2021/01/05 by Martijn J. Schuemie, Schuemie, Martijn J., Yong Chen +5 · 1 citation
Mathematics · Psychology · #Advanced Causal Inference Techniques #Mental Health Research Topics #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2101.01551
Studies of the effects of medical interventions increasingly take place in\ndistributed research settings using data from multiple clinical data sources\nincluding electronic health records and administrative claims. In such\nsettings, privacy concerns typically prohibit sharing of individual patient\ndata, and instead, analyses can only utilize summary statistics from the\nindividual databases. In the specific but very common context of the Cox\nproportional hazards model, we show that standard meta analysis methods then\nlead to substantial bias when outcome counts are small. This bias derives\nprimarily from the normal approximations that the methods utilize. Here we\npropose and evaluate methods that eschew normal approximations in favor of\nthree more flexible approximations: a skew-normal, a one-dimensional grid, and\na custom parametric function that mimics the behavior of the Cox likelihood\nfunction. In extensive simulation studies we demonstrate how these\napproximations impact bias in the context of both fixed-effects and (Bayesian)\nrandom-effects models. We then apply these approaches to three real-world\nstudies of the comparative safety of antidepressants, each using data from four\nobservational healthcare databases.\n