Delaney Joseph A C, Platt Robert W, Suissa Samy
Collaborative Health Studies Coordinating Center, Department of Biostatistics, University of Washington, Seattle, WA 98115, USA.
Eur J Epidemiol. 2009;24(7):343-9. doi: 10.1007/s10654-009-9341-z. Epub 2009 May 6.
We present the results of a Monte Carlo simulation study in which we demonstrate how strong baseline interactions between a confounding variable and a treatment can create an important difference between the marginal effect of exposure on outcome (as estimated by an inverse probability of treatment weighted logistic model) and the conditional effect (as estimated by an adjusted logistic regression model). The scenarios that we explored included one with a rare outcome and a strong and prevalent effect measure modifier where, across 1,000 simulated data sets, the estimates from an adjusted logistic regression model (mean beta = 0.475) and an inverse probability of treatment weighted logistic model (mean beta = 2.144) do not coincide with the known true effect (beta = 0.68925) when the effect measure modifier is not accounted for. When the marginal and conditional estimates do not coincide despite a rare outcome this may suggest that there is heterogeneity in the effect of treatment between individuals. Failure to specify effect measure modification in the statistical model appears to results in systematic differences between the conditional and marginal estimates. When these differences in estimates are observed, testing for or including interactions or non-linear modeling terms may be advised.
我们展示了一项蒙特卡洛模拟研究的结果，在该研究中我们证明了一个混杂变量与一种治疗之间的强基线相互作用如何能够在暴露对结局的边际效应（由治疗加权逻辑回归模型的逆概率估计）和条件效应（由调整后的逻辑回归模型估计）之间产生重要差异。我们探索的情景包括一个具有罕见结局以及一个强且普遍的效应测量修正因素的情景，在1000个模拟数据集中，当不考虑效应测量修正因素时，调整后的逻辑回归模型（平均β = 0.475）和治疗加权逻辑回归模型的逆概率（平均β = 2.144）的估计值与已知的真实效应（β = 0.68925）不一致。当尽管结局罕见但边际估计和条件估计仍不一致时，这可能表明个体之间治疗效果存在异质性。在统计模型中未指定效应测量修正似乎会导致条件估计和边际估计之间出现系统性差异。当观察到这些估计差异时，建议进行交互作用检验或纳入交互作用项或进行非线性建模。
