Avoiding bias and incorrect confidence interval coverage in prescription drug labeling.

Abstract

The primary purpose of prescription drug labeling is to give healthcare professionals the information needed to prescribe drugs appropriately. Therefore, labeling typically reports the effects that the treatment might be expected to have on several efficacy measures, including not only the primary endpoint used to establish effectiveness but also a number of key secondary endpoints that are important to practitioners and patients. One possible regulatory approach to drug labeling is to include results on important secondary efficacy endpoints in labeling only if there is statistical evidence of a treatment effect and a clinically meaningful estimated effect. We evaluate the statistical consequences of this approach by deriving and discussing the potential bias in point estimates and deviation from nominal coverage in confidence intervals that are reported in labeling. Such an approach can lead to substantial conditional bias in point estimates (toward spuriously greater effects than the truth) and undercoverage in confidence intervals. These statistical properties may have important and undesirable regulatory and public health implications. We discuss an alternative approach to include results in labeling for a selected set of reliably ascertained, clinically important endpoints whether or not there is evidence of a treatment effect.