Publications (2)1.88 Total impact
Article: Interval estimation in two-stage, drop-the-losers clinical trials with flexible treatment selection.[show abstract] [hide abstract]
ABSTRACT: In a two-stage, drop-the-losers clinical trial, researchers choose the 'best' among a number of treatments at an interim analysis after the first stage. The selected treatment continues to the second stage for confirmation of efficacy, and the remaining treatments (the 'losers') are dropped from the study. Wu et al. (Biometrika 2010; 97:405-418) showed how to construct confidence limits for the mean difference between the selected treatment and the control when the treatment is chosen after the first stage based on the highest efficacy in the primary clinical endpoint. In this article, we show how to construct a lower confidence limit for the mean difference when the treatment is chosen based on first-stage safety data, early endpoint efficacy data, a combination of safety and efficacy data or any other prespecified selection rule. The result extends the applicability of drop-the-losers designs, for in practice, the 'best' treatment often is not chosen for efficacy alone.Statistics in Medicine 08/2011; 30(23):2804-14. · 1.88 Impact Factor
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ABSTRACT: We consider the problem of estimating a Major League Baseball playerâ€™s batting average in the second half of a season based on his performance in the first half. We fit two linear regression models to playersâ€™ averages from each half of the 2004 season, use these models to predict batting averages in the latter half of 2005 and compare the results to those achieved by three Bayesian estimators considered by Brown (2008). The linear models consistently outperform the Bayesian estimators in terms of four measures of error. Since the regression models use data from 2004 as well as 2005, while Brownâ€™s estimators were based strictly on 2005 data, we also compare the performance of the linear models to that of the Bayesian estimators when the Bayesian estimators are based on the same amount of data. We find the linear models to be superior in this case as well. As a further test, we use the same methods to predict on-base percentages in the last half of the 2005 season, and we find that the linear models again do a better job. While we change the question proposed in Brownâ€™s original paper, our results are a valuable reminder of the power of linear regression.Journal of Quantitative Analysis in Sports 01/2010; 6(3):12-12.