bluuebunny/crossref_metadata_2025_split huggingface dataset:
Some general methods for the analysis of categorical data in longitudinal studies
Authors: Landis J. Richard , Miller Michael E. , Davis Charles S. , Koch Gary G. 
Published: April 2007

AbstractThis paper is concerned with the analysis of multivariate categorical data from epidemiologic and clinical studies with longitudinal designs. An expository discussion of pertinent hypotheses for such situations is provided within the context of two relevant data sets. Appropriate large‐sample tests of these hypotheses are developed through the application of weighted least squares to generate Wald statistics. These procedures are illustrated with extensive analyses of one of these data sets.In some situations, the resulting cross‐classification of the response variables leads to extremely sparse frequency data, especially when the number of subjects is not large. For such repeated measurement designs in which a single variable is measured repeatedly over time, this paper considers the use of a generalized Mantel–Haenszel strategy for tests of marginal homogeneity (symmetry). These randomization model methods are illustrated for data in which the repeated measurement variable is reported on an ordinal scale.This paper also focuses on the available computing software to implement these methods within the version 5 release of the SAS system. The randomization model approach can be implemented within the FREQ procedure and a broad range of models and hypotheses can be investigated within the CATMOD procedure.
Analysing repeated measurements with possibly missing observations by modelling marginal distributions
Authors: Wei L. J. , Stram D. O. 
Published: April 2007

AbstractSuppose that subjects are observed repeatedly over a common set of time points with possibly time‐dependent covariates and possibly missing observations. At each time point we model the marginal distribution of the response variable and the effect of the covariates on that distribution using a class of quasi‐likelihood models studied in McCullagh and Nelder.1 No parametric model of dependence of the repeated observations of the subject is assumed. For large samples, the quasi‐likelihood estimates of the time‐specific regression coefficients over the set of predetermined time points are shown to be approximately jointly normal. This, coupled with various inference procedures, provides a global picture about the effects of the covariates on the response variable over the entire study period. A lack‐of‐fit test for testing the adequacy of the assumed quasilikelihood model is also provided. All the methods considered here are illustrated with real‐life examples.
Likelihood analysis of multi‐state models for disease incidence and mortality
Authors: Kalbfleisch J. D. , Lawless J. F. 
Published: April 2007

AbstractData related to life histories of individuals can be obtained in many different ways, and the usefulness of multi‐state models for statistical analysis is generally highly dependent on the type and nature of the data. In this paper, we focus on this, and present an approach to estimation for certain ‘difficult’ situations associated with retrospective or incomplete prospective observation. The paper begins with the identification of some problem areas in the analysis of data on life history processes. We discuss maximum likelihood estimation in some simple contexts and introduce a pseudo‐likelihood which enables the simple analysis of some sampling procedures. This approach is illustrated on standard retrospective and case‐cohort designs.
Familial aggregation in the presence of temporal trends
Authors: Province M. A. , Rao D. C. 
Published: April 2007

AbstractModels for assessing temporal trends in familial aggregation are described for both cross‐sectional and longitudinal family data. Simultaneous linear structural equations on latent variables are used to model the dependence among family members. The coefficients of the equations are assumed to be parametric functions of time, so that quite complex temporal trends in familial aggregations can be accommodated. Variable family sizes and missing data values pose no problem as the parameters of the models are estimated via maximum likelihood techniques. One of the models is applied to systolic blood pressure data in 542 Japanese–American nuclear families. The results indicate limited evidence for temporal variation in the genetic expression, but that there is substantial temporal variation in environmental influences, which appear to peak at middle age.
Effect of cigarette smoking in epidemiological studies of lung cancer
Authors: Whittemore Alice S. 
Published: April 2007

AbstractThis paper describes a method for adjusting the analysis of occupational/environmental lung cancer risks for the effects of cigarette smoking in cohort and case‐control studies. The method uses a function that relates an individual's death rate to his age and cigarette smoking history. Two such functions are examined. The first depends on total packs of cigarettes smoked and age. The second, based on the multistage theory of carcinogenesis, depends on age, age at start of smoking, and subsequent smoking rates. The lung cancer rates predicted by these two functions are compared to those observed in cohort studies of male British physicians and U.S. veterans, and in a case‐control study of non‐Hispanic white men in New Mexico. Neither of the cohort data sets distinguished the fit of the two functions. The New Mexico data were fit better by the second function, though both functions overpredicted death rates among ex‐smokers. Each function explained substantially more variation in the New Mexico data than did any of several logistic regression models involving categorical variables for age and smoking.
Aspects of the use of relative risk models in the design and analysis of cohort studies and prevention trials
Authors: Prentice Ross L. , Self Steven G. 
Published: April 2007

AbstractRelative risk regression methods provide a unifying and powerful approach to a range of problems in the design and analysis of cohort studies and prevention trials. Standard partial likelihood‐based estimation procedures do not, however, encompass several features that are important in such contexts. Specifically, one may wish to relate disease rates marginally to ‘recent’ risk factor measurements, whereas a partial likelihood approach requires one to condition on an accumulating risk factor history. Secondly, risk factor values may be ascertained with considerable measurement error, thereby requiring specialized procedures to estimate relative risk parameters. Thirdly, analysis of raw materials to obtain desired covariate (risk factor) histories may involve considerable expense if carried out for the entire cohort. Case‐control and case‐cohort sampling procedures can avoid much of this expense, but once again partial likelihood estimation procedures require generalization. Such generalizations are described herein.
Analysing changes in the presence of informative right censoring caused by death and withdrawal
Authors: Wu Margaret C. , Bailey Kent 
Published: April 2007

AbstractSeveral approaches for estimating and comparing the rates of change of a continuous variable in two treatment groups in the presence of informative right censoring are reviewed and compared. The comparisons are made under different models for the censoring probabilities and various types of treatment effects. Some recommendations are discussed regarding the application of these approaches to the different settings.
A model‐based approach to characterize the incubation period of paediatric transfusion‐associated acquired immunodeficiency syndrome
Authors: Lui Kung‐Jong , Peterman Thomas A. , Lawrence Dale N. , Allen James R. 
Published: April 2007

AbstractTraditional statistics, such as the average, currently provide a misleading statistical description of the incubation periods of paediatric transfusion‐associated acquired immunodeficiency syndrome (TA‐AIDS). Two types of length‐biased sampling, left censoring and right censoring, occur in the 37 cases diagnosed before 1986 and reported to the Centers for Disease Control by 15 September 1986. To correct for these problems, we propose a truncated Weibull distribution with use of a maximum likelihood technique. This approach suggests that the mean incubation period of paediatric TA‐AIDS cases is 2.4 years with a 90 per cent confidence interval ranging from 1.5 years to 7.2 years. The mode is 1.25 years. These estimates are much shorter than those recently published for adults, therefore the success of TA‐AIDS prevention strategies may be seen sooner in the paediatric age group.
Correcting for selection bias in estimation of within‐individual variance
Authors: Wilson P. David 
Published: April 2007

AbstractConsider a variable whose expected value distributes among individuals in a population, and which also has an important component of within‐individual variance. In a screening study that involves repeated observations only for those individuals whose initial observation exceeds an arbitrary cutoff point, the usual estimator of within‐individual variance is biased. Assuming normality and independence, this note gives the derivation of the expected value of the estimator and uses it to obtain an unbiased estimator. The results generalize to the bivariate case that involves selection on only one variable of the pair. A companion paper1 provides an example with use of blood pressure.
Multinomial regression models based on continuation ratios
Authors: Cox Christopher 
Published: April 2007

AbstractThis paper concerns continuation ratio models for multinomial responses. These are conditional probabilities used in logit models to define the dependence of the multinomial proportions on explanatory variables and unknown parameters. A distinctive feature of these models is that if one models the various continuation ratios separately, then the resulting estimates and test statistics are asymptotically independent. This allows the partitioning of likelihood ratio statistics and the search for effects in specific categories of an ordinal response variable. Models that use the same parameters for different continuation ratios are suitable for estimating more global differences. The fitting of these models to actual data is illustrated, including an example from a pharmaceutical study. The results show that different models are suitable for modelling complementary sorts of differences between multinomial response distributions.
Density equalized map projections: A method for analysing clustering around a fixed point
Authors: Schulman J. , Selvin S. , Merrill D. W. 
Published: April 2007

AbstractCases plotted on a geopolitical map entail difficulties in interpretation and analysis because of variable population density in the study area. Density equalized map projections (DEMPs) eliminate the distribution of the resident population as an interfering influence by transforming map area to be proportional to population. This paper discusses a transformation algorithm, its properties, and develops statistical methods to detect clustering of cases around a fixed point for data plotted on DEMPs. We suggest two numeric methods where exact solutions are too complicated or do not exist. Finally, we illustrate these methods using data from Denver and Jefferson counties in Colorado to investigate whether lung cancer and leukaemia incidence patterns are associated with plutonium exposure from the Rocky Flats plant site.
Descriptive statistical analyses of serial dilution data
Authors: Hamilton Martin A. , Rinaldi Michael G. 
Published: April 2007

AbstractThe serial dilution assay (for example, an in vitro antimicrobic susceptibility test or a serum antibody titer assay) is an important technique in biomedical research. The structure of the experiment forces grouping of the threshold concentrations into intervals. Statistical methods to analyse threshold concentrations from a batch of serial dilution assays should account for the grouping of the data. Many traditional subject area analyses, however, ignore the grouped nature of the data. This article discusses the discrepancy between the traditional subject area approach and the usual statistical approach for analysing such grouped data.
Smoothing methods for epidemiologic analysis
Authors: Segal Mark R. , Weiss Scott T. , Speizer Frank E. , Tager Ira B. 
Published: April 2007

AbstractThe development of techniques for fitting non‐parametric smooth curves has resulted in less restrictive regression models. We discuss the ideas underlying such smoothing algorithms, develop their application to epidemiologic studies and address specific issues, such as coping with correlated errors. An example illustrates a particular smoothing approach, as applied to pulmonary function data. The method provides new insight into the effect of smoking on pulmonary function. The discussion offers some qualitative comparisons between smoothing methods and conventional linear models.
Multistate models in survival analysis: A study of nephropathy and mortality in diabetes
Authors: Andersen Per Kragh 
Published: April 2007

AbstractHow an analysis of a multistate model for survival data may compare with an ordinary survival analysis is discussed. A study of mortality and incidence of nephropathy in insulin dependent diabetes is used as illustration. Proportional hazards regression models with time‐dependent covariates and regression models for relative mortality are used for the transition intensities. Alternative basic time scales are also considered.
Improving the aggregate performance of psychiatric diagnostic methods when not all subjects receive the standard test
Authors: Lavori Philip W. , Keller Martin B. 
Published: April 2007

AbstractFamily studies of disease incidence often include some subjects who receive a diagnostic evaluation less accurate than that obtained with a standard method. This is particularly true of family studies of mental illness, where the standard is a consensus diagnosis based on both direct interview and corroborating family history from an informant in the family. Family members who are not interviewed have diagnoses based on history alone. Interviewed and uninterviewed relatives differ in several factors that influence the probability of disease, so that interview is not independent of disease. Thus, one cannot use the interview data to estimate the overall rate of illness. Family history‐based illness rates may substantially underestimate true rates, so the observed rate in uninterviewed subjects is also a biased estimate. We discuss several models to reduce the bias in estimation of incidence rates. We propose explicit modelling and imputation as alternatives to the implicit assumptions that constitute the basis of the methods in current use. A clinical example involving 4806 relatives of probands with major affective illness illustrates the statistical issues.
Instrumental variables in the evaluation of diagnostic test procedures when the true disease state is unknown
Authors: Nagelkerke N. J. D. , Fidler V. , Buwalda M. 
Published: April 2007

AbstractWe explore the estimation of sensitivity and specificity of diagnostic tests when the true disease state is unknown. Instrumental variables which subdivide the patient population are used. A logistic model, relating these instrumental variables to the (unknown) true disease state is proposed. It is shown that this procedure allows the goodness‐of‐fit to the resulting model to be tested.
Exact versus asymptotic analysis for a matched case‐control study
Authors: Hirji Karim F. , Elashoff Robert M. , Moore Dan H. , Bennett Deborah E. 
Published: April 2007

AbstractWe compare exact and asymptotic methods for variable selection in matched case‐control studies. Data from a study of melanoma among the employees of the Lawrence Livermore National Laboratory illustrate the comparisons. Relative to large sample methods, the exact method almost always yielded larger p‐values. The differences in p‐values became more pronounced with inclusion of more variables in the logistic model. Thus, when the sample size is not large, and there are many covariates under study, use of the exact method tends to select more parsimonious models and avoids overfit of the data.
A computer enhanced runs test for smoothed data
Authors: Katz Barry P. 
Published: April 2007

AbstractFor a series of data points that follow a pattern due to natural ordering, I develop a modified runs test for continuous data to detect such a pattern. The test statistic is the number of runs in the series after having smoothed the data with use of a moving average. The testing procedure is based on permutations of the original data. My simulations indicate that the modified test is generally more powerful than the usual runs test.
The analysis of event history data: A review of progress and outstanding problems
Authors: Clayton David 
Published: April 2007

AbstractThis paper reviews methods for the analysis of event history data by both parametric (exponential/Poisson) and semi‐parametric methods. It identifies a need for software for handling the data structures of complex event histories and shows that, with such software, most Markov and semi‐Markov models of event history data may be dealt with in the framework of generalized linear models. Finally, the emergent ‘frailty’ models for associated risks are discussed together with their implications for statistical software.
A note on graphical presentation of estimated odds ratios from several clinical trials
Authors: Galbraith R. F. 
Published: April 2007

AbstractTo display a number of estimates of a parameter obtained from different studies it is common practice to plot a sequence of confidence intervals. This can be useful but is often unsatisfactory. An alternative display is suggested which represents intervals as points on a bivariate graph, and which has advantages. When the data are estimates of odds ratios from studies with a binary response, it is argued that for either type of plot, a log scale should be used rather than a linear scale.
Methods for assessing whether change depends on initial value
Authors: Hayes R. J. 
Published: April 2007

AbstractStatistical analysis of whether the change in a variable depends on its initial value, in clinical trials and other studies, is complicated by the phenomenon of regression to the mean. We review this problem and examine some approaches for handling it. MacGregor's log‐log plot fails to correct for the regression effect, while Oldham's method of plotting the change against the average of initial and final values is shown to give misleading results when the effect of treatment varies between subjects, or when subjects are selected for study if their initial observations fall above or below a specified cut‐off point. These results are illustrated by computer simulations. We prefer the use of Blomqvist's method of correcting the association between change and initial value to allow for the regression effect.
Two‐process models for discrete‐time serial categorical response
Authors: Dersimonian Rebecca , Baker Stuart G. 
Published: April 2007

AbstractThis paper proposes a two‐process log‐linear model for analysis of polychotomous response data generated on study subjects assessed at successive discrete intervals. Response type at each discrete time may be either a transient response or a cause‐specific failure. We view outcome of transient response as fundamentally different from outcome of failure, and, in a competing risk framework, we motivate a separate model for each: one to describe the process for transitions to transient response states and the other to describe the process for transitions to absorbing failure states. We maximize the likelihood for each model separately with use of existing software for iterative proportional fitting.
Comparing the effects of different durations of the same therapy
Authors: Freedman L. S. , De Stavola B. L. 
Published: April 2007

AbstractWith clinical trials that compare different durations of the same therapy, particular problems arise when the main response criterion is time to an event, such as death or relapse. With such trials, we first discuss the appropriate time(s) for randomization in the context of both the two‐treatment trial and the three‐treatment trial, where one of the treatments is a control. We then propose a proportional hazards model with time‐varying treatment effects for analysis of a three‐treatment trial in which randomization occurs on entry to the study. We illustrate the methodology with a trial of intravesical chemotherapy for superficial bladder cancer.
The effect of non‐differential exposure misclassification on estimates of the attributable and prevented fraction
Authors: Hsieh Chung‐Cheng , Walter Stephen D. 
Published: April 2007

AbstractThis paper considers the effect of non‐differential exposure misclassification on the population attributable fraction and the population prevented fraction as a function of the sensitivity and specificity of the exposure classification, the true relative risk, and the true prevalence of the exposure. Given a certain set of sensitivity, specificity, and prevalence of the exposure, the apparent attributable fraction is a constant proportion of the true attributable fraction regardless of the true relative risk. This observation does not hold for the apparent prevented fraction and the apparent relative risk, both of which vary with the true relative risk. For both the attributable and the prevented fraction, the sensitivity of the exposure classification has a greater influence on the magnitude of the bias than the specificity; also, the higher the prevalence of the exposure, the larger is the bias caused by the imperfect exposure classification.
Sample size formulae for intervention studies with the cluster as unit of randomization
Authors: Hsieh F. Y. 
Published: April 2007

AbstractThis paper presents sample size formulae for both continuous and dichotomous endpoints obtained from intervention studies that use the cluster as the unit of randomization. The formulae provide the required number of clusters or the required number of individuals per cluster when the other number is given. The proposed formulae derive from Student's t‐test with use of cluster summary measures and a variance that consists of within and between cluster components. Power contours are provided to help in the design of intervention studies that use cluster randomization. Sample size formulae for designs with and without stratification of clusters appear separately.
Stopping rules and estimation problems in clinical trials
Authors: Hughes Michael D. , Pocock Stuart J. 
Published: April 2007

AbstractStopping rules in clinical trials can lead to bias in point estimation of the magnitude of treatment difference. A simulation exercise, based on estimation of the risk ratio in a typical post‐myocardial infarction trial, examines the nature of this exaggeration of treatment effect under various group sequential plans and also under continuous naive monitoring for statistical significance. For a fixed treatment effect the median bias in group sequential design is small, but it is greatest for effects that the trial has reasonable power to detect. Bias is evidently greater in trials that stop early and is dramatic under naive monitoring for significance. Group sequential plans lead to a multimodal sampling distribution of treatment effect, which poses problems for incorporating their estimates into meta‐analyses. By simulating a population of trials with treatment effects modelled by an underlying distribution of true risk ratios, a Bayesian method is proposed for assessing the plausible range of true treatment effect for any trial based on interim results. This approach is particularly useful for producing shrinkage of the unexpectedly large and imprecise observed treatment effects that arise in clinical trials that stop early. Its implications for trial design are discussed.