We investigate estimation of causal effects of multiple competing (multi-valued) treatments in the absence of randomization. Our work is motivated by an intention-to-treat study of the relative cardiometabolic risk of assignment to one of six commonly prescribed antipsychotic drugs in a cohort of nearly 39,000 adults adults with serious mental illness. Doubly-robust estimators, such as targeted minimum loss-based estimation (TMLE), require correct specification of either the treatment model or outcome model to ensure consistent estimation; however, common TMLE implementations estimate treatment probabilities using multiple binomial regressions rather than multinomial regression. We implement a TMLE estimator that uses multinomial treatment assignment and ensemble machine learning to estimate average treatment effects. Our multinomial implementation improves coverage, but does not necessarily reduce bias, relative to the binomial implementation in simulation experiments with varying treatment propensity overlap and event rates. Evaluating the causal effects of the antipsychotics on 3-year diabetes risk or death, we find a safety benefit of moving from a second-generation drug considered among the safest of the second-generation drugs to an infrequently prescribed first-generation drug thought to pose a generally low cardiometabolic risk.

翻译：我们探讨了在非随机化条件下估计多重竞争性（多值）治疗因果效应的方法。本研究受一项意向性治疗研究启发，该研究针对近39,000名严重精神疾病成人患者，评估六种常用抗精神病药物中任意一种的相对心血管代谢风险。双稳健估计量（如基于目标最小损失估计TMLE）需正确设定治疗模型或结局模型以确保一致估计；然而，常见TMLE实现通过多重二项式回归而非多项回归估计治疗概率。我们采用基于多项治疗分配与集成机器学习的TMLE估计量来估计平均处理效应。在治疗倾向重叠度和事件发生率变化的模拟实验中，与二项式实现相比，我们的多项实现提高了覆盖率，但未必降低偏倚。通过评估抗精神病药物对三年糖尿病风险或死亡率的因果效应，我们发现从被认为是第二代药物中最安全的药物换用至一种虽较少处方但被认为心血管代谢风险普遍较低的第一代药物时存在安全性获益。