Propensity score analysis with partially observed covariates: How should multiple imputation be used?
Propensity score analysis with partially observed covariates: How should multiple imputation be used?
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DOI：
10.1177/0962280217713032
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发表时间：
2019-01
影响因子：
2.3
通讯作者：
Williamson EJ
中科院分区：
文献类型：
--
作者：
Leyrat C;Seaman SR;White IR;Douglas I;Smeeth L;Kim J;Resche-Rigon M;Carpenter JR;Williamson EJ
治疗加权的逆概率是一种流行的基于倾向评分的方法，用于估计观察性研究中存在混杂偏倚风险的边际治疗效果。估计倾向得分时的一个主要问题是存在部分观察到的协变量。多重插补是处理协变量缺失数据的自然方法：插补协变量并在每个插补数据集中执行倾向评分分析以估计治疗效果。然后将每个估算数据集的治疗效果估计值组合起来以获得总体估计值。我们将此方法称为 MIte。然而，人们提出了另一种方法，即在估算数据集 (MIps) 中组合倾向得分。因此，如何实施倾向评分分析的多重插补仍然存在不确定性：（a）我们应该将鲁宾规则应用于治疗加权治疗效果估计的逆概率还是倾向评分估计本身？ (b) 结果是否必须包含在插补模型中？ (c) 多重插补后我们应该如何估计治疗权重估计量的逆概率的方差？我们研究了 MIte 和 MIps 估计器的一致性和平衡特性，并进行了模拟研究，以凭经验评估它们在二元结果分析中的性能。我们还将这些方法的性能与完整案例分析和缺失模式方法进行了比较，该方法对每种缺失模式使用不同的倾向评分模型，以及第三种多重插补方法，其中组合了倾向评分参数而不是倾向评分本身（MIpar）。在随机缺失机制下，在大多数情况下，完整病例和缺失模式分析在估计边际治疗效果时存在偏差，而只要结果包含在插补模型中，多重插补方法就近似无偏。只有 MIte 在所有研究场景中都是无偏的，并且 Rubin 规则为 MIte 提供了良好的方差估计。 MIte 方法估计的倾向得分显示出良好的平衡特性。总之，当在治疗加权的逆概率中使用多重插补时，将结果包含在插补模型中的 MIte 是首选方法。
Inverse probability of treatment weighting is a popular propensity score-based approach to estimate marginal treatment effects in observational studies at risk of confounding bias. A major issue when estimating the propensity score is the presence of partially observed covariates. Multiple imputation is a natural approach to handle missing data on covariates: covariates are imputed and a propensity score analysis is performed in each imputed dataset to estimate the treatment effect. The treatment effect estimates from each imputed dataset are then combined to obtain an overall estimate. We call this method MIte. However, an alternative approach has been proposed, in which the propensity scores are combined across the imputed datasets (MIps). Therefore, there are remaining uncertainties about how to implement multiple imputation for propensity score analysis: (a) should we apply Rubin’s rules to the inverse probability of treatment weighting treatment effect estimates or to the propensity score estimates themselves? (b) does the outcome have to be included in the imputation model? (c) how should we estimate the variance of the inverse probability of treatment weighting estimator after multiple imputation? We studied the consistency and balancing properties of the MIte and MIps estimators and performed a simulation study to empirically assess their performance for the analysis of a binary outcome. We also compared the performance of these methods to complete case analysis and the missingness pattern approach, which uses a different propensity score model for each pattern of missingness, and a third multiple imputation approach in which the propensity score parameters are combined rather than the propensity scores themselves (MIpar). Under a missing at random mechanism, complete case and missingness pattern analyses were biased in most cases for estimating the marginal treatment effect, whereas multiple imputation approaches were approximately unbiased as long as the outcome was included in the imputation model. Only MIte was unbiased in all the studied scenarios and Rubin’s rules provided good variance estimates for MIte. The propensity score estimated in the MIte approach showed good balancing properties. In conclusion, when using multiple imputation in the inverse probability of treatment weighting context, MIte with the outcome included in the imputation model is the preferred approach.
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影响因子：
2
作者：
Hade, Erinn M.;Lu, Bo
通讯作者：
Lu, Bo
影响因子：
2.3
作者：
Bartlett JW;Seaman SR;White IR;Carpenter JR;Alzheimer's Disease Neuroimaging Initiative*
通讯作者：
Alzheimer's Disease Neuroimaging Initiative*
影响因子：
3.7
作者：
D'Agostino, RB;Rubin, DB
通讯作者：
Rubin, DB
DOI：
10.1111/j.1467-9531.2010.01226.x
发表时间：
2010-01-01
期刊：
SOCIOLOGICAL METHODOLOGY, VOL 40
影响因子：
--
作者：
An, Weihua
通讯作者：
An, Weihua
影响因子：
5
作者：
Cole, Stephen R.;Hernan, Miguel A.
通讯作者：
Hernan, Miguel A.