Abstract：
OBJECTIVE: The use of secondary databases has become popular for evaluating the effectiveness and safety of interventions in real-life settings. However, the absence of important confounders in these databases is challenging. To address this issue, the high-dimensional propensity score (hdPS) algorithm was developed in 2009. This algorithm uses proxy variables for mitigating confounding by combining information available across several healthcare dimensions. This study assessed the methodology and reporting of the hdPS in comparative effectiveness and safety research.
METHODS: In this methodological review, we searched PubMed and Google Scholar from July 2009 to May 2022 for studies that used the hdPS for evaluating the effectiveness or safety of healthcare interventions. Two reviewers independently extracted study characteristics and assessed how the hdPS was applied and reported. Risk of bias was evaluated with the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool.
RESULTS: In total, 136 studies met the inclusion criteria; the median publication year was 2018 (Q1-Q3 2016-2020). The studies included 192 datasets, mostly North American databases (n = 132, 69%). The hdPS was used in primary analysis in 120 studies (88%). Dimensions were defined in 101 studies (74%), with a median of 5 (Q1-Q3 4-6) dimensions included. A median of 500 (Q1-Q3 200-500) empirically identified covariates were selected. Regarding hdPS reporting, only 11 studies (8%) reported all recommended items. Most studies (n = 81, 60%) had a moderate overall risk of bias.
CONCLUSIONS: There is room for improvement in the reporting of hdPS studies, especially regarding the transparency of methodological choices that underpin the construction of the hdPS.
METHODS: In this methodological review, we searched PubMed and Google Scholar from July 2009 to May 2022 for studies that used the hdPS for evaluating the effectiveness or safety of healthcare interventions. Two reviewers independently extracted study characteristics and assessed how the hdPS was applied and reported. Risk of bias was evaluated with the Risk Of Bias In Non-randomised Studies - of Interventions (ROBINS-I) tool.
RESULTS: In total, 136 studies met the inclusion criteria; the median publication year was 2018 (Q1-Q3 2016-2020). The studies included 192 datasets, mostly North American databases (n = 132, 69%). The hdPS was used in primary analysis in 120 studies (88%). Dimensions were defined in 101 studies (74%), with a median of 5 (Q1-Q3 4-6) dimensions included. A median of 500 (Q1-Q3 200-500) empirically identified covariates were selected. Regarding hdPS reporting, only 11 studies (8%) reported all recommended items. Most studies (n = 81, 60%) had a moderate overall risk of bias.
CONCLUSIONS: There is room for improvement in the reporting of hdPS studies, especially regarding the transparency of methodological choices that underpin the construction of the hdPS.
摘要：
目的：在现实生活中评估干预措施的有效性和安全性时，使用二级数据库已变得很流行。然而,这些数据库中缺乏重要的混杂因素是具有挑战性的。为了解决这个问题，高维倾向评分（hdPS）算法于2009年开发。该算法使用代理变量通过组合跨多个医疗保健维度的可用信息来减轻混淆。本研究评估了hdPS在比较有效性和安全性研究中的方法和报告。
方法：在本方法学综述中，我们在2009年7月至2022年5月的PubMed和GoogleScholar中搜索了使用hdPS评估医疗保健干预措施有效性或安全性的研究.两名评审员独立提取了研究特征，并评估了hdPS的应用和报告方式。使用ROBINS-I工具评估偏倚风险。
结果：总计，136项研究符合纳入标准；中位发表年份为2018年（2016年第一季度至2020年第三季度）。这些研究包括192个数据集，主要是北美数据库（n=132，69%）。在120项研究(88%)中,hdPS用于主要分析。在101项研究中定义了维度（74%），包括5个（Q1-Q34-6）维度的中位数。选择了500个(Q1-Q3200-500)经验识别的协变量的中位数。关于HDPS报告，只有11项研究(8%)报告了所有推荐项目.大多数研究（n=81，60％）的总体偏倚风险中等。
结论：hdPS研究的报告还有改进的空间，特别是关于支撑HDPS构建的方法选择的透明度。
方法：在本方法学综述中，我们在2009年7月至2022年5月的PubMed和GoogleScholar中搜索了使用hdPS评估医疗保健干预措施有效性或安全性的研究.两名评审员独立提取了研究特征，并评估了hdPS的应用和报告方式。使用ROBINS-I工具评估偏倚风险。
结果：总计，136项研究符合纳入标准；中位发表年份为2018年（2016年第一季度至2020年第三季度）。这些研究包括192个数据集，主要是北美数据库（n=132，69%）。在120项研究(88%)中,hdPS用于主要分析。在101项研究中定义了维度（74%），包括5个（Q1-Q34-6）维度的中位数。选择了500个(Q1-Q3200-500)经验识别的协变量的中位数。关于HDPS报告，只有11项研究(8%)报告了所有推荐项目.大多数研究（n=81，60％）的总体偏倚风险中等。
结论：hdPS研究的报告还有改进的空间，特别是关于支撑HDPS构建的方法选择的透明度。
