National Health Interview Survey (NHIS). The results of an investigation of the sampling efficiencies and operational issues in supplementing the NHIS area frame sample with a sample of elderly African and Hispanic Americans and persons with disabilities selected from Social Security Administration files are presented.
Development and applications of a city‐level alcohol availability and alcohol problems database
Authors: Mackinnon David P. , Scribner Richard , Taft Katharine A.
Published: April 2007

Data on alcohol availability and problems in all cities in Los Angeles County were collected from several different sources and linked together to form a Local Alcohol Availability Database (LAAD). The two major purposes of the project are to provide a city‐level alcohol availability and alcohol‐related problems database needed by local community alcohol policy planners and to collect the data necessary for research on the relationship between these measures. The prevalence of drunk driving arrests is displayed on a map. We describe how the LAAD has been used to guide alcohol policy decisions. A fixed year and city effects regression model suggests that outlet density is positively related to several alcohol‐related problems.
Effects of the choice of age‐adjustment method on maps of death rates
Authors: Pickle Linda W. , White Andrew A.
Published: April 2007

Maps of morbidity or mortality rates, whether considered individually or as a layer in a geographic information system application, invite multiple comparisons of area rates. However, comparisons of rates across different populations require standardization of the age‐specific rates to account for differences in population age structures. The indirect standardization method, or equivalently the standardized mortality ratio (SMR), has been recommended for small areas where age‐specific rates can be quite variable. Although theoretically equivalent to directly adjusted rates under the assumption of independent age and area effects, indirect summary measures are not comparable across areas when this assumption is violated. We tested the validity of this assumption for the 10 most common causes of death in the United States during 1980–84 and examined the geographic clustering apparent when categorized death rates, adjusted by different methods, are presented as thematic maps. Although overall agreement between the methods was good (rank correlation coefficient >82 per cent for each cause), when the adjusted rates were classified into quintiles 18 per cent of the states fell into different categories depending on the method of adjustment. Using an internal standard for the indirect method reduced this discrepancy to 4·9 per cent. However, both traditional chi‐square tests and a generalized logistic spline model identified significant interactions between age and area for each cause of death, a violation of the assumption required for equivalence of the methods. Potential variation in geographic inferences is illustrated by maps of direct and indirect rates and an empirical Bayes posterior mean, which is a function of these traditionally adjusted rates. Based on these results, we recommend the direct age‐adjustment method for rate maps.
Public health surveillance for chronic conditions: A scientific basis for decisions
Authors: Thacker Stephen B. , Stroup Donna F. , Rothenberg Richard B. , Brownson Ross C.
Published: April 2007

In this paper we investigate the important contribution of multiple public health surveillance systems to policy in chronic disease control and prevention. We show that, typically, surveillance for chronic diseases relies on multiple data sources, often created for another purpose. We also define the concept of burden for chronic conditions based on data from multiple sources. An example from a state illustrates a model for combining data for use in policy development. These applications illustrate the central role of statistical methods in ensuring the appropriate use of data from multiple surveillance systems.
Differences in morbidity measures and risk factor identification using multiple data sources: The case of coronary heart disease
Authors: Madans Jennifer H. , Reuben Cynthia A. , Rothwell Sandra T. , Eberhardt Mark S.
Published: April 2007

The NHANES I Epidemiologic Followup Study contains several sources of information that can be used to define case status. Incidence rates and relative risks associated with selected, documented risk factors for heart disease were estimated using nine different case definitions. Despite wide variation in the estimates of incidence, the characteristics of the cases were remarkably similar as were the risks associated with heart disease incidence. The main difference occurred when cases were defined on the basis of death certificate information. Cases defined this way are more severe and models based on this definition result in relative risks of greater magnitude.
Combining national health interview survey datasets: Issues and approaches
Authors: Botman Steven L. , Jack Susan S.
Published: April 2007

This paper identifies issues, special problems, and approaches in preparing estimates when combining National Health Interview Survey (NHIS) datasets. Such datasets can be used to produce estimates jointly based on individual NHIS survey components. This paper illustrates several issues associated with the analysis of multiple related datasets.
Model inconsistency, illustrated by the cox proportional hazards model
Authors: Ford Ian , Norrie John , Ahmadi Susan
Published: April 2007

We consider problems involving the comparison of two or more treatments where we have the opportunity to adjust for relevant covariates either conditionally in a regression model or implicitly in repeated measures data, for example, in crossover trials. It is seen that for data arising from non‐Normal distributions there is the possibility that models adjusting for covariates and those not adjusting for covariates will be inconsistent, that is, at most one of the models can be valid. Alternatively, even if conditional and unconditional models are valid, parameters in each model may have different interpretations. We note that this presents difficulties for the specification and interpretation of the analysis. It is also clear that model validation is critical. Specific attention is paid to survival data analysed by the Cox proportional hazards model.
The analysis of repeated‐measures data on schizophrenic reaction times using mixture models
Authors: Belin Thomas R. , Rubin Donald B.
Published: April 2007

Reaction times for schizophrenic individuals in a simple visual tracking experiment can be substantially more variable than for non‐schizophrenic individuals. Current psychological theory suggests that at least some of this extra variability arises from an attentional lapse that delays some, but not all, of each schizophrenic's reaction times. Based on this theory, we pursue models in which measurements from non‐schizophrenics arise from a normal linear model with a separate mean for each individual, whereas measurements from schizophrenics arise from a mixture of (i) a component analogous to the distribution of response times for non‐schizophrenics and (ii) a mean‐shifted component. We fit four mixture models within this framework, where the distinctions between models arise from assumptions about the variance of the shifted observations and the exchangeability of schizophrenic individuals. Some of these models can be fit by maximum likelihood using the EM algorithm, and all can be fit using the ECM algorithm, where the covariance matrices associated with the parameters are calculated by the SEM and SECM algorithms, respectively. Bayesian model monitoring using posterior predictive checks is invoked to discard models that fail to reproduce certain observed features of the data and to stimulate the development of better models.
Effects of correlated and uncorrelated measurement error on linear regression and correlation in medical method comparison studies
Authors: Rifkin Robert D.
Published: April 2007

It is well known that when uncorrelated measurement error affects both variables in linear regression, there is attenuation of the correlation coefficient and regression slope. The effect of correlated measurement error, however, has received little attention. In medical method comparison studies, such error correlation results from the presence of other, unknown explanatory variables that affect the results of the new test method and the reference test method to which it is being compared. The contribution of correlated measurement error to the observed correlation coefficient can be accounted for by the expression ρt1t2 = ρ1ρ2 + ρE1E2 (1–ρ)1/2(1–ρ)1/2 where ρt1t2 is the observed correlation between tests 1 and 2, ρ1 and ρ2 are the correlation with true values for tests 1 and 2, respectively, and ρE1E2 is the correlation between the test errors. The first term describes the attenuation due to uncorrelated error, the second term describes the effect of correlated error. A positive correlation between the measurement errors reduces the attenuation of observed correlation and slope, but, when the reference method is excellent, the effect is very small. For poorer reference tests whose correlations with true values are less than 0·9, however, error correlation may result in a slope and correlation coefficient that differ importantly from the values obtained with either uncorrelated error or with no reference test error. Negatively correlated measurement errors magnify the attenuation of slope and correlation. One might suspect the presence of correlated error when the observed regression slope is close to or exceeds 1 and the reference test is known to have suboptimal reliability. This paper provides several clinical examples of potentially correlated diagnostic methods.
Spatial disease clusters: Detection and inference
Authors: Kulldorff Martin , Nagarwalla Neville
Published: April 2007

We present a new method of detection and inference for spatial clusters of a disease. To avoidad hocprocedures to test for clustering, we have a clearly defined alternative hypothesis and our test statistic is based on the likelihood ratio. The proposed test can detect clusters of any size, located anywhere in the study region. It is not restricted to clusters that conform to predefined administrative or political borders. The test can be used for spatially aggregated data as well as when exact geographic co‐ordinates are known for each individual. We illustrate the method on a data set describing the occurrence of leukaemia in Upstate New York.
Trends in mortality from melanoma in Canada and prediction of future rates
Authors: Macneill I. B. , Elwood J. M. , Miller D. , Mao Y.
Published: April 2007

A long term increase in incidence of and mortality due to malignant melanoma has been observed in all well documented white populations. The major identified cause of melanoma is sun exposure. One would expect predictions of future atmospheric ozone depletion to lead to an increase in ultraviolet radiation (UVR) and in the effects of sun exposure. We consider age‐period data for Canadian malignant malanoma mortality. We fit a multiplicative exponential/logistic (MEL) model to the data and extrapolate to AD 2010 hence yielding point estimates of future rates. We obtain total mortality forecasts by multiplying rates by population estimates. We present standard errors for forecasts. We forecast that melanoma will be a much larger burden on the health care system in the early years of the next century than it is at present. We obtain an age‐cohort model by a simple transformation of the age‐period model. Also, we obtain unconditional probabilities of death due to melanoma both for age‐period and age‐cohort models. We discuss the assumptions underlying the MEL model that suggest possible relationships between UVR and melanoma.
An alternative approach to the optimal design of an LD50 bioassay
Authors: Markus Richard A. , Frank Jess , Groshen Susan , Azen Stanley P.
Published: April 2007

In this paper we propose an alternative approach to the optimal design of an LD50 bioassay. We adopt a Bayesian approach to make use of prior information about the location and scale parameters of the tolerance distribution function to select the design parameters (number of doses, total number of animals, centre of doses, space between doses), and we adopt a frequentist approach using the Spearman—Karber statistic to estimate the LD50. We define the optimal design as the one that produces the minimum expected mean squared error E(MSE) with respect to the joint prior distribution of the parameters of the tolerance distribution. For the design parameters investigated, we found: (i) the shape of the E(MSE) is relatively smooth and continuous, the magnitude of which is influenced by the underlying tolerance distribution; (ii) the amount of prior information about the location and scale parameters independently and jointly affect the optimal design; and (iii) as the amount of prior information decreases, one requires more doses and/or animals. Finally, we show the proposed method is robust for an incorrectly assumed tolerance distribution function.
Bias of two one‐sided tests procedures in assessment of bioequivalence
Authors: Liu Jen‐Pei , Weng Chung‐Sing
Published: April 2007

The current applications of Schuirmann's two one‐sided tests procedure for the original scale ignore the variability of the least squares mean of the reference formulation when it substitutes for the unknown reference average of pharmacokinetic responses. We propose a modified two one‐sided tests procedure that takes into account the variability of the least squares mean of the reference formulation. The non‐parametric version of the modified procedure is also available. We conducted a simulation study to examine the true level of significance and empirical power of four current parametric and non‐parametric two one‐sided tests procedures under a 2 × 2 crossover design with different combinations of sample size, intrasubject variability, and correlation between the two responses from a subject. Both theoretical results and empirical evidence show that the true level of significance of the current two one‐sided tests procedures converges to 0·5 when the correlation of the two responses approaches 1. However, not only is the modified two one‐sided tests procedure a test of size α, but empirical evidence indicates it is also an unbiased test. The modified non‐parametric procedure also controls the size of the tests and is competitive even under normality assumption.