The SIMEX approach to measurement error correction in meta-analysis with baseline risk as covariate

This paper investigates the use of SIMEX, a simulation based measurement error correction technique, for meta- analysis of studies involving the baseline risk of subjects in the control group as explanatory variable. The approach accounts for the measurement error affecting either the information about the outcome in the treatment group and the baseline risk available from each study, while requiring no assumption about the distribution of the true unobserved baseline risk. This robustness property, together with the feasibility of computation, makes SIMEX very attractive. The approach is suggested as an alternative to the usual likelihood analysis, which can provide misleading inferential results when the commonly assumed normal distribution for the baseline risk is violated. The performance of SIMEX is compared to the likelihood method and to the moment based correction through an extensive simulation study and the analysis of two datasets from the medical literature.