[What is an effect modifier?].

Repeated significance testing in a sequential experiment not only increases the overall type I error rate of the false positive conclusion but also causes biases in estimating the unknown parameter. In general, the test statistics in a sequential trial can be properly approximated by a Brownian motion with a drift parameter at interim looks. The unadjusted maximum likelihood estimator can be potentially very biased due to the possible early stopping rule at any interim. In this paper, we investigate the conditional and marginal biases with focus on the conditional one upon the stopping time in estimating the Brownian motion drift parameter. It is found that the conditional bias may be very serious for existing point estimation methods, even if the unconditional bias is satisfactory. New conditional estimators are thus proposed, which can significantly reduce the conditional bias from unconditional estimators. The results of Monte-Carlo studies show that the proposed estimators can provide a much smaller conditional bias and MSE than the naive MLE and a Whitebead's bias reduced estimator.

Effect Modifier, Epidemiologic↗

PubMed2004

A measure of effect size for experimental designs with heterogeneous variances.

A recent trend in the psychological literature has been to include measures of effect size when reporting probability values. The several measures of effect size associated with the Student t test for two independent samples are appropriate only when the variances are homogeneous. In this paper, commonly used measures of effect size are considered and compared, using four data sets. A chance-corrected measure of effect size is provided for two or more treatment groups characterized by either homogeneous or heterogeneous variances.

Effect Modifier, Epidemiologic↗