Analysis of treatment effectiveness in longitudinal observational data
Analysis of treatment effectiveness in longitudinal observational data
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DOI：
10.1080/10543400701513967
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发表时间：
2007-01-01
影响因子：
1.1
通讯作者：
Belger, Mark
中科院分区：
文献类型：
--
作者：
Faries, Douglas;Ascher-Svanum, Haya;Belger, Mark
由于患者可能随时更换药物，因此通过纵向观察数据评估治疗效果很复杂。为了说明这一点，在一项观察性精神分裂症研究中，使用了三种通用统计策略来评估治疗效果：忽略、消除和建模转换。不同的转换率导致不同策略的治疗效果估计存在显着差异，p 值范围从接近 0 到几乎 1。利用意向治疗方法忽略治疗转换导致治疗效果估计接近于零。消除转换的各种方法，例如时代分析和药物子集分析，以及边际结构模型的使用产生了相当一致的非零治疗效果估计。在分析纵向观测数据时，研究人员必须了解各种可用统计方法背后的选项、关键概念和假设。边际结构模型是估计此类数据中因果治疗效果的一种有前途的方法。
Assessing treatment effectiveness in longitudinal observational data is complicated as patients may change medications at any time. To illustrate, three general statistical strategies were utilized to assess treatment effectiveness in an observational schizophrenia study: ignoring, eliminating, and modeling the switching. Differential switching rates produced dramatic differences in estimates of treatment effectiveness across the strategies, with p-values ranging from nearly 0 to almost 1. Ignoring the treatment switching by utilizing intent-to-treat approaches resulted in treatment effect estimates of near zero. Various methods of eliminating the switching, such as epoch analyses and on-drug subset analyses, along with use of marginal structural models generated reasonably consistent non-zero treatment effect estimates. When analyzing longitudinal observational data, researchers must understand the options, key concepts and assumptions behind the various statistical methods available. Marginal structural models are a promising approach to estimation of causal treatment effects in such data.