Appendix: Visualizing the target estimand in comparative effectiveness studies with multiple treatments

These are peer-reviewed supplementary materials for the article 'Visualizing the target estimand in comparative effectiveness studies with multiple treatments' published in the Journal of Comparative Effectiveness Research.Data-generating mechanismAdditional resultsAdditional Multiple Sclerosis Case Study Methods and ResultsAdditional methodsCohort DefinitionDetails on baseline covariatesMissing data imputationMatchingAdditional resultsMissing data patternBaseline Characteristics Before ImputationBaseline characteristics after imputationAdditional bivariate ellipsesTreatment effect heterogeneity explorationAssessment of matching implementationJoy plotsReferencesAim: Comparative effectiveness research using real-world data often involves pairwise propensity score matching to adjust for confounding bias. We show that corresponding treatment effect estimates may have limited external validity, and propose two visualization tools to clarify the target estimand. Materials & methods: We conduct a simulation study to demonstrate, with bivariate ellipses and joy plots, that differences in covariate distributions across treatment groups may affect the external validity of treatment effect estimates. We showcase how these visualization tools can facilitate the interpretation of target estimands in a case study comparing the effectiveness of teriflunomide (TERI), dimethyl fumarate (DMF) and natalizumab (NAT) on manual dexterity in patients with multiple sclerosis. Results: In the simulation study, estimates of the treatment effect greatly differed depending on the target population. For example, when comparing treatment B with C, the estimated treatment effect (and respective standard error) varied from -0.27 (0.03) to -0.37 (0.04) in the type of patients initially receiving treatment B and C, respectively. Visualization of the matched samples revealed that covariate distributions vary for each comparison and cannot be used to target one common treatment effect for the three treatment comparisons. In the case study, the bivariate distribution of age and disease duration varied across the population of patients receiving TERI, DMF or NAT. Although results suggest that DMF and NAT improve manual dexterity at 1 year compared with TERI, the effectiveness of DMF versus NAT differs depending on which target estimand is used. Conclusion: Visualization tools may help to clarify the target population in comparative effectiveness studies and resolve ambiguity about the interpretation of estimated treatment effects.

本数据集为发表于《比较有效性研究杂志》的论文《在多种治疗方案比较有效性研究中可视化目标估计量》的同行评审补充材料。数据生成机制、额外结果、额外的多发性硬化症案例研究方法与结果、额外方法、队列定义、基线协变量的详细信息、缺失数据插补、匹配、额外结果、缺失数据模式、插补前的基线特征、插补后的基线特征、额外的双变量椭圆、治疗效应异质性探索、匹配实施的评估、喜悦图、参考文献。目标：使用真实世界数据进行比较有效性研究时，常常涉及成对倾向得分匹配以调整混杂偏倚。本文展示了相应的治疗效应估计可能具有有限的外部效度，并提出两种可视化工具以阐明目标估计量。材料与方法：通过双变量椭圆和喜悦图，我们进行了一项模拟研究，以证明协变量分布在不同治疗组之间的差异可能影响治疗效应估计的外部效度。在比较teriflunomide（TERI）、dimethyl fumarate（DMF）和natalizumab（NAT）对多发性硬化症患者手部灵巧性疗效的案例研究中，我们展示了这些可视化工具如何有助于解释目标估计量。结果：在模拟研究中，治疗效应的估计值根据目标人群的差异而显著不同。例如，当比较治疗B与C时，在最初接受治疗B和C的患者中，估计的治疗效应（及其相应的标准误）从-0.27（0.03）变化到-0.37（0.04）。匹配样本的可视化揭示了每个比较的协变量分布均不同，不能用于针对三种治疗比较的共同治疗效应。在案例研究中，年龄和疾病病程的双变量分布在不同接受TERI、DMF或NAT治疗的患病人群之间有所变化。尽管结果提示DMF和NAT在1年后与TERI相比可以改善手部灵巧性，但DMF与NAT之间的有效性取决于所使用的目标估计量。结论：可视化工具可能有助于阐明比较有效性研究中的目标人群，并解决对估计治疗效应解释的模糊性。