Testing secondary hypotheses following sequential clinical trials

Sequential monitoring in a clinical trial poses difficulty in hypotheses testing on secondary endpoints after the trial is terminated. The conventional likelihood-based testing procedure that ignores the sequential monitoring inflates Type I error and undermines power. In this article, we show that the power of the conventional testing procedure can be substantially improved while the Type I error is controlled. The method is illustrated with a real clinical trial.     

Point estimation following group sequential tests

Kim and DeMets (1987, Biometrics 43, 857-864) described an exact procedure for constructing confidence intervals for a normal mean following group sequential tests when the boundaries were generated based on the notion of use functions proposed by Lan and DeMets (1983, Biometrika 70, 659-663). In this article, three point estimators for a normal mean following group sequential tests are considered, and their properties are investigated by Monte Carlo simulation. Based on the simulation results, some suggestions are made as to the choice of group sequential designs and use function.     

Exact confidence intervals following a group sequential test

A numerical method is used to compute confidence intervals, which have exact coverage probabilities, for the mean of a normal distribution following a group sequential test. This method, which uses an ordering of the sample space similar to that employed by Siegmund (1978, Biometrika 65, 341-349), is contrasted with the usual confidence interval for the mean.     

Confidence intervals for a normal mean following a group sequential test

This paper presents methodology based on the likelihood-ratio test, developed for construction of confidence intervals for a normal mean, following a group sequential test. Examples show that the confidence intervals produced by this method have accurate nominal probability of coverage and have generally shorter average length than that produced by Tsiatis, Rosner, and Mehta (1984, Biometrics 40, 797-803).     

A sequential test for assessing the effectiveness of response strategies during an emerging epidemic

In epidemiology, the fatality rate is an important indicator of disease severity and has been used to evaluate the effects of new treatments. During an emerging epidemic with limited resources, monitoring the changes in fatality rate can also provide signals on the evaluation of government policies and healthcare quality, which helps to guide public health decision. A statistical test is developed in this paper to detect changes in fatality rate over time during the course of an emerging infectious disease. A major advantage of the proposed test is that it only requires the regularly reported numbers of deaths and recoveries, which meets the actual need as detailed surveillance data are hard to collect during the course of an emerging epidemic especially the deadly infectious diseases with large magnitude. In addition, with the sequential testing procedure, the effective measures can be detected at the earliest possible time to provide guidance to policymakers for swift action. Simulation studies showed that the proposed test performs well and is sensitive in picking up changes in the fatality rate. The test is applied to the 2014–2016 Ebola outbreak in Sierra Leone for illustration.     

Symmetric group sequential test designs

In Phase III clinical trials, ethical considerations often demand interim analyses in order that the better treatment be made available to all patients as soon as possible. Group sequential test designs that do not treat the hypotheses symmetrically may not fully address this concern since early termination of the study may be easier under one of the hypotheses. We present a one-parameter family of symmetric one-sided group sequential designs that are nearly fully efficient in terms of the average sample number. The symmetric tests are then extended to a two-sided hypothesis test. These symmetric two-sided group sequential tests are found to have improved overall efficiency when compared to the tests proposed by Pocock (1977, Biometrika 64, 191-199) and O'Brien and Fleming (1979, Biometrics 35, 549-556). Tables of critical values for both one-sided and two-sided symmetric designs are provided, thus allowing easy determination of sample sizes and stopping boundaries for a group sequential test. Approximate tests based on these designs are proposed for use when the number and timing of analyses are random.     

A unifying family of group sequential test designs

Currently, the design of group sequential clinical trials requires choosing among several distinct design categories, design scales, and strategies for determining stopping rules. This approach can limit the design selection process so that clinical issues are not fully addressed. This paper describes a family of designs that unifies previous approaches and allows continuous movement among the previous categories. This unified approach facilitates the process of tailoring the design to address important clinical issues. The unified family of designs is constructed from a generalization of a four-boundary group sequential design in which the shape and location of each boundary can be independently specified. Methods for implementing the design using error-spending functions are described. Examples illustrating the use of the design family are also presented.     

Exact confidence bounds following adaptive group sequential tests

Summary .  We provide a method for obtaining confidence intervals, point estimates, and p-values for the primary effect size parameter at the end of a two-arm group sequential clinical trial in which adaptive changes have been implemented along the way. The method is based on applying the adaptive hypothesis testing procedure of Müller and Schäfer (2001, Biometrics 57, 886–891) to a sequence of dual tests derived from the stage-wise adjusted confidence interval of Tsiatis, Rosner, and Mehta (1984, Biometrics 40, 797–803). In the nonadaptive setting this confidence interval is known to provide exact coverage. In the adaptive setting exact coverage is guaranteed provided the adaptation takes place at the penultimate stage. In general, however, all that can be claimed theoretically is that the coverage is guaranteed to be conservative. Nevertheless, extensive simulation experiments, supported by an empirical characterization of the conditional error function, demonstrate convincingly that for all practical purposes the coverage is exact and the point estimate is median unbiased. No procedure has previously been available for producing confidence intervals and point estimates with these desirable properties in an adaptive group sequential setting. The methodology is illustrated by an application to a clinical trial of deep brain stimulation for Parkinson's disease.     

Unbiased estimation of individual asymmetry

The importance of measurement error (ME) for the estimation of population level fluctuating asymmetry (FA) has long been recognized. At the individual level, however, this aspect has been studied in less detail. Recently, it has been shown that the random slopes of a mixed regression model can estimate individual asymmetry levels that are unbiased with respect to ME. Yet, recent studies have shown that such estimates may fail to reflect heterogeneity in these effects. In this note I show that this is not the case for the estimation of individual asymmetry. The random slopes adequately reflect between‐individual heterogeneity in the underlying developmental instability. Increased levels of ME resulted in, on average, lower estimates of individual asymmetry relative to the traditional unsigned asymmetry. This well‐known shrinkage effect in Bayesian analysis adequately corrected for ME and heterogeneity in ME resulting in unbiased estimates of individual asymmetry that were more closely correlated with the true underlying asymmetry.     

Patchy patterns due to group dispersal

The presence of multiple foci in population patterns may be due to various processes arising in the population dynamics. Group dispersal, which has been lightly investigated for airborne species, is one of these processes.We built a stochastic model generating the dispersal of groups of particles. This model may be viewed as an extension of classical dispersal models based on parametric kernels. It has a hierarchical structure: at the first stage group centers are drawn under a classical dispersal kernel; at the second stage the particles are diffused around their group centers. Analytic and simulation results show that group dispersal is a sufficient condition to generate patterns with multiple foci, i.e. patchy patterns, even if the population can remain particularly concentrated.     

Confidence intervals following group sequential tests in clinical trials

Tsiatis, Rosner, and Mehta (1984, Biometrics 40, 797-803) proposed a procedure for constructing confidence intervals following group sequential tests of a normal mean. This method is first extended for group sequential tests for which the sample sizes between interim analyses are not identical or the times are not equally spaced. Then properties of this confidence interval estimation procedure are studied by simulation. The extension accommodates the flexible procedure by Lan and DeMets (1983, Biometrika 70, 659-663) for constructing discrete group sequential boundaries to form a structure for monitoring and estimation following a class of group sequential tests. Finally, it is demonstrated how to combine the procedures by Lan and DeMets and by Tsiatis, Rosner, and Mehta using a FORTRAN program.