Austin Peter C, Cafri Guy
ICES, Toronto, Ontario, Canada.
Institute of Health Management, Policy and Evaluation, University of Toronto, Toronto, Ontario, Canada.
Stat Med. 2020 May 20;39(11):1623-1640. doi: 10.1002/sim.8502. Epub 2020 Feb 28.
Propensity-score matching is a popular analytic method to estimate the effects of treatments when using observational data. Matching on the propensity score typically requires a pool of potential controls that is larger than the number of treated or exposed subjects. The most common approach to matching on the propensity score is matching without replacement, in which each control subject is matched to at most one treated subject. Failure to find a matched control for each treated subject can lead to "bias due to incomplete matching." To avoid this bias, it is important to identify a matched control subject for each treated subject. An alternative to matching without replacement is matching with replacement, in which control subjects are allowed to be matched to multiple treated subjects. A limitation to the use of matching with replacement is that variance estimation must account for both the matched nature of the sample and for some control subjects being included in multiple matched sets. While a variance estimator has been proposed for when outcomes are continuous, no such estimator has been proposed for use with time-to-event outcomes, which are common in medical and epidemiological research. We propose a variance estimator for the hazard ratio when matching with replacement. We conducted a series of Monte Carlo simulations to examine the performance of this estimator. We illustrate the utility of matching with replacement to estimate the effect of smoking cessation counseling on survival in smokers discharged from hospital with a heart attack.
倾向得分匹配是一种在使用观察性数据时估计治疗效果的常用分析方法。基于倾向得分进行匹配通常需要一组潜在对照，其数量要多于接受治疗或暴露的受试者数量。基于倾向得分进行匹配最常见的方法是无放回匹配，即每个对照受试者最多与一个接受治疗的受试者匹配。未能为每个接受治疗的受试者找到匹配的对照可能会导致“因不完全匹配而产生的偏差”。为避免这种偏差，为每个接受治疗的受试者确定一个匹配的对照受试者很重要。无放回匹配的一种替代方法是有放回匹配，即允许对照受试者与多个接受治疗的受试者匹配。有放回匹配使用的一个限制是方差估计必须同时考虑样本的匹配性质以及一些对照受试者被纳入多个匹配组的情况。虽然已经针对连续结局提出了一种方差估计器，但对于医学和流行病学研究中常见的事件发生时间结局，尚未提出这样的估计器。我们提出了一种用于有放回匹配时风险比的方差估计器。我们进行了一系列蒙特卡罗模拟来检验该估计器的性能。我们举例说明了有放回匹配在估计戒烟咨询对因心脏病发作出院的吸烟者生存影响方面的效用。
