Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes
Dynamic marginal structural modeling to evaluate the comparative effectiveness of more or less aggressive treatment intensification strategies in adults with type 2 diabetes
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DOI：
10.1002/pds.3253
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发表时间：
2012-05-01
影响因子：
2.6
通讯作者：
Selby, Joe V.
中科院分区：
文献类型：
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作者：
Neugebauer, Romain;Fireman, Bruce;Selby, Joe V.
目的慢性病护理通常涉及治疗决策，这些决策经常根据患者不断发展的临床过程进行调整（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影响通常不如比较竞争性医疗指南的有效性具有临床相关性，即适应性治疗策略将患者的临床过程映射到随后的治疗决策。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，这些因素也是关注结局的风险因素。这种时间依赖性混杂因素不能用标准统计方法正确处理，因为这种混杂因素可能受到先前治疗决策的影响，因此可能存在于非常结果和早期治疗决策（其影响正在研究中）之间的因果通路上。在明确的假设下，我们鼓励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），以解决务实的CER问题，并适当调整随时间变化的混杂因素和信息丢失。方法我们回顾了这种建模方法背后的原理，并描述了其在2型糖尿病患者观察性研究中的应用，以调查四种适应性强化血糖控制策略对尿白蛋白排泄的后续发展或进展的比较有效性。结果表明，在已经使用两种或两种以上口服药物或基础胰岛素的患者中，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。结论逆概率加权估计，以适应动态MSM是一个可行的和有吸引力的替代不充分的标准建模方法在许多CER的问题，时间依赖性的混杂和信息损失，以后续的预期。版权所有（C）2012约翰威利父子有限公司
Purpose 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. Methods 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 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. Conclusions 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. Copyright (C) 2012 John Wiley & Sons, Ltd.