Bias associated with using the estimated propensity score as a regression covariate.
Bias associated with using the estimated propensity score as a regression covariate.
复制标题
DOI：
10.1002/sim.5884
复制
发表时间：
2014-01-15
影响因子：
2
通讯作者：
Lu, Bo
中科院分区：
文献类型：
--
作者：
Hade, Erinn M.;Lu, Bo
在公共卫生和医学研究中，使用倾向评分方法来调整观察性研究中的选择偏差已变得越来越流行。在使用倾向分数调整的研究中，相当一部分将倾向分数视为传统的回归预测因子。通过蒙特卡洛模拟研究，奥斯汀和他的同事。研究了在Logistic回归和COX比例风险模型等非线性回归模型中，当倾向评分作为协变量时，与治疗效果估计相关的偏差。我们证明了即使在线性回归模型中，当使用估计倾向得分时，偏差也是存在的，并推导出偏差的显式形式。我们还进行了大量的模拟研究，以比较这种协变量调整与倾向分数分层、倾向分数匹配、逆治疗概率加权方法以及使用样条法的非参数函数估计的性能。模拟场景旨在反映真实数据分析实践。我们没有指定一个已知的参数倾向得分模型，而是通过考虑治疗组和对照组之间协变量分布的不同程度的重叠来生成数据。当治疗组被包含在更大的控制池中时，倾向性得分匹配表现出色，而基于模型的调整在治疗时可能具有优势，而对照组没有太多重叠。总体而言，通过分层或匹配，然后回归或使用样条法来调整倾向性分数似乎是一个很好的实用策略。
The use of propensity score methods to adjust for selection bias in observational studies has become increasingly popular in public health and medical research. A substantial portion of studies using propensity score adjustment treat the propensity score as a conventional regression predictor. Through a Monte Carlo simulation study, Austin and colleagues. investigated the bias associated with treatment effect estimation when the propensity score is used as a covariate in nonlinear regression models, such as logistic regression and Cox proportional hazards models. We show that the bias exists even in a linear regression model when the estimated propensity score is used and derive the explicit form of the bias. We also conduct an extensive simulation study to compare the performance of such covariate adjustment with propensity score stratification, propensity score matching, inverse probability of treatment weighted method, and nonparametric functional estimation using splines. The simulation scenarios are designed to reflect real data analysis practice. Instead of specifying a known parametric propensity score model, we generate the data by considering various degrees of overlap of the covariate distributions between treated and control groups. Propensity score matching excels when the treated group is contained within a larger control pool, while the model-based adjustment may have an edge when treated and control groups do not have too much overlap. Overall, adjusting for the propensity score through stratification or matching followed by regression or using splines, appears to be a good practical strategy.
登录
查看更多内容
影响因子：
2
作者：
DURRLEMAN, S;SIMON, R
通讯作者：
SIMON, R
影响因子：
5.7
作者：
Hansen, Ben B.;Bowers, Jake
通讯作者：
Bowers, Jake
影响因子：
2
作者：
Austin, Peter C.
通讯作者：
Austin, Peter C.
影响因子：
1.9
作者：
Rubin, DB;Thomas, N
通讯作者：
Thomas, N
DOI：
10.1111/j.1467-9868.2005.00513.x
发表时间：
2005-09-01
影响因子：
5.8
作者：
Rosenbaum, PR
通讯作者：
Rosenbaum, PR