Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Operating Rooms, Radboudumc, Nijmegen, The Netherlands
Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Operating Rooms and Health Evidence, Radboudumc, Nijmegen, The Netherlands.
BMJ Open. 2022 Mar 18;12(3):e058977. doi: 10.1136/bmjopen-2021-058977.
To adjust for confounding in observational data, researchers use propensity score matching (PSM), but more advanced methods might be required when dealing with longitudinal data and time-varying treatments as PSM might not include possible changes that occurred over time. This study aims to explore which confounding adjustment methods have been used in longitudinal observational data to estimate a treatment effect and identify potential inappropriate use of PSM.
Mapping review.
We searched PubMed, from inception up to January 2021, for studies in which a treatment was evaluated using longitudinal observational data.
Methodological, non-medical and cost-effectiveness papers were excluded, as were non-English studies and studies that did not study a treatment effect.
Studies were categorised based on time of treatment: at baseline (interventions performed at start of follow-up) or time-varying (interventions received asynchronously during follow-up) and sorted based on publication year, time of treatment and confounding adjustment method. Cumulative time series plots were used to investigate the use of different methods over time. No risk-of-bias assessment was performed as it was not applicable.
In total, 764 studies were included that met the eligibility criteria. PSM (165/201, 82%) and inverse probability weighting (IPW; 154/502, 31%) were most common for studies with a treatment at baseline (n=201) and time-varying treatment (n=502), respectively. Of the 502 studies with a time-varying treatment, 123 (25%) used PSM with baseline covariates, which might be inappropriate. In the past 5 years, the proportion of studies with a time-varying treatment that used PSM over IPW increased.
PSM is the most frequently used method to correct for confounding in longitudinal observational data. In studies with a time-varying treatment, PSM was potentially inappropriately used in 25% of studies. Confounding adjustment methods designed to deal with a time-varying treatment and time-varying confounding are available, but were only used in 45% of the studies with a time-varying treatment.
为了在观察性数据中调整混杂因素，研究人员使用倾向评分匹配（PSM），但在处理纵向数据和随时间变化的治疗时，可能需要更先进的方法，因为 PSM 可能不包括随时间发生的可能变化。本研究旨在探讨在估计治疗效果的纵向观察数据中使用了哪些混杂调整方法，并确定 PSM 的潜在不当使用。
映射综述。
我们在 PubMed 中搜索了从成立到 2021 年 1 月的研究，这些研究使用纵向观察数据评估了一种治疗方法。
排除方法学、非医学和成本效益论文，以及非英语研究和未研究治疗效果的研究。
根据治疗时间将研究进行分类：基线时（在随访开始时进行干预）或随时间变化（在随访期间异步进行干预），并根据发表年份、治疗时间和混杂调整方法进行排序。累积时间序列图用于研究不同方法随时间的使用情况。由于不适用，因此未进行风险偏倚评估。
共有 764 项符合入选标准的研究。PSM（165/201，82%）和逆概率加权（IPW；154/502，31%）分别是治疗在基线时（n=201）和治疗随时间变化时（n=502）的最常用方法。在 502 项随时间变化的治疗研究中，有 123 项（25%）使用了包含基线协变量的 PSM，这可能不恰当。在过去 5 年中，使用 PSM 替代 IPW 的治疗随时间变化的研究比例有所增加。
PSM 是最常用于纠正纵向观察数据中混杂因素的方法。在治疗随时间变化的研究中，25%的研究中可能不恰当地使用了 PSM。可用于处理随时间变化的治疗和随时间变化的混杂因素的混杂调整方法，但仅在 45%的治疗随时间变化的研究中使用。
