Using audit information to adjust parameter estimates for data errors in clinical trials July 30, 2012

Audits are often performed to assess the quality of clinical trial data, but beyond detecting fraud or sloppiness, the audit data are generally ignored. In an earlier study, using data from a nonrandomized study, Shepherd and Yu developed statistical methods to incorporate audit results into study estimates and demonstrated that audit data could be used to eliminate bias.

Purpose In this article, we examine the usefulness of audit-based error-correction methods in clinical trial settings where a continuous outcome is of primary interest.

Methods We demonstrate the bias of multiple linear regression estimates in general settings with an outcome that may have errors and a set of covariates for which some may have errors and others, including treatment assignment, are recorded correctly for all subjects. We study this bias under different assumptions, including independence between treatment assignment, covariates, and data errors (conceivable in a double-blinded randomized trial) and independence between treatment assignment and covariates but not data errors (possible in an unblinded randomized trial). We review moment-based estimators to incorporate the audit data and propose new multiple imputation estimators. The performance of estimators is studied in simulations. Read more