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Peter H Westfall

Publications and source records attributed to Peter H Westfall.

4 recordsLinked to original sources
PubMed2003

Gatekeeping strategies for clinical trials that do not require all primary effects to be significant.

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.

Clinical Trials as Topic↗
PubMed2003

Bayesian multiple testing for two-sample multivariate endpoints.

In clinical studies involving multiple variables, simultaneous tests are often considered where both the outcomes and hypotheses are correlated. This article proposes a multivariate mixture prior on treatment effects, that allows positive probability of zero effect for each hypothesis, correlations among effect sizes, correlations among binary outcomes of zero versus nonzero effect, and correlations among the observed test statistics (conditional on the effects). We develop a Bayesian multiple testing procedure, for the multivariate two-sample situation with unknown covariance structure, and obtain the posterior probabilities of no difference between treatment regimens for specific variables. Prior selection methods and robustness issues are discussed in the context of a clinical example.

Bayes Theorem↗
PubMed2002

Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals.

There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H(0) and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.

Algorithms↗