Statistics in medicine
-
Statistics in medicine · Sep 2008
Factors affecting enrollment in literacy studies for English- and Spanish-speaking cancer patients.
Study participation bias can affect inferences regarding outcomes. ⋯ Spanish-speaking patients enrolled at a much higher rate than English-speaking patients, which is encouraging for future research in this underserved population. One important literacy-related factor (education) did not affect enrollment in Spanish-speaking patients, suggesting that there was no selection bias in this study. Recruiting sites with more indigent patients and long clinic waiting times had higher enrollment, suggesting that monetary compensation and time availability may be important considerations in study participation.
-
Statistics in medicine · Aug 2008
A note on the conservativeness of the confidence interval approach for the selection of non-inferiority margin in the two-arm active-control trial.
Compared with placebo-control clinical trials, the interpretation of efficacy results from active-control trials requires more caution. This is because efficacy results from such trials cannot be reliably interpreted without a thorough understanding of the efficacy evidence that formed the basis for the approval of the active control, especially when such drug efficacy is to be established on the basis of clinical evidence from the traditional two-arm active-control clinical equivalence studies as opposed to the multi-arm active control. ⋯ Simulation results are presented to show that the point estimate method provides adequate control of the Type I error rate with > or =75 per cent retention of known active-control effect and that the confidence interval approach is uniformly ultra-conservative. We also report (via a numerical example from real clinical trial data) a couple of potentially less stringent alternative approaches for establishing the non-inferiority of a test drug over a control, which have been used in the past to provide additional efficacy evidence in NDA submission.
-
Statistics in medicine · May 2008
ReviewA critical appraisal of propensity-score matching in the medical literature between 1996 and 2003.
Propensity-score methods are increasingly being used to reduce the impact of treatment-selection bias in the estimation of treatment effects using observational data. Commonly used propensity-score methods include covariate adjustment using the propensity score, stratification on the propensity score, and propensity-score matching. Empirical and theoretical research has demonstrated that matching on the propensity score eliminates a greater proportion of baseline differences between treated and untreated subjects than does stratification on the propensity score. ⋯ Thirteen (28 per cent) of the articles explicitly used statistical methods appropriate for the analysis of matched data when estimating the treatment effect and its statistical significance. Common errors included using the log-rank test to compare Kaplan-Meier survival curves in the matched sample, using Cox regression, logistic regression, chi-squared tests, t-tests, and Wilcoxon rank sum tests in the matched sample, thereby failing to account for the matched nature of the data. We provide guidelines for the analysis and reporting of studies that employ propensity-score matching.