Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-Score Matched Analyses
Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-Score Matched Analyses
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DOI：
10.2202/1557-4679.1146
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发表时间：
2009-01-01
影响因子：
1.2
通讯作者：
Austin, Peter C.
中科院分区：
文献类型：
--
作者：
Austin, Peter C.
倾向得分匹配在医学文献中经常使用，以减少或消除治疗选择偏倚的影响，当使用观察数据估计治疗或暴露对结果的影响时。在倾向分数匹配中，形成倾向分数相似的治疗组和未治疗组。最近对倾向分数匹配使用的系统回顾发现，在估计治疗效果的统计显著性时，绝大多数研究人员忽略了倾向分数匹配样本的匹配性质。我们进行了一系列蒙特卡罗模拟，以检验忽略倾向分数匹配样本的匹配性质对I型错误率、置信区间覆盖率和处理效果方差估计的影响。我们检查了均值、相对风险、比值比、泊松模型的比率比和Cox回归模型的风险比的估计差异。我们证明，与不将匹配纳入分析相比，考虑倾向分数匹配样本的匹配性质往往会导致更接近广告水平的I型错误率。同样，与不考虑匹配的情况相比，考虑样本的匹配性质往往会导致覆盖率更接近名义水平的置信区间。最后，考虑到样本的匹配性质，得出的标准误差估计值比不考虑匹配时更能反映治疗效果的抽样可变性。
Propensity-score matching is frequently used in the medical literature to reduce or eliminate the effect of treatment selection bias when estimating the effect of treatments or exposures on outcomes using observational data. In propensity-score matching, pairs of treated and untreated subjects with similar propensity scores are formed. Recent systematic reviews of the use of propensity-score matching found that the large majority of researchers ignore the matched nature of the propensity-score matched sample when estimating the statistical significance of the treatment effect. We conducted a series of Monte Carlo simulations to examine the impact of ignoring the matched nature of the propensity-score matched sample on Type I error rates, coverage of confidence intervals, and variance estimation of the treatment effect. We examined estimating differences in means, relative risks, odds ratios, rate ratios from Poisson models, and hazard ratios from Cox regression models. We demonstrated that accounting for the matched nature of the propensity-score matched sample tended to result in type I error rates that were closer to the advertised level compared to when matching was not incorporated into the analyses. Similarly, accounting for the matched nature of the sample tended to result in confidence intervals with coverage rates that were closer to the nominal level, compared to when matching was not taken into account. Finally, accounting for the matched nature of the sample resulted in estimates of standard error that more closely reflected the sampling variability of the treatment effect compared to when matching was not taken into account.