Statistics in medicine
-
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.
-
Traditional designs for phase I clinical trials assign the same dose to patients in the same cohort. In this paper, we present a new class of designs for cancer phase I trials which initially rapidly escalate by allowing multiple doses (usually 3) to be assigned to each cohort of patients. ⋯ Three designs (slow, moderate and fast) are derived based on different dose-escalation restrictions. Simulation results show that moderate and fast LMH-CRM combine the advantages of the CRM with one patient per cohort and three patients per cohort: it accurately estimates the MTD; controls overall toxicity rates; and is time efficient.
-
Statistics in medicine · May 2008
Adaptive dose finding based on t-statistic for dose-response trials.
The goals of phase II dose-response studies are to prove that the treatment is effective and to choose the dose for further development. Randomized designs with equal allocation to either a high dose and placebo or to each of several doses and placebo are typically used. ⋯ We propose an adaptive design for dose-response trials that concentrates the allocation of subjects in one or more areas of interest, for example, near a minimum clinically important effect level, or near some maximal effect level, and also allows for the possibility to stop the trial early if needed. The proposed adaptive design yields higher power to detect a dose-response relationship, higher power in comparison with placebo, and selects the correct dose more frequently compared with a corresponding randomized design with equal allocation to doses.
-
We consider evaluation and comparison of the diagnostic accuracy of biomarkers with continuous test outcomes, possibly correlated due to repeated measurements. We develop nonparametric group sequential testing procedures to evaluate and compare the area of biomarkers under their receiver operating characteristic curves, with either independent or paired test outcomes. These procedures rely on the construction of a two-dimensional statistic of Whitehead (Statist. Med. 1999; 18:2271-2286) so that design methods based on Brownian motion can be applied.