Center for Population Health Sciences, Stanford University, Palo Alto, California.
Center for Primary Care and Outcomes Research, Department of Medicine, School of Medicine, Stanford University, Palo Alto, California.
Am J Epidemiol. 2019 Jul 1;188(7):1345-1354. doi: 10.1093/aje/kwz093.
Matching methods are assumed to reduce the likelihood of a biased inference compared with ordinary least squares (OLS) regression. Using simulations, we compared inferences from propensity score matching, coarsened exact matching, and unmatched covariate-adjusted OLS regression to identify which methods, in which scenarios, produced unbiased inferences at the expected type I error rate of 5%. We simulated multiple data sets and systematically varied common support, discontinuities in the exposure and/or outcome, exposure prevalence, and analytical model misspecification. Matching inferences were often biased in comparison with OLS, particularly when common support was poor; when analysis models were correctly specified and common support was poor, the type I error rate was 1.6% for propensity score matching (statistically inefficient), 18.2% for coarsened exact matching (high), and 4.8% for OLS (expected). Our results suggest that when estimates from matching and OLS are similar (i.e., confidence intervals overlap), OLS inferences are unbiased more often than matching inferences; however, when estimates from matching and OLS are dissimilar (i.e., confidence intervals do not overlap), matching inferences are unbiased more often than OLS inferences. This empirical "rule of thumb" may help applied researchers identify situations in which OLS inferences may be unbiased as compared with matching inferences.
匹配方法被认为可以降低有偏推断的可能性，与普通最小二乘法（OLS）回归相比。我们通过模拟，比较了倾向评分匹配、粗糙精确匹配和未匹配协变量调整的 OLS 回归的推断，以确定哪种方法在何种情况下，以预期的 5%的Ⅰ型错误率产生无偏推断。我们模拟了多个数据集，并系统地改变了共同支持、暴露和/或结果的不连续性、暴露的普遍性以及分析模型的误指定。与 OLS 相比，匹配推断往往存在偏差，尤其是在共同支持较差的情况下；当分析模型正确指定且共同支持较差时，倾向评分匹配的Ⅰ型错误率为 1.6%（统计效率低下），粗糙精确匹配的Ⅰ型错误率为 18.2%（高），OLS 的Ⅰ型错误率为 4.8%（预期）。我们的结果表明，当匹配和 OLS 的估计值相似（即置信区间重叠）时，OLS 推断比匹配推断更无偏；然而，当匹配和 OLS 的估计值不同时（即置信区间不重叠），匹配推断比 OLS 推断更无偏。这种经验性的“经验法则”可能有助于应用研究人员确定在哪些情况下 OLS 推断可能比匹配推断更无偏。
