Doctoral Dissertation Research: A Comparison of Propensity Score Methods on Simulated Data

博士论文研究：模拟数据上倾向评分方法的比较

• 批准号: 0519288
• 负责人: David Murray
• 金额: $ 0.5万
• 依托单位: University of Memphis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0519288&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Comparison; Propensity

Propensity score analysis is a technique for addressing the problem of selection bias in nonequivalent control group designs. It involves estimating each participant's conditional probability of treatment assignment given his or her vector of observed covariates and using these probabilities (i.e., propensity scores) to balance nonequivalent groups using matching, stratification, covariance adjustment (ANCOVA), weighting, or some combination of these adjustment methods. Most applied research using propensity scores has involved large samples, computing propensity scores via logistic regression, and making analytic adjustments after stratifying on the distribution of propensity scores. Considerably less attention has been paid to other methods of computing propensity scores, like classification trees and bootstrap aggregation or "bagging." However, there is some evidence that the method by which propensity scores are computed impacts the estimates of treatment effects obtained using propensity score analysis. This doctoral dissertation research project will use simulated data to determine the relative performance of three methods of computing estimated propensity scores (logistic regression, classification trees, and bagging) crossed with three types of adjustments that use propensity scores (stratification, covariance adjustment, and weighting).This line of research is important because there are circumstances where it is not feasible for researchers to conduct randomized experiments. In such cases, researchers often resort to quasi-experiments. The problem with quasi-experiments is that the estimates of treatment effects may be biased due to the nonrandom selection process. Researchers must make adjustments to the estimates of treatment effects in an attempt to minimize the selection bias. This study will result in additional guidelines on the use of propensity score methods. Consequently, researchers in various fields may be able to consult the guidelines, employ propensity score methods, and have greater confidence in the accuracy of adjusted estimates of treatment effects from quasi-experiments. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.

倾向评分分析是一种解决非等效对照组设计中选择偏差问题的技术。 它涉及根据每个参与者观察到的协变量向量估计治疗分配的条件概率，并使用这些概率（即倾向得分）通过匹配、分层、协方差调整 (ANCOVA)、加权或这些调整方法的某种组合来平衡非等效组。 大多数使用倾向得分的应用研究都涉及大样本，通过逻辑回归计算倾向得分，并在对倾向得分的分布进行分层后进行分析调整。 对其他计算倾向得分的方法（例如分类树和引导聚合或“装袋”）的关注要少得多。 然而，有一些证据表明，计算倾向得分的方法会影响使用倾向得分分析获得的治疗效果的估计。 本博士论文研究项目将使用模拟数据来确定计算估计倾向得分的三种方法（逻辑回归、分类树和装袋）与使用倾向得分的三种调整类型（分层、协方差调整和加权）的相对性能。这方面的研究很重要，因为在某些情况下研究人员无法进行随机实验。 在这种情况下，研究人员经常诉诸准实验。 准实验的问题在于，由于非随机选择过程，治疗效果的估计可能会出现偏差。 研究人员必须对治疗效果的估计进行调整，以尽量减少选择偏差。 这项研究将产生关于使用倾向评分方法的额外指南。 因此，各个领域的研究人员可以参考指南，采用倾向评分方法，并对准实验治疗效果调整估计的准确性更有信心。 作为博士论文研究改进奖，该奖项还将为有前途的学生建立强大的独立研究生涯提供支持。