PubMed · 12370196
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Holger Breithaupt. 2002. Do the maths.. https://doi.org/10.1093/embo-reports%2Fkvf203
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Evaluation of the performance of a new diagnostic procedure with respect to a standard procedure arises frequently in practice. The response of interest, often in a dichotomous form, is measured twice, once with each procedure. The two procedures are administered to either two matched individuals, or when practical, to the same individual. A large sample test for matched-pair data is the McNemar test. The main assumption of this test is independent paired responses; however, when more than one outcome from an individual is measured by each procedure, the data are clustered. Examples of such cases can be seen in dental and ophthalmology studies. Variance adjustment methods for the analysis of clustered matched-pair data have been proposed; however, because of unequal cluster sizes, variability of correlation structures within a cluster (within paired responses in a cluster as well as between paired responses in a cluster), and unequal success probabilities among the clusters, the performances of some available methods are not consistent. This research proposes a simple adjustment to the McNemar test for the analysis of clustered matched-pair data. Method of moments is used to calculate a consistent variance estimator. Using Monte Carlo simulation, the size and power of the proposed test are compared to those of two currently available methods. To illustrate practical application, clustered matched-pair data from two clinical studies are analysed.
In this paper we describe methods for addressing multiplicity issues arising in the analysis of clinical trials with multiple endpoints and/or multiple dose levels. Efficient 'gatekeeping strategies' for multiplicity problems of this kind are developed. One family of hypotheses (comprising the primary objectives) is treated as a 'gatekeeper', and the other family or families (comprising secondary and tertiary objectives) are tested only if one or more gatekeeper hypotheses have been rejected. We discuss methods for constructing gatekeeping testing procedures using weighted Bonferroni tests, weighted Simes tests, and weighted resampling-based tests, all within the closed testing framework. The new strategies are illustrated using an example from a clinical trial with co-primary endpoints, and using an example from a dose-finding study with multiple endpoints. Power comparisons with competing methods show the gatekeeping methods are more powerful when the primary objective of the trial must be met.
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