Biomedical subjects

Guang Yong Zou

Publications and source records attributed to Guang Yong Zou.

3 recordsLinked to original sources

PubMed2006

The merits of testing Hardy-Weinberg equilibrium in the analysis of unmatched case-control data: a cautionary note.

Testing for departures from the assumption of Hardy-Weinberg equilibrium (HWE) has been widely recommended as a preliminary step in the analysis of genetic case-control studies. Some authors suggest using a two-stage procedure in which gene/disease associations are ultimately evaluated using either the Pearson chi-square procedure or the Cochran-Armitage test for trend. Other authors go further and encourage investigators to discard data that are in violation of HWE, essentially using the test as a tool for identifying genotyping errors. In this paper we show that 1) testing for HWE should not be used as a tool to identify genotyping errors; and 2) it is not necessary, and possibly even harmful, to test the HWE assumption before testing for association between alleles and disease. Instead one should inherently account for deviations from HWE with an adjusted chi-square test statistic, a procedure which in the present context is identical to the trend test. Examples from previous reports are used to illustrate the methodology.

Case-Control Studies↗

PubMed2005

Quantifying responsiveness of quality of life measures without an external criterion.

The responsiveness of a quality of life measure has received considerable attention in the literature. A two time-point (pre-/post-) study design is usually adopted to evaluate this property when a gold standard is not available. Among many indices, Cohen's effect size and the standardized response mean (SRM) are usually computed. To interpret the results, researchers commonly appeal to an arbitrary criterion for both indices even though they are different by definition. In this paper, we demonstrate their close algebraic relationship and conceptual differences, showing that only the SRM is necessary to quantify responsiveness. To facilitate interpretation, we transform the SRM to the 'probability of change' with a value of 0.5 denoting null responsiveness and 1.0 perfect responsiveness. Simple confidence interval procedures are provided and evaluated. We also discuss the possibility of applying the results to the analysis of data from a two independent groups pre-/post- design. Two examples are provided.

Clinical Trials as Topic↗

PubMed2005

Group sequential methods for cluster randomization trials with binary outcomes.

BACKGROUND: Cluster randomization trials in which intact social units are randomly assigned to different intervention groups have become very popular in recent years, particularly for the evaluation of innovations in the delivery of health care. An extensive literature dealing with the associated methodological challenges has also appeared. Although the monitoring of such trials using formal stopping rules is clearly indicated when the outcomes are irreversible and individual-level data are available sequentially, simple and reliable statistical methods that may be used for this purpose are currently not available. PURPOSE: To investigate the validity of standard group sequential methods when applied to cluster randomization trials having binary outcomes. METHODS: The large sample distributions for each of five test statistics computed from sequentially accumulated data are derived. A simulation study is performed to evaluate the finite sample properties of these statistics when applied to the interim analysis of cluster randomization trials. Data from the World Health Organization antenatal care trial are used to illustrate the methods. RESULTS: Each of the joint distributions is shown to be characterized by a covariance structure that asymptotically satisfies an independent increments structure, a foundation that simplifies group sequential methods. The simulation study reveals that four of the five test statistics evaluated provide satisfactory performance with as few as 10 clusters allocated to each of two interventions. LIMITATIONS: The applicability of our results to effect estimation following a group sequential cluster randomization trial is not investigated, although a theoretical foundation which may be used for this purpose is presented. CONCLUSIONS: Standard group sequential methods can be applied to cluster randomization trials when interim analyses are warranted.

Clinical Trials as Topic↗