Division of Research, Kaiser Permanente Northern California, CA, USA.
Pharmacoepidemiol Drug Saf. 2012 May;21 Suppl 2:99-113. doi: 10.1002/pds.3253.
Chronic disease care typically involves treatment decisions that are frequently adjusted to the patient's evolving clinical course (e.g., hemoglobin A1c monitoring and treatment intensification in diabetes patients). Thus, in comparative effectiveness and safety research (CER), it often is less clinically relevant to contrast the health effects of static treatment decisions than to compare the effectiveness of competing medical guidelines, that is, adaptive treatment strategies that map the patient's unfolding clinical course to subsequent treatment decisions. With longitudinal observational studies, treatment decisions at any point in time may be influenced by clinical factors that also are risk factors for the outcome of interest. Such time-dependent confounders cannot be properly handled with standard statistical approaches, because such confounders may be influenced by previous treatment decisions and may thus lie on causal pathways between the very outcomes and early treatment decisions whose effects are under study. Under explicit assumptions, we motivate the application of inverse probability weighting estimation to fit dynamic marginal structural models (MSMs) in observational studies to address pragmatic CER questions and properly adjust for time-dependent confounding and informative loss to follow-up.
We review the principles behind this modeling approach and describe its application in an observational study of type 2 diabetes patients to investigate the comparative effectiveness of four adaptive treatment intensification strategies for glucose control on subsequent development or progression of urinary albumin excretion.
Results indicate a protective effect of more aggressive treatment intensification strategies in patients already on two or more oral agents or basal insulin. These conclusions are concordant with recent randomized trials.
Inverse probability weighting estimation to fit dynamic MSM is a viable and appealing alternative to inadequate standard modeling approaches in many CER problems where time-dependent confounding and informative loss to follow-up are expected.
慢性病护理通常涉及治疗决策，这些决策经常根据患者的临床病程进行调整（例如，糖尿病患者的血红蛋白 A1c 监测和治疗强化）。因此，在比较有效性和安全性研究（CER）中，对比静态治疗决策的健康效果通常不如比较竞争医疗指南的有效性更具临床意义，即适应治疗策略，将患者的临床病程映射到后续治疗决策上。在纵向观察性研究中，任何时间点的治疗决策都可能受到临床因素的影响，这些因素也是研究兴趣相关结局的风险因素。此类随时间变化的混杂因素不能通过标准统计方法正确处理，因为这些混杂因素可能受到先前治疗决策的影响，因此可能位于研究中的结局和早期治疗决策之间的因果路径上。在明确的假设下，我们提出了应用逆概率加权估计来拟合观察性研究中的动态边际结构模型（MSM），以解决实际 CER 问题，并正确调整随时间变化的混杂因素和随访信息丢失。
我们回顾了这种建模方法的原理，并描述了它在一项 2 型糖尿病患者观察性研究中的应用，以研究四种适应治疗强化策略对葡萄糖控制的比较有效性，以观察后续发展或进展的尿白蛋白排泄。
结果表明，对于已经使用两种或更多种口服药物或基础胰岛素的患者，更积极的治疗强化策略具有保护作用。这些结论与最近的随机试验一致。
在许多 CER 问题中，当预期存在随时间变化的混杂因素和随访信息丢失时，逆概率加权估计拟合动态 MSM 是一种可行且有吸引力的替代方法，而不是不充分的标准建模方法。
