Evaluating the Performance of High-Dimensional Propensity Scores Compared with Standard Propensity Scores for Comparing Antihypertensive Therapies in the CPRD GOLD Database.
IF 3
Q2 CARDIAC & CARDIOVASCULAR SYSTEMS
引用次数: 0
Abstract
Introduction: Propensity score (PS) matching is widely used in medical record studies to create balanced treatment groups, but relies on prior knowledge of confounding factors. High-dimensional PS (hdPS) is a semi-automated algorithm that selects variables with the highest potential for confounding from medical databases. The objective of this study was to evaluate performance of hdPS and PS when used to compare antihypertensive therapies in the UK clinical practice research datalink (CPRD) GOLD database.
Methods: Patients initiating antihypertensive treatment with either monotherapy or bitherapy were extracted from the CPRD GOLD database. Simulated datasets were generated using plasmode simulations with a marginal hazard ratio (HRm) of 1.29 for bitherapy versus monotherapy for reaching blood pressure control at 3 months. Either 16 or 36 known covariates were forced into the PS and hdPS models, and 200 additional variables were automatically selected for hdPS. Sensitivity analyses were conducted to assess the impact of removing known confounders from the database on hdPS performance.
Results: With 36 known covariates, the estimated HRm (RMSE) was 1.31 (0.05) for hdPS and 1.30 (0.04) for PS matching; the crude HR was 0.68 (0.61). Using 16 known covariates, the estimated HRm (RMSE) was 1.23 (0.10) and 1.09 (0.20) for hdPS and PS, respectively. Performance of hdPS was not compromised when known confounders were removed from the database.
Results on real data: With 49 investigator-selected covariates, the HR was 1.18 (95% CI 1.10; 1.26) for PS and 1.33 (95% CI 1.22; 1.46) for hdPS. Both methods yielded the same conclusion, suggesting superiority of bitherapy over monotherapy for time to blood pressure control.
Conclusion: HdPS can identify proxies for missing confounders, thereby having an advantage over PS in case of unobserved covariates. Both PS and hdPS showed superiority of bitherapy over monotherapy for reaching blood pressure control.
评价CPRD GOLD数据库中高维度倾向评分与标准倾向评分在比较降压治疗中的表现
简介:倾向评分(PS)匹配广泛应用于病历研究，以创建平衡的治疗组，但依赖于混杂因素的先验知识。高维PS (hdPS)是一种半自动算法，它从医学数据库中选择最有可能混淆的变量。本研究的目的是评估hdPS和PS在英国临床实践研究数据链(CPRD) GOLD数据库中用于比较降压治疗的性能。方法:从CPRD GOLD数据库中提取开始接受单药或双药降压治疗的患者。利用等离子体模型模拟生成模拟数据集，生物疗法与单一疗法在3个月达到血压控制的边际风险比(HRm)为1.29。将16或36个已知协变量强制纳入PS和hdPS模型，并自动为hdPS选择200个额外变量。进行敏感性分析以评估从数据库中删除已知混杂因素对hdPS性能的影响。结果:在已知36个协变量的情况下，hdPS的估计HRm (RMSE)为1.31 (0.05)，PS匹配的估计HRm (RMSE)为1.30 (0.04);粗HR为0.68(0.61)。使用16个已知协变量，hdPS和PS的估计HRm (RMSE)分别为1.23(0.10)和1.09(0.20)。当从数据库中删除已知的混杂因素时，hdPS的性能不会受到影响。真实数据的结果:在49个研究者选择的协变量中，风险比为1.18 (95% CI 1.10;PS为1.26)，1.33 (95% CI 1.22;1.46)的hdPS。两种方法都得出了相同的结论，表明生物疗法在时间上优于单一疗法来控制血压。结论:HdPS可以识别缺失混杂因素的代理，因此在未观察到的协变量情况下比PS具有优势。PS和hdPS在达到血压控制方面均显示出生物疗法优于单一疗法。
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来源期刊
Cardiology and Therapy
CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
5.30
自引率
0.00%
发文量
38
审稿时长
6 weeks
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