Abstract
AIM: Evaluate performance of analytical strategies commonly used to adjust for baseline differences in continuous outcome variables for comparative effectiveness studies. PATIENTS & METHODS: Data simulations resembling a comparison of HbA1c values after initiation of antidiabetic treatments adjusting for baseline HbA1c. We evaluated change scores, analyses of covariance including linear, nonlinear with/without robust variance estimations, before and after optimal matching. We also evaluated the impact of measurement error. RESULTS: With increasing HbA1c baseline differences between groups, bias in effect estimates and suboptimal CI coverage probabilities increased in all approaches. These issues were further compounded by measurement error. Matching on baseline HbA1c, substantially mitigated these issues. CONCLUSION: In comparative studies with continuous outcomes, matching on baseline values of the outcome variable improves analytical performance.