bluuebunny/crossref_metadata_2025 huggingface dataset:
Maximum likelihood estimation and likelihood ratio test for square tables with missing data
Authors: Shih W. J.
Published: April 2007

Missing data complicate the analysis of paired categorical data. This paper considers with the use of the EM algorithm, the maximum likelihood estimation and likelihood ratio test for incomplete square tables with missing data. An example involving the susceptibility of micro‐organisms to antimicrobial drugs illustrates the procedure.
The numerical approach to classification: A medical application to develop a typology for facial pain
Authors: Wastell D. G. , Gray R.
Published: April 2007

Applications of numerical taxonomy in medicine are relatively rare. We describe an application in which a new typology is developed for the classification of pain distribution in patients with temporomandibular pain dysfunction syndrome. The methodology of the numerical approach is presented and key issues are highlighted, especially relating to validation of taxonomic structure. From the dendrogram obtained by Ward's hierarchical method, five pain groups are distinguished which relate systematically to anatomy and are clinically meaningful. Cluster structure is subjected to a battery of reproducibility tests and is found to be broadly reproducible for different algorithms and sub‐samples of subjects. Finally, the new typology is shown to have heuristic value in providing fresh insight into the nature of a complex syndrome.
Comparison of quantitative diagnostic tests: Type I error, power, and sample size
Authors: Linnet Kristian
Published: April 2007

For a quantitative laboratory test the 0.975 fractile of the distribution of reference values is commonly used as a discrimination limit, and the sensitivity of the test is the proportion of diseased subjects with values exceeding this limit. A comparison of the estimates of sensitivity between two tests without taking into account the sampling variation of the discrimination limits can increase the type I error to about seven times the nominal value of 0.05. Correct statistical procedures are considered, and the power and required sample size are studied for Gaussian and log‐Gaussian distributions of diagnostic test values. The results may be useful for the planning phase of studies to evaluate quantitative diagnostic tests.
Statistical analysis of heart rates in subsequent victims of sudden infant death syndrome
Authors: Spicer C. C. , Lawrence C. J. , Southall D. P.
Published: April 2007

An analysis was made of heart rate records obtained prospectively from a group of full‐term infants. The group consisted of 16 subsequent victims of sudden infant death syndrome (SIDS) and a set of 49 controls. The power spectra were calculated for each record and this together with the birth weight, gestational age and age at recording provided the basis for a discriminant analysis. The analysis identified a periodicity of 6–8 heart beats, in addition to the known effect of low birth weight, as associated with the risk of SIDS.
On the modelling of human growth
Authors: Karlberg Johan
Published: April 2007

A new approach to modelling human linear growth from birth to maturity is presented. The model splits growth into three additive and partly superimposed components, appropriately named infancy, childhood and puberty; we refer to it as the ICP‐model for obvious reasons. A key feature of this model, compared with others, is that the components correspond with known features of the endocrinological regulation of growth and can be considered in isolation from one another.
Bias in indirectly adjusted comparisons due to taking the total study population as the reference group
Authors: Greenland Sander
Published: April 2007

It is well known that comparisons of indirectly adjusted statistics, such as standardized morbidity ratios (SMRs), can be biased when the joint effect of the study exposure and thecovariates is not multiplicative. This paper describes a related bias in SMR comparisons, one that can arise if the total study population is used as the referent. This bias can occur even if the exposure‐covariate effects are multiplicative.
Problems in the medical interpretation of overviews
Authors: Wittes Robert E.
Published: April 2007

Recently, investigators have combined formally the results of all available randomized trials testing a particular therapy to get a better estimate of the effectiveness of that treatment than any single trial can provide in isolation. It seems intuitively clear, however, that formal overviews will yield medically meaningful results only under certain defined circumstances. First, the treatments that are pooled should be similar enough that any inferences about the effect of treatment will refer to something more specific than an idealized ‘average therapy’. Second, patient selection in the pooled trials should be uniform enough that the inferences will be applicable to some defined patient population. Third, although the potential biases of excluding trials from pooling are substantial, so are the problems of including trials whose execution might be flawed in a biased manner; it is not clear what can be done about this, since biased studies probably cannot be identified in an unambiguous manner. Finally, it would seem prudent to consider the medical context during which trials were performed; it is probably not reasonable to assume that quantitative measures of treatment effect obtained by pooling studies from different eras of treatment will be an accurate reflection of what current treatment can achieve, even if everything else is held constant. For these reasons the quantitative measures of treatment effect that derive from formal overviews may have little relevance to medical decision‐making. Overviews might still be useful in indicating the general direction of a treatment effect, provided that no qualitative interactions are present. Although such interactions may seem improbable, some recent examples from the cancer literature suggest that their presence cannot be ruled out a priori.
Interpretation of results from subset analyses within overviews of randomized clinical trials
Authors: Gelber Richard D. , Goldhirsch Aron
Published: April 2007

Evaluating treatment effects within different subsets of patients is a common practice in the analysis of individual randomized clinical trials. Such analyses are limited, however, by the number of patients available. Overviews, by providing evidence based on large numbers of patients, can be useful for overcoming the difficulties of detecting therapeutic effects within subsets of patients. However, inconsistent subset definitions, misclassification of patients, and incomplete availability of patient subsets from the trials included in the overview bias the estimates of effect size.

Separate analyses ofsubsets of studiesare also possible within an overview. Studies being pooled generally differ with respect to treatments applied, control groups, patient eligibility, quality control, study conduct, and follow‐up maturity. Separate comparisons within subsets defined by these features will be misinterpreted unless confounding factors are recognized. Indirect comparisons between overviews have the same informative value as nonrandomized trials with historical controls.
Biases in the assessment of diagnostic tests
Authors: Begg Colin B.
Published: April 2007

Diagnostic tests are traditionally characterized by simple measures of efficacy such as the sensitivity and the specificity. These measures, though widely recognized and easy to understand, are subject to definitional arbitrariness. Moreover, studies constructed to estimate the sensitivity and specificity are susceptible to a variety of biases. In this paper the various potential problems are described with reference to examples from the diagnostic literature. These difficulties have implications for the design of diagnostic test evaluations, and the choice of suitable measures of test efficacy.
Models for temporal variation in cancer rates. II: Age–period–cohort models
Authors: Clayton D. , Schifflers E.
Published: April 2007

Our first paper reviewed methods for modelling variation in cancer incidence and mortality rates in terms of either period effects or cohort effects in the general multiplicative risk model. There we drew attention to the difficulty of attributing regular trends to either period or cohort influences. In this paper we turn to the more realistic problem in which neither period nor cohort effects alone lead to an adequate description of the data. We describe the age‐period‐cohort model and show how its ambiguities surrounding regular trends ‘intensify’. We recommend methods for presenting the results of analyses based upon this model which minimize the serious risk of misleading implications and critically review previous suggestions. The discussion is illustrated by an analysis of breast cancer mortality in Japan with special reference to the phenomenon of ‘Clemmesen's hook’.
Cure rate models: Power of the logrank and generalized wilcoxon tests
Authors: Halpern Jerry , Wm. Byron , Jun. Brown
Published: April 2007

This paper reports the results of simulations that compare the power of the logrank and generalized Wilcoxon tests in situations where the cure rate model is appropriate.
Modelling binary data from a three‐period cross‐over trial
Authors: Jones Byron , Kenward Michael G.
Published: April 2007

A new method of analysing binary data from a three‐treatment, three‐period cross‐over trial is described. This method is based on a log‐linear model and mirrors the analysis of continuous data. It is an extension of the method we introduced recently for the analysis of binary data from a two‐treatment, two‐period crossover trial. We illustrate our method using data from a trial which compared two analgesics and a placebo for the relief of primary dysmenorrhea.
Issues of efficiency in combining proportions of deaths from several clinical trials
Authors: Buyse Marc , Ryan Louise M.
Published: April 2007

The Mantel–Haenszel test provides a straightforward method to combine results from several clinical trials when only summary information, such as the proportion of deaths, is available for each trial. More efficient tests, such as the stratified logrank test, should be used if the survival and censoring times are known for all individuals, but in practice, the cost and effort of obtaining this information may be prohibitive. The purpose of this paper is to derive a general expression for the asymptotic relative efficiency (ARE) of the Mantel–Haenszel test with respect to the stratified logrank test, and to compute the ARE in situations which are likely to be of practical interest. The results show that under realistic assumptions about the survival distribution, losses to follow‐up and duration of accrual, the ARE frequently exceeds 80 per cent. An example is given to show the usefulness of the approach when combining proportions of deaths from several cancer clinical trials.
Some considerations in the analysis of rates of change in longitudinal studies
Authors: Palta Mari , Cook Thomas
Published: April 2007

This paper discusses and compares several estimators of mean rate of change in unbalanced longitudinal data based on a model with randomly distributed regression coefficients across individuals. The estimators are unweighted and weighted means of these coefficients. The paper also evaluates commonly used variance estimates corresponding to the estimators. Results show that in situations of very slight imbalance, the choice of method is not critical. When imbalance is substantial, however, one should weight the regression coefficients by their estimated precision. An example using data from a nutritional study on premature neonates illustrates some issues encountered in the analysis of longitudinal clinical data sets.
Non‐random sampling in human genetics: Estimation of familial correlations, model testing, and interpretation
Authors: Chakraborty Ranajit , Hanis Craig L.
Published: April 2007

By choice or necessity, human geneticists and genetic epidemiologists often design studies that involve non‐random sampling of clusters of individuals, and yet address hypotheses appropriate to the population as a whole. Failure to adjust for the non‐randomness of data often leads to biased parameter estimates and misspecification of predictive models that cause familial resemblance of traits. We develop an approach to adjust for common forms of non‐randomness in the context of estimating familial correlation with minimal distributional assumptions and discuss its implications in connection with adjustments for concomitant variables.
Evaluating the efficacy of a combination therapy
Authors: Snapinn Steven M.
Published: April 2007

Standard multiple comparison techniques do not adequately address the situation in which one must demonstrate a combination therapy's superiority to both of its components. This paper proposes several alternative tests to deal with this situation, and evaluates the tests with a Monte Carlo sampling experiment that compares the actual with the nominal significance levels of the tests. Data from a clinical trial that compares the efficacy of a combination analgesic/muscle relaxant to its components and placebo in the treatment of low back pain and spasm illustrate the tests.
Effective sample sizes for confidence intervals for survival probabilities
Authors: Dorey Frederick J. , Korn Edward L.
Published: April 2007

We examine various methods to estimate the effective sample size for construction of confidence intervals for survival probabilities. We compare the effective sample sizes of Cutler and Ederer1 and Peto et al.,2 as well as a modified Cutler‐Ederer effective sample size. We investigate the use of these effective sample sizes in the common situation of many censored observations that intervene between the time point of interest and the last death before this time. We note that there is no a priori reason to treat upper and lower confidence intervals in a symmetric fashion since censored survival data are by nature asymmetric. We recommend the use of the Cutler‐Ederer effective sample size in construction of upper confidence intervals and the Peto effective sample size in construction of lower confidence intervals. Two examples with real data demonstrate the differences between confidence intervals formed with different effective sample sizes. This study also illustrates the need for caution in the application of simulation studies to real problems.
Practical aspects in data monitoring: A brief review
Authors: Demets David L.
Published: April 2007

Monitoring interim accumulating data in a clinical trial for evidence of therapeutic benefit or toxicity is a frequent policy, usually carried out by an independent scientific committee. Repeated testing at conventional critical values can substantially inflate the type I error rate. To maintain acceptable levels, group sequential and stochastic curtailment have been developed for clinical trials. One should not view such methods as absolute rules, but as useful guides. The decision process to terminate a trial early is complex and necessitates an accounting for many factors. The Beta‐Blocker Heart Attack Trial provides an excellent example of many of these issues.
The impact of heterogeneity on the comparison of survival times
Authors: Schumacher Martin , Olschewski Manfred , Schmoor Claudia
Published: April 2007

We consider several sources of heterogeneity in a clinical trial with patients' survival time as the main response criterion: differences in prognosis which can be attributed to a latent or ignored prognostic factor; differences in treatment efficacy in subgroups of patients, and differences in treatment combinations received by the patients. The impact of these types of heterogeneity on the treatment comparison is studied assuming a proportional hazards model. It is measured by the size and power of the logrank and proportional hazards score tests and by the bias of the estimated treatment effect. We show that heterogeneity can seriously affect the treatment comparison and has to be considered during the planning stage as well as at the analysis of a clinical trial.
A transient state model for analysing ventilatory status in a randomized clinical trial
Authors: Jais Jean‐Philippe , Chastang Claude , Raphael Jean‐Claude
Published: April 2007

We designed a randomized clinical trial to determine the efficacy of plasma exchange in Guillain‐Barré syndrome, an acute neurological disease leading to paralysis in previously healthy subjects. Some patients require temporary mechanical ventilation during the course of the disease. Ventilatory status was analysed using a three‐state model with a transient state defined by use of temporary mechanical ventilation, and the probability of being mechanically ventilated modelled as a function of time since randomization. The plot of this function reflects the effect of plasma exchange on ventilatory status showing that fewer plasma‐exchanged patients than controls required ventilatory assistance and that the duration of assistance was shorter. Similar functions can be used in a more general framework when the response criterion includes a transient state.
Fluoride and dental caries: Two different statistical approaches to the same data source
Authors: Busse H. , Bergmann E. , Bergmann K.
Published: April 2007

A recent analysis of data from earlier papers on the relationship between dental caries and drinking water fluoride concentration suggested that the commonly accepted inverse relationship did not exist. Our reanalysis of those data, however, confirms the well‐known association between fluoride concentration and dental caries. It also shows that the contrary result arose misleadingly from three simultaneous methodological errors: use of a unifactorial instead of a multifactorial model; omission of or over‐aggregation of some data, and analysis of homoscedastic probits instead of heteroscedastic counts.
A comparative study of three methods for analysing longitudinal pulmonary function data
Authors: Diem John E. , Liukkonen John R.
Published: April 2007

We compare three methods of longitudinal analysis of pulmonary function data. Our data set is taken from a study of exposure to toluene diisocyanate (TDI) vapours in a new manufacturing plant. The first two methods are a two‐stage weighted regression method and maximum likelihood estimation via the EM algorithm, and these give very similar results. The third method, regression with an autoregressive error structure, was not successfully implemented, and in our view needs better documentation.
General methods for analysing repeated measures
Authors: Louis Thomas A.
Published: April 2007

We present an overview of issues and methods for analysing repeated measures of a continuous random variable. We discuss modelling the mean vector and covariance structure, statistical efficiency, regression diagnostics, and discrepancies between longitudinal and cross‐sectional methods. We illustrate key points with examples and discuss areas requiring further development.
Issues in the analysis of repeated categorical outcomes
Authors: Ware James H. , Lipsitz Stuart , Speizer Frank E.
Published: April 2007

This paper discusses statistical methods for the analysis of repeated observations of categorical variables as they might arise in longitudinal studies. Two general types of models are described:marginalmodels that give representations for the marginal distribution of response at each occasion, andtransitionalmodels that give representations for the transition probabilities between outcome states at successive occasions. The conceptual and technical differences are discussed and recent work advancing both approaches is reviewed. The two approaches are illustrated through analysis of repeated observations on interval history of the respiratory symptom ‘persistent wheeze’ in preadolescent children.
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

This 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

Suppose 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

Data 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

Models 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

This 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

Relative 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

Several 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

Traditional 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

Consider 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

This 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

Cases 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

The 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

The 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

How 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.
The extrapolation of attributable risk to new populations
Authors: Ellenberg Jonas H.
Published: April 2007

I develop a method for extrapolation of attributable risk estimated from one population, to other populations with a different rate of risk factor exposure and/or rate of outcome. The method uses the relationship between attributable risk and the product moment correlation.
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

Family 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

We 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

We 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

For 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

This 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

To 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

Statistical 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

This 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

With 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

This 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

This 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.
Bayesian analysis of case‐control studies
Authors: Marshall R. J.
Published: April 2007

A Bayesian approach to the estimation of an odds ratio from case‐control data is considered. The exact posterior density of the odds ratio and its moments are derived. A log‐normal approximation to the density is shown to be adequate for practical purposes. Mechanisms for setting prior parameters are discussed and some examples are presented.
Stopping rules and estimation problems in clinical trials
Authors: Hughes Michael D. , Pocock Stuart J.
Published: April 2007

Stopping 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.
A quantitative study of the bias in estimating the treatment effect caused by omitting a balanced covariate in survival models
Authors: Chastang Claude , Byar David , Piantadosi Steven
Published: April 2007

This paper discusses the quantitative aspects of bias in estimates of treatment effect in survival models when there is failure to adjust on balanced prognostic variables. A simple numerical example of this bias is given along with approximate formulae for its calculation in the multiplicative exponential survival model. The accuracy of the formulae is checked by simulation. In addition, approximate calculations and simulations of power loss and the effects of omitting more than one prognostic covariate are presented. The Weibull and Cox models are also examined using simulation. Study of this bias is pertinent to much applied work, and shows that the effect of omitting balanced covariates can be modest unless the variables are strongly prognostic or many in number. This work emphasizes the need for thorough comparisons of adjusted and unadjusted analyses for sensible interpretation of treatment effects.
Non‐parametric analysis of treatment—covariate interaction in the presence of censoring
Authors: Schemper M.
Published: April 2007

The demonstration of varying treatment efficacy among different subsets of patients is an important part of the analysis of clinical trials. The paper commences by clarifying the meaning of interaction and by reviewing valid procedures of analysis in the presence of interaction. Suitable descriptive measures of treatment—covariate interactions are (ratios of) hazard ratios to which generalized Patel—Hoel tests for qualitative or ordinal categorical covariates are related. Both the hazard ratios and the tests are given in a formulation for use with different scoring systems such as u‐ or e. Furthermore, the procedures are related to common k‐sample tests and thus are suitable for proportional and for converging hazards. A worked example is included.
On sample sizes to estimate the protective efficacy of a vaccine
Authors: O'neill Robert T.
Published: April 2007

To estimate vaccine protective efficacy, defined as VE = 1 — ARV/ARU where ARV is the disease attack rate in the vaccinated group and ARU is the disease attack rate in the controls, investigators have used both cohort and case‐control designs. For each design, we present a method for calculation of the sample size required to provide an approximate confidence interval for VE of predetermined width and probability of coverage. The required sample size is a function of the desired width of the confidence interval, the probability of coverage, the assumed VE, and, for cohort designs, the assumed disease attack rate in the controls, and for case‐control designs, the assumed vaccine exposure prevalence for the controls.
Beyond sensitivity, specificity and statistical independence
Authors: Jones Richard H. , McClatchey Maureen W.
Published: April 2007

Sensitivity and specificity have clear definitions when there is a single test for one disease, and the test is either positive or negative. This paper presents a unified appraoch for obtaining posterior probabilities (predictive values) when there are more than two test outcomes and/or more than one disease state. In these cases, sensitivity and specificity do not have clear definitions. Three examples from the literature demonstrate how this approach simplifies the presentation of Bayesian revision of prior probabilities. Use of proper care in data collection for the purpose of estimating conditional probabilities can avoid assumptions of statistical independence.