Biomedical subjects

Yan D Zhao

Publications and source records attributed to Yan D Zhao.

4 recordsLinked to original sources

PubMed2006

Sample size estimation for the van Elteren test--a stratified Wilcoxon-Mann-Whitney test.

The van Elteren test is a type of stratified Wilcoxon-Mann-Whitney test for comparing two treatments accounting for strata. In this paper, we study sample size estimation methods for the asymptotic version of the van Elteren test, assuming that the stratum fractions (ratios of each stratum size to the total sample size) and the treatment fractions (ratios of each treatment size to the stratum size) are known in the study design. In particular, we develop three large-sample sample size estimation methods and present a real data example to illustrate the necessary information in the study design phase in order to apply the methods. Simulation studies are conducted to compare the performance of the methods and recommendations are made for method choice. Finally, sample size estimation for the van Elteren test when the stratum fractions are unknown is also discussed.

Clinical Trials as Topic↗

PubMed2006

Power approximation for the van Elteren test based on location-scale family of distributions.

The van Elteren test, as a type of stratified Wilcoxon-Mann-Whitney test for comparing two treatments accounting for stratum effects, has been used to replace the analysis of variance when the normality assumption was seriously violated. The sample size estimation methods for the van Elteren test have been proposed and evaluated previously. However, in designing an active-comparator trial where a sample of responses from the new treatment is available but the patient response data to the comparator are limited to summary statistics, the existing methods are either inapplicable or poorly behaved. In this paper we develop a new method for active-comparator trials assuming the responses from both treatments are from the same location-scale family. Theories and simulations have shown that the new method performs well when the location-scale assumption holds and works reasonably when the assumption does not hold. Thus, the new method is preferred when computing sample sizes for the van Elteren test in active-comparator trials.

Algorithms↗

PubMed2005

A randomized controlled trial of duloxetine alone, pelvic floor muscle training alone, combined treatment and no active treatment in women with stress urinary incontinence.

PURPOSE: We primarily compared the effectiveness of combined pelvic floor muscle training (PFMT) and duloxetine with imitation PFMT and placebo for 12 weeks in women with stress urinary incontinence (SUI). In addition, we compared the effectiveness of combined treatment with single treatments, single treatments with each other and single treatments with no treatment. MATERIALS AND METHODS: This blinded, doubly controlled, randomized trial enrolled 201 women 18 to 75 years old with SUI at 17 incontinence centers in the Netherlands, United Kingdom and United States. Women averaged 2 or more incontinence episodes daily and were randomized to 1 of 4 combinations of 80 mg duloxetine daily, placebo, PFMT and imitation PFMT, including combined treatment (in 52), no active treatment (in 47), PFMT only (in 50) and duloxetine only (in 52). The primary efficacy measure was incontinence episode frequency. Other efficacy variables included the number of continence pads used and the Incontinence Quality of Life questionnaire score. RESULTS: The intent to treat population incontinence episode frequency analysis demonstrated the superiority of duloxetine with or without PFMT compared with no treatment or with PFMT alone. However, pad and Incontinence Quality of Life analyses suggested greater improvement with combined treatment than single treatment. A completer population analysis demonstrated the efficacy of duloxetine with or without PFMT and suggested combined treatment was more effective than either treatment alone. CONCLUSIONS: The data support significant efficacy of combined PFMT and duloxetine in the treatment of women with SUI. We hypothesize that complementary modes of action of duloxetine and PFMT may result in an additive effect of combined treatment.

Adult↗

PubMed2003

Modelling the random effects covariance matrix in longitudinal data.

A common class of models for longitudinal data are random effects (mixed) models. In these models, the random effects covariance matrix is typically assumed constant across subject. However, in many situations this matrix may differ by measured covariates. In this paper, we propose an approach to model the random effects covariance matrix by using a special Cholesky decomposition of the matrix. In particular, we will allow the parameters that result from this decomposition to depend on subject-specific covariates and also explore ways to parsimoniously model these parameters. An advantage of this parameterization is that there is no concern about the positive definiteness of the resulting estimator of the covariance matrix. In addition, the parameters resulting from this decomposition have a sensible interpretation. We propose fully Bayesian modelling for which a simple Gibbs sampler can be implemented to sample from the posterior distribution of the parameters. We illustrate these models on data from depression studies and examine the impact of heterogeneity in the covariance matrix on estimation of both fixed and random effects.

Antidepressive Agents↗