Simon Virginie, Vadel Jade
Global Real World Evidence, Institut de Recherches Internationales Servier (IRIS), Suresnes, France.
Cardiol Ther. 2023 Jun;12(2):393-408. doi: 10.1007/s40119-023-00316-7. Epub 2023 May 5.
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.
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.
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.
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.
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.
倾向评分(PS)匹配在病历研究中被广泛用于创建平衡的治疗组,但依赖于对混杂因素的先验知识。高维倾向评分(hdPS)是一种半自动算法,可从医学数据库中选择具有最高混杂可能性的变量。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数据库中用于比较抗高血压治疗时的性能。
从CPRD GOLD数据库中提取开始使用单一疗法或联合疗法进行抗高血压治疗的患者。使用模式模拟生成模拟数据集,联合疗法与单一疗法在3个月时达到血压控制的边际风险比(HRm)为1.29。将16个或36个已知协变量强制纳入PS和hdPS模型,并为hdPS自动选择另外200个变量。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。
对于36个已知协变量,hdPS的估计HRm(均方根误差)为1.31(0.05),PS匹配为1.30(0.04);粗风险比为0.68(0.61)。对于16个已知协变量,hdPS和PS的估计HRm(均方根误差)分别为1.23(0.10)和1.09(0.20)。当从数据库中删除已知混杂因素时,hdPS的性能不受影响。
对于49个研究者选择的协变量,PS的风险比为1.18(95%置信区间1.10;1.26),hdPS为1.33(95%置信区间1.22;1.46)。两种方法得出相同结论,表明联合疗法在血压控制时间方面优于单一疗法。
HdPS可以识别缺失混杂因素的替代变量,因此在存在未观察到的协变量的情况下比PS具有优势。PS和hdPS在达到血压控制方面均显示联合疗法优于单一疗法。