Observational studies provide a core resource in assessing post-market drug safety and effectiveness. Propensity scores are a predominant method for confounding adjustment to achieve unbiased estimation of average treatment effects in observational data. However, the use of propensity score methods has been limited to comparing two treatment groups, while medical situations frequently present with multiple treatment options. Inverse probability of treatment weighting (IPTW) is a popular propensity score adjustment method, but its performance degrades with decreased positivity leading to extreme weights, a problem that can be amplified with multiple treatment groups. Meanwhile, regression on a spline of the propensity score has shown favorable performance compared to other propensity score methods in recent studies involving two treatments. This project utilizes a simulation study to compare IPTW and propensity score splines as adjustment methods in a three-treatment setting. We test a variety of spline methods, including natural cubic splines with varying numbers of interior knots, and thin-plate regression splines. We vary several parameters across simulations, including the degree of propensity score overlap among treatment groups, treatment prevalence, outcome prevalence, and true marginal relative risk. We assess methods based on their bias, root mean squared error, and coverage of the true marginal relative risk across simulations. We find that all methods perform similarly well when there is good propensity score distribution overlap. However, with even moderate decrease in overlap or low outcome prevalence, IPTW produces more biased estimates and higher variance than propensity score splines. Low treatment prevalence or unequal treatment prevalences across groups also worsens IPTW performance. Overall, a natural cubic spline with a relatively small number of interior knots provides good performance across a range of simulations.

观察性研究提供了评估售后药品安全性和有效性的核心资源。倾向评分是混淆调整以在观察数据中实现平均治疗效果的无偏估计的主要方法。但是，倾向评分方法的使用仅限于比较两个治疗组，而医疗情况经常出现多种治疗选择。治疗加权的逆概率（IPTW）是一种流行的倾向评分调整方法，但是其性能会随着阳性率的降低而降低，从而导致极端的权重，这个问题可以在多个治疗组中得到解决。同时，在涉及两种治疗方法的最新研究中，与其他倾向评分方法相比，倾向评分样条的回归已显示出良好的性能。该项目利用模拟研究来比较IPTW和倾向得分样条作为三种处理环境中的调整方法。我们测试了多种样条方法，包括具有不同内部结数的自然立方样条和薄板回归样条。我们在模拟中改变了几个参数，包括治疗组之间的倾向得分重叠程度，治疗患病率，结局患病率和真实的边际相对风险。我们根据方法的偏差，均方根误差和模拟中真实边际相对风险的覆盖率来评估方法。我们发现，当倾向得分分布重叠良好时，所有方法的性能均相似。但是，即使重叠出现适度的下降或结果发生率较低，与倾向得分样条曲线相比，IPTW产生更多的偏差估计和更高的方差。各组之间的低治疗流行度或不平等的治疗流行度也使IPTW性能恶化。总体而言，具有内部结相对较少数量的自然三次样条可在一系列模拟中提供良好的性能。