bluuebunny/crossref_metadata_2025_split huggingface dataset:
An estimating equation approach for the analysis of case‐control studies with exposure measured at several levels
Authors: Sato Tosiya 
Published: April 2007

AbstractLiang gave an extension of the Mantel‐Haenszel estimating procedure for a common odds ratio to logistic regression models. It is applicable to case‐control studies with multiple exposure levels, which yield K 2 × J tables. This paper provides variance and covariance estimators, which are consistent in both sparse‐data and large‐strata, for Liang's estimating functions in the K 2 × J tables case, and proposes an approximate confidence interval method for the common odds ratios.
The analysis of failure time data in crossover studies
Authors: France Lesley A. , Lewis John A. , Kay Richard 
Published: April 2007

AbstractClinical trials of new drugs in the treatment of angina pectoris frequently make use of exercise tests to evaluate efficacy. The crossover design is often employed. The methods commonly used to analyse the various exercise times, for example, ‘time to pain’, are insensitive and potentially biased by the manner in which they deal with the censored nature of the data. Survival analysis can be adapted for use in crossover trials, both in a relatively simple way, and also through the full power of the Cox model. This is considerably more sensitive and not subject to the same bias. This methodology leads to the use of median survival times to illustrate treatment effects and this provides a practical interpretation of clinical relevance. The estimation of median survival times in crossover trials poses some special problems. The methodology is illustrated throughout by means of a specific two‐period example in which atenolol was compared with the combination of atenolol and nifedipine. The three‐period design is also briefly discussed.
Recovery of inter‐block information in cross‐over trials
Authors: Chi Eric M. 
Published: April 2007

AbstractFor cross‐over designs, we show that one can recover information on direct and/or residual treatment effects from an inter‐block (patients as blocks) analysis as is done in an incomplete block design.1 By combining the intra‐ and inter‐block estimates, we obtain the generalized least squares estimate. In analysing cross‐over trials, we recommended this approach provided the between‐patient variance component can be estimated efficiently.
A quasi‐exact test for comparing two binomial proportions
Authors: Hirji Karim F. , Tan Shu‐Jane , Elashoff Robert M. 
Published: April 2007

AbstractThe use of the Fisher exact test for comparing two independent binomial proportions has spawned an extensive controversy in the statistical literature. Many critics have faulted this test for being highly conservative. Partly in response to such criticism, some statisticians have suggested the use of a modified, non‐randomized version of this test, namely the mid‐P‐value test. This paper examines the actual type I error rates of this test. For both one‐sided and two‐sided tests, and for a wide range of sample sizes, we show that the actual levels of significance of the mid‐P‐test tend to be closer to the nominal level as compared with various classical tests. The computational effort required for the mid‐P‐test is no more than that needed for the Fisher exact test. Further, the basis for its modification is a natural adjustment for discreteness; thus the test easily generalizes to r × c contingency tables and other discrete data problems.
Adjustment for non‐differential misclassification error in the generalized linear model
Authors: Liu Xinhua , Liang Kung‐Yee 
Published: April 2007

AbstractIt is well known that estimates of association between an outcome variable and a set of categorical covariates, some of which are measured with misclassification, tend to be biased upon application of the usual methods of estimation that ignore the classification error. We propose a method to adjust for misclassification in covariates when one applies the generalized linear model. In the case where one can observe some true covariates only through surrogates, we combine a latent class analysis with the approach to incorporate multiple surrogates into the model. We include discussion on the efficacy of repeated measurements which one can view as a special case of multiple surrogates with identical distribution. We provide two examples to demonstrate the applicability of the method and the efficacy of multiple replicates for a covariate subject to misclassification in a regression framework.
An empirical bayes formulation of cohort models in cancer epidemiology
Authors: Desouza Cynthia M. 
Published: April 2007

AbstractThis paper concerns the incidence rates of malignant skin melanoma for several age‐sex groups and time periods in three geographic regions, uses a method of cohort analysis and employs a two‐stage random effects model. The first stage entails the assumption that the within‐region variation in the frequency of disease incidence for a fixed age‐sex‐cohort group has a Poisson distribution with mean proportional to the population at risk. The second stage, after adjusting for age and sex, entails the assumption that the between‐region geographic variation in the logarithm of the true incidence rate has a prior distribution with parameters estimated by the method of maximum likelihood. After adjusting for age effects, we estimate random geographic‐specific cohort effects for each sex with use of an empirical Bayes method and compare the results with the usual multiplicative Poisson model that assumes fixed geographic‐specific cohort effects for each sex. This comparison shows that the method presented here provides more stable estimates of geographic‐specific cohort effects, and in addition the random effects model describes these data more adequately.
Estimating departure from normality
Authors: Royston Patrick 
Published: April 2007

AbstractDeparture of a sample from a normal distribution should be assessed by a quantity that is meaningful in terms of the data, rather than merely by the P‐value from a test statistic. Indices of departure based on the Shapiro‐Francia W′ and the Shapiro‐Wilk W statistics are derived, and shown to have a natural interpretation in relation to the normal probability plot. A new diagnostic plot is proposed. An example is given which shows the relationship between one of the new indices and errors in calculated reference ranges due to non‐normality of the data.
Estimating kappa from binocular data
Authors: Oden Neal L. 
Published: April 2007

AbstractA common error in statistical analysis of ophthalmic data is the lack of accounting for the positive correlation generally present between observations made in fellow eyes. The alternative of data analysis from only one eye in each patient may lead to loss of power and unrealistically large confidence intervals. This paper discusses a method to estimate kappa, a measure of agreement between two graders, when both graders rate the same set of pairs of eyes. The method assumes that the true left‐eye and right‐eye kappa values are equal and makes use of the correlated binocular data to estimate confidence intervals for the common kappa. Simulations show that the new estimators are better than the estimator based on only one eye; new confidence intervals had the correct coverage probability, but were usually only about 70 per cent as wide as single‐eye intervals. The general methodology described here applies to analysis of grader agreement in rating other paired body structures.
Contradictory correlations between derived scales
Authors: Thompson M. J. , Hand D. J. , Everitt B. S. 
Published: April 2007

AbstractIn a clinical trial one scale of pain relief is scored backwards relative to another (high on one corresponding to low on the other), with a consequent large negative correlation. But two derived scales of total pain, obtained by multiplying average pain relief on each scale by duration of pain (common to both pain relief measurements) gave an almost zero correlation. This apparent contradiction is explained by the inverse relationship between the pain relief scales and the large differences in duration of pain experienced by the patients.
A comparative phase II clinical trials procedure for choosing the best of three treatments
Authors: Palmer Christopher R. 
Published: April 2007

AbstractIn some clinical trials one can employ adaptive designs advantageously, although in practice such techniques are rarely used, in part due to their inherent complexity. A simple and practicable decision‐theoretic approach for the case of three treatments with binary responses is considered, using equal allocation to remaining treatments and, once eliminated, a treatment cannot be re‐employed. Having specified the overall number of patients treated within and beyond the comparative stages of the trial, the goal is to maximize the expected total number of those successfully treated. Investigation of the method involves a computer program that can handle arbitrarily large numbers of patients. It is shown empirically that the decision procedure behaves only marginally worse than if the truly superior treatment had been known and had been given to all patients. Implementation of the method uses a minimax approach that removes dependence on prior parameters. Primarily an identification procedure, one advantage of this approach over traditional hypothesis testing methods is the potential to detect small improvements in treatment efficacy. The intended application is to assist in treatment selection during phase II trials, especially with rapid responses and when the disease involved is serious enough that design‐motivating ethical considerations become paramount.
Using provisional diagnoses in monitoring a clinical trial
Authors: Karon John M. 
Published: April 2007

AbstractIn clinical trials there is delay between the occurrence of an event and the recording of that event as an end point in the trial data base. Delays are especially likely if events are reviewed carefully to determine whether diagnostic criteria for end points are satisfied. As a result, the value of a a statistic used to evaluate efficacy during a trial may differ from the value of that statistic based on the true end point status of all events which have already occurred. Simulating the process causing delays can be useful in evaluating interim efficacy data by providing a quantitative estimate of the uncertainty in a monitoring statistic. These ideas are illustrated using data from the Lipid Research Clinics Coronary Primary Prevention Trial.
Crossover trials, degrees of freedom, the carryover problem and its dual
Authors: Senn S. J. , Hildebrand H. 
Published: April 2007

AbstractThe problem of carryover in crossover trials has received a great deal of attention in the statistical literature. Carryover is just one form of period by treatment interaction; yet a parallel problem of patient by treatment interaction, which may be regarded as dual to that of carryover, has received little attention. We suggest that the phenomenon of patient by treatment interaction requires a repeated measures approach to the analysis of crossover trials. A simple solution using predefined contrasts is presented and illustrated by example.
Multivariate probit analysis: A neglected procedure in medical statistics
Authors: Lesaffre E. , Molenberghs G. 
Published: April 2007

AbstractThe multivariate probit model is designed to regress a vector of correlated quantal variables on a mixture of continuous and discrete predictors. Various applications can be found in the biological, economical and psychosociological literature, but the method is not yet widely used in medical applications. We reintroduce this model thereby showing its usefulness in medical problems. Software for this model is, however, not widely available. We have written a PC program to select predictors and estimate parameters in the multivariate probit framework. The performance and characteristics of the program are briefly illustrated.
Pooling controls from different studies
Authors: Raghunathan T. E. 
Published: April 2007

AbstractThis paper develops a method for estimating the odds ratio in a case‐control study that ‘borrows’ strength from the controls in similar previous studies. We assume that the observations within each stratum in the current study are generated from a pair of independent binomial distributions. We assume that the observations on the controls, again within each stratum and from the previous studies, are independent binomial random variables. We use these controls to construct a prior distribution for the stratum specific log‐odds for the controls in the current study which we then combine with the likelihood from the current study to arrive at the posterior distribution of the target quantities of interest, the adjusted or stratum specific log‐odds ratios. We assume a diffuse prior distribution for the stratum specific log‐odds ratio. A simulation study investigates the frequency or repeated sampling properties of the inference based upon the posterior distribution of the log‐odds ratio so constructed.
Modelling hearing thresholds in the elderly
Authors: Morrell Christopher H. , Brant Larry J. 
Published: April 2007

AbstractThis paper concerns a linear mixed‐effects repeated measures model in the analysis of a large data set with over 17,000 observations in a longitudinal study of pure‐tone hearing perception in the elderly. The repeated measurements are described by fixed and random components in the model. The fixed effects include the age at entry, time of follow‐up, a quadratic component in natural logarithm of frequency, a component to allow for participants with hearing impairments, as well as interaction terms between age and frequency and between impairment and frequency. The random factors include a term for subject, a time component and a frequency component. The analysis shows that hearing impaired individuals have similar patterns of hearing loss over time but, on average, have higher hearing thresholds than normal individuals. Estimation of the random effects in the model by restricted maximum likelihood (REML) using the Newton‐Raphson method made possible the analysis of this large data set with speed and efficiency.
Radiation dose and leukaemia risk: General relative risk techniques for dose‐response models in a matched case‐control study
Authors: Blettner Maria , Boice John D. 
Published: April 2007

AbstractGeneralized relative risk functions were used to model radiation dose‐response information from a large matched case‐control study of leukaemia occurring after treatment for cervical cancer. Models suggested by radiobiological theory were investigated and compared to standard analyses of categorical dose‐response to the linear model. Local radiation doses to each of fourteen bone marrow compartements for each patient were incorporated into the models, and the corresponding risks were summed. Conditional maximum likelihood methods were used to estimate risk parameters. Unique features of the analysis include modelling both induction and reduction of risk as a function of radiation dose absorbed by different parts of the body within individuals. Detailed statistical aspects of these analyses are presented and discussed.
AX one degree of freedom nominal association model for testing independence in two‐way contingency tables
Authors: Davis Charles S. 
Published: April 2007

AbstractIn testing the independence of the row and column variables in a two‐way contingency table, the standard Pearson and likelihood ratio chi‐square statistics often have low power, especially as the dimensions of the table increase. In this paper, one degree of freedom tests of independence based on likelihood ratio and score statistics from an association model for tables with nominal row and column classifications are described. The score statistic is especially easy to use, since it can be expressed in closed form, is simple to compute, and has size and power properties which are only slightly inferior to those of the more complicated likelihood ratio statistic.
Estimation of vaccine efficacy in outbreaks of acute infectious diseases
Authors: Haber Michael , Longini Ira M. , Halloran M. Elizabeth 
Published: April 2007

AbstractIn a previous paper we defined the efficacy of a vaccine as 1 ‐ β1/β1, where β0 is the instantaneous probability of transmission of infection to an unvaccinated person exposed to a single infectious person, and β1 is similarly defined for a vaccinated person. We showed that under the conditions of an outbreak of an acute, directly transmitted infectious disease in a homogeneous and randomly mixing population, an estimate of this measure of vaccine efficacy is 1 ‐ [ln(1 ‐ A1)/ln(1 ‐ A0)], where A0 and A1 are the observed final attack rates among unvaccinated and vaccinated persons, respectively. In the present work we present an approximation for the standard error of this estimator, accounting for both the sampling and process variation. We extend the results of our previous paper to a stratified population, where the strata correspond to different levels of susceptibility and may have different vaccination coverages. We also consider populations that consist of small units (for example, households) where individuals mix primarily in these units. In this case, definition of vaccine efficacy is in terms of the within‐unit transmission probabilities and is estimable by using transmission models for infectious diseases. We apply the estimation methods described above to data from influenza and measles outbreaks. We also examine, via a stochastic simulation study, the robustness of the vaccine efficacy estimators under various population structures and mixing patterns.
Estimating the effect of the run‐in on the power of the physicians' health study
Authors: Lang Janet M. , Buring Julie E. , Rosner Bernard , Cook Nancy , Hennekens Charles H. 
Published: April 2007

AbstractIn the Physicians' Health Study, a randomized, placebo‐controlled, double‐blind trial of aspirin in the reduction of cardiovascular mortality and beta‐carotene in decreasing cancer incidence, 33,223 subjects were eligible and willing to enter the trial. Instead of randomizing this group immediately, all participants received identical calendar packs that contained active aspirin and beta‐carotene placebo. Following an 18‐week run‐in, only 22,071 subjects who remained eligible and willing and had taken at least 2/3 of their pills were randomized. We estimated the effect of the run‐in as follows: pill taking compliance increased 20‐41 per cent; sample size decreased 34 per cent; duration of follow‐up decreased 7 per cent which resulted in a 7 per cent decrease in the expected event rate for the placebo group. To estimate these changes, we made assumptions about compliance and outcome risk for those excluded by the run‐in. Our conclusion, however, about the net effect of the run‐in on the power of the study remains constant across variations in a number of those assumptions. The power with the run‐in, with 22,071 good compliers was typically higher, and never more than negligibly lower, than the power without the run‐in, with 33,223 good and poor compliers. In addition, savings from enrolling 11,152 fewer subjects in the trial resulted from the use of the run‐in.
Methods for dose finding studies in cancer clinical trials: A review and results of a monte carlo study
Authors: O'Quigley John , Chevret Sylvie 
Published: April 2007

AbstractWe discuss some of the statistical approaches to the design and analysis of phase I clinical trials in cancer. An attempt is made to identify the issues, particular to this type of trial, that should be addressed by an appropriate methodology. A brief review of schemes currently in use is provided together with our views of the extent to which any particular scheme addresses the main issues. Some simulations are provided together with graphical illustration of the operating characteristics of the particular methods. It appears that the continual reassessment method is preferable to other contending schemes.
A general parametric approach to the meta‐analysis of randomized clinical trials
Authors: Whitehead Anne , Whitehead John 
Published: April 2007

AbstractMeta‐analysis provides a systematic and quantitative approach to the summary of results from randomized studies. Whilst many authors have published actual meta‐analyses concerning specific therapeutic questions, less has been published about comprehensive methodology. This article presents a general parametric approach, which utilizes efficient score statistics and Fisher's information, and relates this to different methods suggested by previous authors. Normally distributed, binary, ordinal and survival data are considered. Both the fixed effects and random effects model for treatments are described.
Multivariate analysis of spect images with illustrations in Alzheimer's disease
Authors: McCrory S. J. , Ford I. 
Published: April 2007

AbstractModern neuroimaging techniques such as positron emission tomography and single photon emission computed tomography (SPECT) often generate datasets consisting of a large number of variables measured on a small number of subjects. In addition, the data contain large ‘subject effects’ which must be adjusted for in any statistical analysis. This paper illustrates the dangers inherent in naive univariate analysis and proceeds to demonstrate the application of multivariate methods such as HotellingsT2‐test, canonical correlation analysis and discriminant analysis. The issues and statistical methods are illustrated using a dataset from a SPECT study of groups of normal control subjects and subjects with a clinical diagnosis of Alzheimer's disease.
Methods of adjustment for estimating the attributable risk in case‐control studies: A review
Authors: Benichou Jacques 
Published: April 2007

AbstractIn the 1980's, progress was made in adjusting estimates of the attributable risk (AR) for confounding factors and in calculating associated confidence intervals. In this paper, methods of adjustment for estimation of the AR in case‐control studies are reviewed. The limitations and problems associated with two methods based on stratification, the weighted‐sum approach and the Mantel‐Haenszel approach, are discussed. They include small‐sample bias with the weighted‐sum approach and the difficulty of taking interaction into account with the Mantel‐Haenszel approach. A third method based on logistic regression is reviewed. It is argued that this latter method has the greatest generality and flexibility, and includes the two other approaches as special cases. Throughout the paper, an example of a case‐control study of oesophageal cancer illustrates the use of the methods described.
A bibliography and comments on the use of statistical models in epidemiology in the 1980s
Authors: Gail Mitchell H. 
Published: April 2007

AbstractThis paper reviews developments in statistical modelling in epidemiology in the 1980's, with emphasis on cohort and case‐control studies. The central roles of the logistic and proportional hazard models are highlighted, and it is shown how these models lead to a deeper understanding of classical designs and methods of analysis as well as to efficient new designs and analytical procedures. The important area of model misspecification is discussed, including the problems of omitted latent structure, mis‐modelling of available measurements, missing data and errors in measurements. Various designs motivated by the logistic model are illustrated numerically, and designs based on the proportional hazards model are discussed, as are papers on sample size determination. There are brief introductions to the literature on other topics, including attributable risk, disease clustering, family studies and genetics, analysis of disease incidence data, infectious disease, longitudinal data, screening and miscellaneous related topics in statistics. An extensive bibliography is indexed according to the outline of the paper.