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The preponderance of large-scale healthcare databases provide abundantopportunities for comparative effectiveness research. Evidence necessary tomaking informed treatment decisions often relies on comparing effectiveness ofmultiple treatment options on outcomes of interest observed in a small numberof individuals. Causal inference with multiple treatments and rare outcomes isa subject that has been treated sparingly in the literature. This paper designsthree sets of simulations, representative of the structure of our healthcaredatabase study, and propose causal analysis strategies for such settings. Weinvestigate and compare the operating characteristics of three types of methodsand their variants: Bayesian Additive Regression Trees (BART), regressionadjustment on multivariate spline of generalized propensity scores (RAMS) andinverse probability of treatment weighting (IPTW) with multinomial logisticregression or generalized boosted models. Our results suggest that BART andRAMS provide lower bias and mean squared error, and the widely used IPTWmethods deliver unfavorable operating characteristics. We illustrate themethods using a case study evaluating the comparative effectiveness ofrobotic-assisted surgery, video-assisted thoracoscopic surgery and openthoracotomy for treating non-small cell lung cancer.

Estimation of causal effects of multiple treatments in healthcare database studies with rare outcomes