bluuebunny/crossref_metadata_2025 huggingface dataset:
Cohort versus cross‐sectional design in large field trials: Precision, sample size, and a unifying model
Authors: Feldman Henry A. , McKinlay Sonja M.
Published: April 2007

In planning large longitudinal field trials, one is often faced with a choice between a cohort design and a cross‐sectional design, with attendant issues of precision, sample size, and bias. To provide a practical method for assessing these trade‐offs quantitatively, we present a unifying statistical model that embraces both designs as special cases. The model takes account of continuous and discrete endpoints, site differences, and random cluster and subject effects of both a time‐invariant and a time‐varying nature. We provide a comprehensive design equation, relating sample size to precision for cohort and cross‐sectional designs, and show that the follow‐up cost and selection bias attending a cohort design may outweigh any theoretical advantage in precision. We provide formulae for the minimum number of clusters and subjects. We relate this model to the recently published prevalence model for COMMIT, a multi‐site trial of smoking cessation programmes. Finally, we tabulate parameter estimates for some physiological endpoints from recent community‐based heart‐disease prevention trials, work an example, and discuss the need for compiling such estimates as a basis for informed design of future field trials.
Sample size requirements for stratified prospective studies with null hypothesis of non‐unity relative risk using the score test
Authors: Nam Jun‐Mo
Published: April 2007

The standard test of the null hypothesis of unity of the relative risk seeks to determine if two treatments differ. It does not apply when the requirement is either to establish the equivalence of two treatments or to determine whether the relative risk is less than a specified value other than one. This paper presents the asymptotic power function of the score test for the null hypothesis of a specified value of a common relative risk for stratified prospective studies and proposes an approximate formula for the sample size required for a specific power of the test. One can obtain a sample size formula for stratified studies with the standard null hypothesis of unity relative risk as a special case of this formula.
Selecting the best dose when a monotonic dose‐response relation exists
Authors: Russek‐Cohen Estelle , Simon Richard M.
Published: April 2007

We propose a method for selecting the best treatment when a monotonic dose—response relationship exists. Because of side effects associated with higher doses, the highest dose may not be the optimum, particularly when a lower dose gives a similar response. Rather than assume a particular functional relationship of dose to response, we use isotonic regression techniques. We consider the case of three treatment levels, which is applicable to many clinical trials. The lowest treatment level may represent a placebo or no treatment control. While we focus primarily on Bernoulli response variables, we also discuss a model for normally distributed data. We suggest a two‐stage procedure that we have investigated via simulation.
Group sequential testing in clinical trials with multivariate observations: A review
Authors: Lee Jae Won
Published: April 2007

Most group sequential testing procedures assume that each patient has only one response. Thus interim test statistics have independent increments. In many clinical trials, however, a response variable is measured at each follow‐up visit. In this article, I review some recently developed group sequential methods for repeated measures and for other types of multivariate responses. Six parametric methods and three non‐parametric methods are included. My review focuses on the comparison of the assumptions made and the applications of each of these methods.
Estimating the variations and autocorrelations in dietary intakes on weekdays and weekends
Authors: Bhargava Alok , Forthofer Ronald , McPherson Susie , Nichaman Milton
Published: April 2007

The high incidence of breast cancer in the U.S. and the possible link with dietary fat has led to the development of educational programmes for reducing women's fat intakes by agencies such as the National Cancer Institute and the U.S. Department of Agriculture. In this paper, we analyse the effects of an intervention on the intakes of 12 nutrients by 37 women in the Houston area. We estimate a dynamic random effects model by maximum likelihood to estimate the between and the within variations and the autocorrelations using 7 consecutive food records before and after the intervention programme. The main findings are that the pattern of within variations differs during weekdays and weekends. Secondly, the mean intakes of nutrients such as β‐carotene and ascorbic acid tend to be lower on weekends. Lastly, the intervention programme reduced the overall fat intakes and also increased the variation in the consumption of foods high in fats during weekdays. We discuss the implications of the results for the design of further studies.
Estimating the accuracy of dietary questionnaire assessments: Validation in terms of structural equation models
Authors: Kaaks Rudolf , Riboli Elio , Estève Jacques , Van Kappel Anne Linda , Van Staveren Wija A.
Published: April 2007

The validity and precision of questionnaire assessments of the habitual intake of individuals are usually evaluated by comparison with reference measurements that are supposed to provide a best possible substitute for the individuals' true intake values. In the present paper, a measurement error model is presented, defining different types of error ‐ random or systematic, and within or between individuals ‐ that may occur in dietary intake measurements. It is then discussed how simple latent variable models (structural equation models) can be used to estimate the average magnitude of these various types of error. So far, approaches described for the analysis of dietary validity studies have all been based on the assumption that the random errors of repeat reference measurements, taken by the same method on different occasions, are uncorrelated, so that the average of a sufficiently large number of repeat reference measurements will provide an accurate ranking of individuals by true intake level. In the present paper it is described how, by additional comparison with a third type of measurement such as a biochemical marker, the validity of dietary questionnaire measurements can be evaluated even in situations where the random errors of repeat reference measurements can no longer be assumed to be independent.
A robust goodness‐of‐fit test statistic with application to ordinal regression models
Authors: Lipsitz Stuart R. , Buoncristiani John F.
Published: April 2007

We propose a goodness‐of‐fit test statistic for linear regression with heterogeneous variance, which is asymptotically chi‐square if the given model is correct. The test statistic is computed as a quadratic form of observed minus predicted responses. We apply the method to a linear regression for an ordinal categorical response, the wheezing status of a child (no wheeze, wheeze with cold, wheeze apart from cold) as a function of maternal smoking and city of residence.
Non‐hierarchical logistic models and case‐only designs for assessing susceptibility in population‐based case‐control studies
Authors: Piegorsch Walter W. , Weinberg Clarice R. , Taylor Jack A.
Published: April 2007

This article describes how genetic components of disease susceptibility can be evaluated in case‐control studies, where cases and controls are sampled independently from the population at large. Subjects are assumed unrelated, in contrast to studies of familial aggregation and linkage. The logistic model can be used to test collapsibility over phenotypes or genotypes, and to estimate interactions between environmental and genetic factors. Such interactions provide an example of a context where non‐hierarchical models make sense biologically. Also, if the exposure and genetic categories occur independently and the disease is rare, then analyses based only on cases are valid, and offer better precision for estimating gene‐environment interactions than those based on the full data.
Median time from seroconversion to AIDS in Italian HIV‐positive haemophiliacs: Different parametric estimates
Authors: Chiarotti Flavia , Palombi Massimo , Schinaia Nicola , Ghirardini Alessandro , Bellocco Rino
Published: April 2007

The purpose of this study was to estimate the median incubation time between human immunodeficiency virus (HIV) infection and onset of acquired immunodeficiency syndrome (AIDS), using three parametric models and six estimates of seroconversion time. Study subjects were 732 HIV‐positive haemophiliacs enrolled in the Italian Registry of patients with congenital coagulation disorders. Seroconversion time was estimated for each subject according to six different criteria, based on three distributions of seroconversion (uniform, uniform on three sub‐intervals and truncated Weibull) and two indices synthesizing each distribution (median and median of three random values). The estimated seroconversion times were subsequently used as starting points in the analysis of incubation. This was performed applying Kaplan‐Meier non‐parametric survival analysis, and fitting to incubation data three probability density functions, representing three different situations with respect to the hazard of developing AIDS following seroconversion (namely Weibull (WE), generalized exponential (GE) and log‐logistic (LL)). The cumulative incidence over an 8‐year period ranged from 14.9 to 17.8 per cent when applying the Kaplan‐Meier method, from 14.1 to 17.2 per cent when using the WE function, from 14.5 to 17.3 per cent when using the GE function and from 14.4 to 17.3 per cent when using the LL function, depending on the estimate of seroconversion time used. Similarly, the median incubation times ranged from 12.6 to 15.0 years with the WE function, from 14.0 to 16.5 years with the GE function, and from 13.4 to 16.1 years with the LL function. The presence of a bound on the increase of the hazard function seems to affect the incubation more strongly than the eventual decrease following the attainment of the maximum risk. This may be due to the decrease in the hazard beginning when most of the seropositive subjects have already developed AIDS.
Estimation of the number of people in a register from the number of birthdates
Authors: Larsen Severin Olesen
Published: April 2007

The actual number of people in a register may not be known when unique identifiers are not available (perhaps for reasons of confidentiality) and when multiple entries can occur. A method for estimating the number of registered people is presented when dates of birth (day, month and year) are available.
Designing an optimal experiment for Bayesian stimation: Application to the Kinetics of iodine thyroid uptake
Authors: Merlé Y. , Mentré F. , Mallet A. , Aurengo A. H.
Published: April 2007

We consider the problem of designing an optimal experiment for Bayesian estimation of the parameters of a non‐linear model. When their distribution is known, the Bayesian approach allows individual estimation from a small number of measurements; the design determines the accuracy of the estimates. We propose to optimize this design by maximizing a general criterion: the expectation of the information supplied by the experiment. This approach is applied to optimize the two sampling times for Bayesian estimation of the kinetics of radioiodine thyroid uptake from an estimated non‐parametric prior distribution.
Foundations of medical diagnosis: What actually are the parameters involved in Bayes' theorem?
Authors: Miettinen Olli S. , Caro J. Jaime
Published: April 2007

Three decades ago, the thesis was adduced that setting diagnostic probabilities requires, by the inherent nature of diagnosis‐pertinent medical knowledge, the use of Bayes' theorem. That paper was both vague and inconsistent in its delineation of the nature of the parameters involved in this formulation, and subsequent authors have only added to the confusion. Nevertheless, that thesis has been, and continues to be, enthusiastically embraced by clinical scholars. We here posit what those parameters must be taken to represent in principle; and this explication reveals that their quantification poses generally unsurmountable epistemologic challenges. The implication of this is not that informed setting of diagnostic probabilities is generally infeasible. Our conclusion is, instead, that the seminal thesis was founded on an untenable pair of premises about the nature of scientifically attainable knowledge pertinent to diagnosis.
Fisher's game with the devil
Authors: Senn Stephen
Published: April 2007

The publication of Fisher's correspondence1on statistics has shed new light on his views on randomization. Quotations from this correspondence and from other works of Fisher are used to illustrate the role of randomization in clinical trials. It is concluded that Fisher's views not only are coherent but, despite having been developed over 60 years ago and with particular reference to agricultural experiments, are still relevant to the planning and analysis of clinical trials today.
Estimation of time since infection using longitudinal disease‐marker data
Authors: Dubin Neil , Berman Simeon , Marmor Michael , Tindall Brett , Jarlais Don Des , Kim Mimi
Published: April 2007

We propose a method to estimate the usually unknown time since infection for individuals infected with human immunodeficiency virus type 1 (HIV‐1). If we assume the time since infection has an exponential prior distribution, then under the model the conditional distribution of time since infection, given the CD4 level at the time of the first positive HIV‐1 antibody test, is a truncated normal density. We applied the method to prevalent cohort data both from intravenous drug users and from homosexual/bisexual men. For the intravenous drug users the estimated mean time since infection was 15.0 months from infection at a presumed mean CD4 level of 1060 cells/ml to first positive antibody test at a CD4 level of 597 cells/ml, which was the average CD4 at enrolment for infected subjects. For the homosexual/bisexual men the estimated mean time since infection was 16.7 months from infection at a presumed mean CD4 level of 699 cells/ml to first positive antibody test at an average CD4 level of 577 cells/ml. We performed a validation study using initially seronegative subjects in these cohorts who seroconverted to HIV‐1‐positive antibody status during the follow‐up period. For the intravenous drug users, data were too few to provide definitive verification of the method. In the cohort of homosexual/bisexual men, however, there was a total of 70 seroconverters with relevant data. Among them, the median absolute difference between the midpoint of the known seroconversion interval and the estimated mean infection date was 4.6 months, conditional on CD4‐lymphocyte measurements taken approximately 18 months subsequent to infection. Conditional on CD4 approximately 30 months after infection, this median difference increased modestly to 8.2 months. Our analysis suggested that the underlying mathematical model tends to overestimate short times since infection and underestimate long times since infection. We consider potential corrective modifications to the model.
Comparative exposure ratios: A non‐parametric, multifactor technique for case‐control studies
Authors: Aickin Mikel , Ritenbaugh Cheryl , Surwit Earl , Meyskens Frank
Published: April 2007

The odds ratio in a two‐by‐two table is widely used in case‐control studies to measure association between disease and a binary risk factor. In this article we propose a more general measure of association, the comparative exposure ratio (CER), which is the ratio of the number of case‐control pairs where the case has greater exposure divided by the number where the control has greater exposure. In simple cases, the CER is an odds ratio or a weighted combination of odds ratios. In more general cases a CER continues to measure association even when an odds ratio computation is not feasible. Moreover, CERs improve on odds ratios in several ways: they do not require binary risk factors, or a choice of the scale of measurement of continuous risk factors; they make it possible to investigate multiple risk factors simultaneously, without multivariate parametric assumptions; they also can be used to detect patterns that might indicate possible causal pathways. We illustrate how various choices of the definition of ‘greater exposure’ make the CER a powerful and flexible tool. We give expressions for confidence intervals for CERs, and verify in a pilot simulation that they are valid. Finally, we illustrate with a case‐control study of cervical dysplasia how exploratory inference using CERs can be carried out. (This research was partially supported by grants from the National Cancer Institute, CA 41108 and CA 25702).
A geometric approach to the analysis of physiological flow data
Authors: Shih Weichung Joseph
Published: April 2007

Physiological flow data are common in various medical fields. Examples include urinary, blood and expiratory flows. They are widely used in assessing functions in the urinary, circulatory, or pulmonary systems, respectively. Current statistical methods for analysing these flow data in clinical trials are either univariate analyses, which do not utilize all the information together, or some conventional multivariate methods (such as regression analyses) which yield results that do not render clear medical interpretations. This paper presents a new approach to analysing the flow data, using urinary flow as the primary focus. The basic idea and technical steps are applicable to other flow data as well. The proposed method aims to transform the flow measurements back to the shape of the flow graphs. Since the whole geometric pattern of the flow graph provides more information about the patient's flow condition than any individual flow parameter alone, the method is a meaningful way of combining and analysing the flow data in both statistical and clinical senses. The method is a three‐stage procedure. Patients are classified into three classes in the first stage and then ranked in sequence in the second stage, according to the geometry of the shape pattern and some clinical criteria. The classification procedure is shown to be very reliable when compared with the clinician's visual evaluation, and hence can be implemented by computer programming to aid clinical trials involving many patients. The whole ranking score is then readily analysed at the third stage for comparing treatment effects by the analysis of covariance method based on ranks, with the post‐treatment score as the response variable and the baseline score as the covariate. An example of a urinary flow data set is provided to illustrate the use of the procedure.
Analysis of the wideband external pulse: An application of graphical methods
Authors: Denby Lorraine , Mallows Colin L. , Blank Seymour G.
Published: April 2007

This paper demonstrates the power of statistical graphics in gleaning information from very large medical data sets. In the case of the particular data set described here (the wideband external pulse signal) little was known about the shape of the signal and the relationship of that shape to the characteristics of the related physical system, the cardiovascular system. Previous numerical statistical analyses of the data were unfruitful in finding the important relationships which were easily discovered through the appropriately designed graphics. The paper also describes the process of designing graphics to help in discovering the important relationships in the data.
Practical problems in fitting a proportional hazards model to data with udated measurements of the covariates
Authors: Altman Douglas G. , De Stavola Bianca L.
Published: April 2007

We review and discuss the practical problems encountered when analysing the effect on survival of covariates which are measured repeatedly over time. Specific issues arise over and above those met with the standard proportional hazards model and concern all stages of data preparation, data analysis and interpretation of the results. Data from a randomized clinical trial of patients with primary biliary cirrhosis, on whom several measurements were taken at regular intervals after entry, are presented as an illustration.
Application of sample survey methods for modelling ratios to incidence densities
Authors: Lavange Lisa M. , Keyes Lynette L. , Koch Gary G. , Margolis Peter A.
Published: April 2007

We describe ratio estimation methods for multivariately analysing incidence densities from prospective epidemiologic studies. Commonly used in survey data analysis, these ratio methods require minimal distributional assumptions and take into account the random variability in the at‐risk periods. We illustrate their application with data from a study of lower respiratory illness (LRI) in children during the first year of life. One question of interest is whether children with passive exposure to tobacco smoke have a higher rate of LRI, on average, than those with no exposure and in a setting where age of child and season are taken into account. A second question is whether the relationship persists after adjusting for background variables such as family's socioeconomic status, crowding in the home, race, and type of feeding. The basic strategy consists of a two‐step process in which we first estimate subgroup‐specific incidence densities and their covariance matrix via a first‐order Taylor series approximation. These estimates are used to test for differences in marginal rates of LRI between children exposed to tobacco smoke and those not exposed. We then fit a log‐linear model to the estimated ratios in order to test for significant covariate effects. The ability to produce direct estimates of adjusted incidence density ratios for risk factors of interest is an important advantage of this approach. For comparison purposes and to address the limitations of the ratio method with respect to the number of covariates that can be controlled simultaneously, we consider survey logistic regression methods for the example data as well as logistic and Poisson regression models fitted via generalized estimating equation methods. Although the analysis strategy is illustrated with illness data from an epidemiologic study, the context of application is broader and includes, for example, data on adverse events from a clinical trial.
Exposure efficacy and change in contact rates in evaluating prophylactic HIV vaccines in the field
Authors: Halloran M. Elizabeth , Longini Ira M. , Haber Michael J. , Struchiner Claudio J. , Brunet Robert C.
Published: April 2007

Field studies of the efficacy of prophylactic vaccines in reducing susceptibility rely on the assumption of equal exposure to infection in the vaccinated and unvaccinated groups. Differential exposure to infection could, however, be the goal of other types of intervention programme, or it could occur secondary to belief in the protective effects of a prophylactic measure, such as vaccination. We call this differential exposure the exposure efficacy, or behaviour efficacy. To study the relative contribution of unequal exposure to infection and differential susceptibility to the estimate of vaccine efficacy, we formulate a simple model that explicitly includes both susceptibility and exposure to infection. We illustrate this on the example of randomized field trials of prophylactic human immunodeficiency virus vaccines. Increased exposure to infection in the vaccinated group may bias the estimated reduction in susceptibility. The bias in the estimate depends on the choice of efficacy parameter, the amount of information used in the analysis, the distribution and level of protection in the population, and the imbalance in exposure to infection. Sufficient increase in contacts in the vaccinated could result in the vaccine being interpreted as having an immunosuppressive effect. Estimates of vaccine efficacy are generally more robust to imbalances in exposure to infection when the detailed history of exposure to infection can be used in the analysis or at high levels of protection. The bias also depends on the relationship between the distribution of vaccine protection and the distribution of behaviour change, which could differ between blinded and unblinded trials.
Design and analysis considerations in a cohort study involving repeated measurement of both exposure and outcome: The association between genital papillomavirus infection and risk of cervical intraepithelial neoplasia
Authors: Duffy S. W. , Rohan T. E. , McLaughlin J. R.
Published: April 2007

Cohort studies commonly involve a single determination of exposure, and one or more assessments of whether or not the outcome of interest has occurred. This approach is appropriate when the exposure does not change with time, and when one can readily determine the time of outcome (for example, mortality). If either of these conditions is not met, however, we may introduce bias into the estimate of effect, since we may misclassify individual members of the cohort with respect to exposure, outcome or both. We can reduce this bias by measuring exposure and outcome on more than one occasion. In this paper, we illustrate the design and analysis issues that arise in such circumstances, by reference to an ongoing prospective study of the relationship between genital papillomavirus infection and risk of cervical intraepithelial neoplasia. This study entails annual assessments of the status of the study subjects with respect to both conditions. In particular, we examine the implications that use of this design has on the statistical power of the study.
Sample size determination for an exponential survival model with an unrestricted covariate
Authors: Zhen Boguang , Murphy James R.
Published: April 2007

We derive formulae for estimating sample size and power for detecting the effect of an unrestricted covariate on survival time. These are useful in designing survival studies with different patterns of recruitment and follow‐up when survival time is exponentially distributed. We use the asymptotic covariance matrix, conditional expectation and Taylor's expansion techniques to develop these formulae. Computer simulations indicate that the asymptotic approximations used in developing the formulae are good over a range of parameter values and different patterns of recruitment and follow‐up that are relevant to survival studies.
New designs for the selection of treatments to be tested in randomized clinical trials
Authors: Simon R. , Thall P. F. , Ellenberg S. S.
Published: April 2007

The most important aspect of phase III randomized clinical trials is the selection of the experimental treatments to be tested. Often this decision is based on uncontrolled phase II trials. Substantial statistical attention has been focused on the design of phase III trials and for simple phase II trials, which determine whether a new drug has any anti‐disease activity. Much less statistical effort has been devoted to the design and analysis of phase II trials for screening active experimental treatments to determine whether they are sufficiently active, relative to standard treatments, to warrant the conduct of a large randomized phase III trial. This problem is particularly acute in the development of drug combinations where many regimens are possible. We review several designs for such screening trials which we have developed.
A multiple decision procedure in clinical trials
Authors: Chen T. Timothy , Simon Richard
Published: April 2007

In some multiple treatment arm clinical trials there is an order of preference for the treatments based on secondary considerations like toxicity or cost. In this paper, we consider the case where two or more treatments could have equal prior preference. This formulation includes the problem of comparing several equally preferred experimental treatments to one control, or the comparison of a combination with its components. Our decision procedures will guarantee a high selection probability for the correct treatment(s) when that selection is appropriate. We establish sample size requirements for our decision procedures which can be applied to clinical trials with normal, binomial, or right censored exponential endpoints.
Time series for modelling counts from a relapsing‐remitting disease: Application to modelling disease activity in multiple sclerosis
Authors: Albert Paul S. , McFarland Henry F. , Smith Mary E. , Frank Joseph A.
Published: April 2007

Many chronic diseases are relapsing‐remitting diseases, in which subjects alternate between periods with increasing and decreasing disease activity; relapsing‐remitting multiple sclerosis is an example. This paper proposes two classes of models for sequences of counts observed from a relapsing‐remitting disease. In the first, the relapsing‐remitting nature of the data is modelled by a Poisson time series with a periodic trend in the mean. In this approach, the mean is expressed as a function of a sinusoidal trend and past observations of the time series. An algorithm that uses GLIM is developed, and it results in maximum‐likelihood estimation for the amplitude, frequency and autoregressive effects. In the second class of models, the relapsing‐remitting behaviour is described by a Poisson time series in which changes in the mean follow a latent Markov chain. An EM algorithm is developed for maximum‐likelihood estimation for this model. The two models are illustrated and compared with data from a study evaluating the use of serial magnetic resonance imaging as a measure of disease activity in relapsing‐remitting multiple sclerosis.
Modelling transitional and joint marginal distributions in repeated categorical data
Authors: Follmann Dean
Published: April 2007

When a repeated measures endpoint classifies people into several categories, marginal and transitional models provide two distinct approaches for data analysis. Marginal models estimate the probabilities of being in different categories over time. Transitional models estimate the probability of changing between any two given states during follow‐up visits. This paper develops transitional and marginal models and applies them to a clinical trial of treatments of opiate addiction. The primary outcome was the presence or absence of opiates in a thrice weekly urine test, administered for 17 weeks. Subjects frequently miss visits, however, and in effect respond in one of three ways to a visit: missing, opiates present or opiates absent. Thus we have three possible states.

Our transitional model conditions on the current state and models the transition from statekto one of the other (0,…,K‐ 1) states using a mutinomial logit model. This model generalizes previous work of Muenz and Rubinstein. Significant covariates in this model are predictive of statechanges.Our marginal model views the state at each time point, rather than the transitions, as the primary response. Here we model the probability of being in statekwith a multinomial logit model. Correlation within individuals over visits can be handled by applying the approach of Zeger and Liang or the bootstrap. Significant covariates in this model can include more ‘global’ summaries of a person such as extent of previous opiate use. Both marginal and transitional models are needed to provide a complete description of an individual's behaviour over time since global summaries might not affect transitions. Of particular substantive interest is how the opiate treatments affect both the marginal and transition probabilities.
A comparison of likelihood‐based and marginal estimating equation methods for analysing repeated ordered categorical responses with missing data: Application to an intervention trial of vitamin prophylaxis for oesophageal dysplasia
Authors: Mark Steven D. , Gail Mitchell H.
Published: April 2007

The purpose of this research was to develop appropriate methods for analysing repeated ordinal categorical data that arose in an intervention trial to prevent oesophageal cancer. The measured response was the degree of oesophageal dysplasia at 2.5 and 6 years after randomization. An important feature was that some response measurements were missing, and the missingness was not ‘completely at random’ (MCAR). We show that standard likelihood‐based methods and standard methods based on marginal estimating equations yield biased results, and we propose adaptations to both these approaches that yield valid inference under the weaker ‘missing at random’ (MAR) assumption. On the basis of efficiency calculations, simulation studies of finite sample properties, ease of computation, and flexibility for testing and exploring a range of treatment models, we recommend the adapted likelihood‐based approach for problems of this type, in which there are abundant data for estimating parameters.
Advances in statistical methodology for the evaluation of diagnostic and laboratory tests
Authors: Campbell Gregory
Published: April 2007

The ROC plot is a useful tool in the evaluation of the performance of medical tests for separating two populations. For a two‐state decision rule based on such a test, the ROC plot is the graph of all observed (1‐specificity, sensitivity) pairs. Each point on this empirical plot can be represented by a 2 × 2 contingency table. The non‐parametric statistics of Mann‐Whitney and Kolmogorov‐Smirnov can be immediately identified on this plot. Local non‐parametric confidence interval procedures related to the theoretical ROC curve are briefly reviewed. For continuous data, two new simultaneous confidence regions associated with the ROC curve are presented, one based on Kolmogorov‐Smirnov confidence bands for distribution functions and the other based on bootstrapping.

Two different tests on the same patients can be compared on the ROC scale. For continuous data, one important problem concerns the comparison of two ROC plots (as would arise from two correlated diagnostic tests on each patient) using a sup norm (this metric can detect differences that the ROC area cannot). The distribution of a statistic based on this norm is studied, using the bootstrap. A biomedical example illustrates the methodologies.
Regression analysis with clustered data
Authors: Graubard Barry I. , Korn Edward L.
Published: April 2007

Clustered data are found in many different types of studies, for example, studies involving repeated measures, inter‐rater agreement studies, household surveys, crossover designs and community randomized trials. Analyses based on population average and cluster specific models are commonly used for estimating treatment (exposure) effects with clustered data. This paper discusses conditions when one or both types of models are appropriate for estimating causal effects and when there is agreement between population average and cluster specific analyses. Applications of survey sampling methods to the robust estimation of standard errors of estimated treatment parameters are discussed.
Spatial distribution of neurons in tissue culture wells: Implications for sampling methods to estimate population size
Authors: McShane Lisa M. , Palmatier Meg
Published: April 2007

Many laboratory procedures require the counting of cells in culture. While many cultured cells may be counted by automated methods, neuronal cultures often require manual cell counting methods that are prohibitively time‐consuming. This paper examines methods of sampling from tissue culture wells for estimating total cell counts. Performance of sampling and estimation schemes will depend in part on how the cells distribute themselves within a well. Spatial statistical analysis techniques are applied to the known total number and distribution of neurons in two wells counted in a grid scheme to demonstrate some important features of the neuron distributional patterns. Based on these two wells and simulated realizations from other point processes, a new sampling and estimation technique using open wedge‐shaped sampling regions radiating from the centre of the well is proposed. This method is shown to result in more accurate estimates of the total number of neurons in the well than standard methods.
Selection bias in observational and experimental studies
Authors: Ellenberg Jonas H.
Published: April 2007

There has been a heightened awareness of the dangers of selection bias over the past two decades. Certainly coverage in statistical and ‘statistics for medicine’, and epidemiology textbooks have allocated pages to warn investigators and readers of investigations to be aware of its presence. The scientific community has not, however, yet accepted the necessity for critical assessment of the method of sample selection in the planning and execution of studies as a fundamental underpinning of observational and experimental studies. To wit, we are faced with a plethora of research studies receiving funding, being published in peer‐reviewed journals and influencing future studies, that may be reporting entirely spurious associations. It is the intent of this paper to present examples of selection bias in a variety of areas which have resulted in misleading or entirely incorrect results. We hope to help make such research scientifically ‘politically incorrect’ to the degree that the scientific community ‘just says no’ to such studies, either proposed or reported.
A data‐analytic approach for estimating lead time and screening benefit based on survival curves in randomized cancer screening trials
Authors: Kafadar Karen , Prorok Philip C.
Published: April 2007

Screening tests are used frequently for control of diseases such as cancer. The increased survival time of screen‐detected cases over those that are detected clinically may be due in part to ‘lead time’, or the length of time by which the disease is diagnosed earlier by screening in the presence or absence of any real extension in survival time. A realistic evaluation of screening needs to assess the true benefit of screening; that is, the length of time by which survival has been extended, beyond merely the time of the advanced diagnosis. The comparison of survival measured from time of entry between cases in a screening arm and in a control arm in randomized studies avoids the lead time bias. If the effects of average lead time and average benefit on survival are additive, these effects can be estimated by recognizing that (a) the difference in survival curves since time of diagnosis confounds benefit and lead time, but (b) the difference in survival curves since time of start of study involves benefit only. The method is evaluated on simulated data for its accuracy and may be used on data from randomized studies.
Mixed‐effects regression models for studying the natural history of prostate disease
Authors: Pearson Jay D. , Morrell Christopher H. , Landis Patricia K. , Carter H. Ballentine , Brant Larry J.
Published: April 2007

Although prostate cancer and benign prostatic hyperplasia are major health problems in U.S. men, little is known about the early stages of the natural history of prostate disease. A molecular biomarker called prostate specific antigen (PSA), together with a unique longitudinal bank of frozen serum, now allows a historic prospective study of changes in PSA levels for decades prior to the diagnosis of prostate disease. Linear mixed‐effects regression models were used to test whether rates of change in PSA were different in men with and without prostate disease. In addition, since the prostate cancer cases developed their tumours at different (and unknown) times during their periods of follow‐up, a piece‐wise non‐linear mixed‐effects regression model was used to estimate the time when rapid increases in PSA were first observable beyond the background level of PSA change. These methods have a wide range of applications in biomedical research utilizing repeated measures data such as pharmacokinetic studies, crossover trials, growth and development studies, aging studies, and disease detection.
Composite linear models for incomplete multinomial data
Authors: Baker Stuart G.
Published: April 2007

A composite linear model (CLM) is a matrix model for incomplete multinomial data. A CLM provides a unified approach for maximum likelihood inference which is applicable to a wide variety of problems involving incomplete multinomial data. By formulating a model as a CLM, one can simplify computation of maximum likelihood estimates and asymptotic standard errors. As an example, we use CLM to test marginal homogeneity for ordered categories, subject to both ignorable and non‐ignorable missing‐data mechanisms.
The partial questionnaire design for case‐control studies
Authors: Wacholder Sholom , Carroll Raymond J. , Pee David , Gail Mitchell H.
Published: April 2007

We propose an alternative to a long questionnaire that may increase quality while reducing the cost and effort of participants and researchers. In the ‘partial questionnaire design’, information about the exposure of interest is obtained from all subjects, while zero, one, or more disjoint subsets of questions about possible confounders are asked to randomly selected subgroups. The proposed analyses exploit the fact that the uncollected data can be considered to be missing at random. We show that it is possible to obtain high efficiency for estimating the effect of exposure of interest, adjusted for confounding, while substantially shortening average questionnaire length.
Testing for differences in changes in the presence of censoring: Parametric and non‐parametric methods
Authors: Wu Margaret C. , Hunsberger Sally , Zucker David
Published: April 2007

Some commonly used parametric and non‐parametric methods for analysing repeated measures with incomplete observations are briefly reviewed. The performances of these methods in the presence of completely random, as well as informative censoring are compared in simulated experiments generated under the linear random effects model with parameter values derived from realistic examples. The effects of some moderate model deviations are also compared. The results indicate that in the presence of informative censoring, the usual parametric and nonparametric methods derived under the assumption of random censoring could either suffer severe loss of power or provide false positive results. The conditional linear model for informative censoring when used in conjunction with the bootstrap variance estimation procedure performed well under both random and informative censoring mechanisms. The non‐parametric procedure obtained by ranking the individual summary statistics, although not as efficient as the conditional linear model with robust variance, also performed relatively well in most situations. Therefore, in situations in which informative censoring is likely to occur it is important to select the proper method of analysis to test for the informativeness of censoring and to account for its effects.
Stochastic curtailing and conditional power in matched case‐control studies
Authors: Hunsberger Sally , Sorlie Paul , Geller Nancy L.
Published: April 2007

New methodology for early stopping of observational epidemiologic studies is developed. To indicate the likelihood of a positive result at the end of a study, the conditional power (CP) is calculated using an approach similar to that of Halperin et al. Given the current result, the conditional probability of a positive result for the entire study is determined. An expression relating the new approach to an approach given by Lan and Wittes is derived. As an example, the CP is calculated based on information from the first third of a matched case‐control study embedded in an epidemiological study.
Sources of bias in studies of time to pregnancy
Authors: Weinberg Clarice R. , Baird Donna D. , Wilcox Allen J.
Published: April 2007

Among sexually active couples who are not using contraception, there is considerable heterogeneity in fertility, even among those who eventually achieve pregnancy. The number of menstrual cycles required, measured in integer time, is greatly overdispersed compared to the geometric, suggesting that the per cycle probability of conception varies considerably among couples. Some of this variability may reflect the effect of reproductive toxicants on fertility, and studies of time to pregnancy can be useful in identifying such toxic effects. We describe models for analysing time‐to‐pregnancy data, and discuss seven sources of bias that can lead the reproductive epidemiologist to spurious conclusions. Certain analytic and design strategies can help protect against some of the pitfalls.
A comparison of tumour incidence analyses applicable in single‐sacrifice animal experiments
Authors: Dinse Gregg E.
Published: April 2007

Monte Carlo methods are used to evaluate the operating characteristics of several trend tests for comparing incidence rates of occult tumours. The candidate tests are applicable in long‐term animal experiments that have just one sacrifice time and no information on cause of death. When survival decreases with dose and the tumour is lethal, the tests that do not adjust for survival or that assume tumours are non‐lethal behave conservatively, whereas the test that assumes tumours are rapidly lethal rejects the hypothesis of equal incidence rates too frequently. The only test which consistently operates at the correct level is one that specifies a constant difference between the death rates for animals with and without the tumour. Surprisingly, the test that specifies a constant ratio of these death rates often is conservative. Finally, a test based on a simple modification of the unadjusted test, which accounts for differential mortality by scaling down the size of the risk set, performs reasonably well in many cases. Among the tests that operate at the proper level, the constant risk difference test consistently exhibits high power across a wide range of situations.
Meta‐analysis of animal experiments on dietary fat intake and mammary tumours
Authors: Freedman Laurence S.
Published: April 2007

I describe a meta‐analysis of animal experiments designed to investigate the effects of dietary fat intake upon mammary tumour development. These experiments involve one or more of three types of dietary manipulation: (i) varying the proportion of fat in the diet; (ii) varying the source of fat; and (iii) varying the amount of energy consumed. I use linear logistic regression models to investigate questions about the relative effects of fat and total energy intake upon mammary tumour development and about the effects of different sources of fat. The results of these analyses are presented and discussed. The regression models employed are characterized by the inclusion of an experiment ‘effect’ to ensure that estimated nutrient effects are based only upon within‐experiment comparisons. Homogeneity of nutrient effects across experiments is assumed. The method differs from the usual meta‐analysis of clinical trials in that the nutrient effects may not be estimable from each individual experiment, but only from combinations of experiments. This poses problems for the development of a model that allows heterogeneity of nutrient effects.