Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approaches.
Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approaches.
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DOI：
10.1002/pds.5338
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发表时间：
2021-11
影响因子：
2.6
通讯作者：
Mitra N
中科院分区：
文献类型：
--
作者：
Zhu Y;Hubbard RA;Chubak J;Roy J;Mitra N
在观察性数据的因果分析中，阳性假设要求在每个患者亚组中观察到所有关注的治疗。数据不重叠表明违反了这一假设，即未观察到具有某些协变量组合的患者接受感兴趣的治疗，这可能是由于治疗禁忌症或样本量小。在本文中，我们强调了这一经常被忽视的假设的重要性和影响。此外，我们详细阐述了非重叠对估计和推理构成的挑战，并讨论了之前提出的方法。我们区分结构性违规和实际违规，并深入了解哪些方法适合每种情况。为了证明解决阳性违规时的替代方法和相关考虑因素（包括如何定义重叠以及结果可能推广的目标人群），我们采用电子健康记录衍生数据集来评估二甲双胍对糖尿病患者结肠癌复发的影响。
In the causal analysis of observational data, the positivity assumption requires that all treatments of interest be observed in every patient subgroup. Violations of this assumption are indicated by nonoverlap in the data in the sense that patients with certain covariate combinations are not observed to receive a treatment of interest, which may arise from contraindications to treatment or small sample size. In this paper, we emphasize the importance and implications of this often-overlooked assumption. Further, we elaborate on the challenges nonoverlap poses to estimation and inference and discuss previously proposed methods. We distinguish between structural and practical violations and provide insight into which methods are appropriate for each. To demonstrate alternative approaches and relevant considerations (including how overlap is defined and the target population to which results may be generalized) when addressing positivity violations, we employ an electronic health record-derived data set to assess the effects of metformin on colon cancer recurrence among diabetic patients.
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影响因子：
5.2
作者：
Higurashi T;Nakajima A
通讯作者：
Nakajima A
影响因子：
5.8
作者：
Heckman, JJ;Ichimura, H;Todd, PE
通讯作者：
Todd, PE
影响因子：
5
作者：
Hernan, Miguel A.;Robins, James M.
通讯作者：
Robins, James M.
DOI：
10.1111/rssb.12212
发表时间：
2017-09
期刊：
Journal of the Royal Statistical Society. Series B, Statistical methodology
影响因子：
--
作者：
Kennedy EH;Ma Z;McHugh MD;Small DS
通讯作者：
Small DS
影响因子：
1.8
作者：
Hill, Jennifer;Su, Yu-Sung
通讯作者：
Su, Yu-Sung