Suarez David, Haro Josep Maria, Novick Diego, Ochoa Susana
Research & Development Unit, Sant Joan de Déu-SSM, Fundació Sant Joan de Déu, RETICS RD06/0011(REM-TAP Network), Sant Boi, Barcelona, Spain.
J Clin Epidemiol. 2008 Jun;61(6):525-30. doi: 10.1016/j.jclinepi.2007.11.007.
We review marginal structural models (MSMs) and show how they are useful when comparing the effects of multiple treatments on outcomes in observational studies. Until now, MSMs have not been used to compare the effects of more than two treatments.
To illustrate the application of MSMs when patients may receive several treatments, we have reanalyzed the effects of antipsychotic medication on achieving remission in schizophrenia using data from the SOHO study, a 3-year observational study of health outcomes associated with the treatment of schizophrenia.
The MSM results were, in general, consistent with but less statistically significant than those obtained using conventional methods. The MSM also showed qualitative differences in some comparisons in which the conventional analysis obtained results that were not consistent with previous knowledge.
MSMs can be used to analyze multiple treatment effects. MSMs, by using inverse-probability of treatment weights, might provide a better control for confounding than conventional methods by improving the adjustment for treatment group differences in observational studies, which may approximate their results to those of randomized controlled trials.
我们回顾边际结构模型（MSMs），并展示其在观察性研究中比较多种治疗对结局的影响时如何发挥作用。到目前为止，MSMs尚未用于比较两种以上治疗的效果。
为了说明当患者可能接受多种治疗时MSMs的应用，我们使用SOHO研究的数据重新分析了抗精神病药物对精神分裂症缓解的影响，SOHO研究是一项为期3年的关于精神分裂症治疗相关健康结局的观察性研究。
总体而言，MSMs的结果与使用传统方法获得的结果一致，但统计学显著性较低。MSMs在一些比较中也显示出定性差异，在这些比较中，传统分析得到的结果与先前的知识不一致。
MSMs可用于分析多种治疗效果。通过使用治疗权重的逆概率，MSMs可能比传统方法更好地控制混杂因素，通过改善观察性研究中治疗组差异的调整，这可能使它们的结果接近随机对照试验的结果。
