Robins James M, Hernán Miguel A, Rotnitzky Andrea
Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
Am J Epidemiol. 2007 Nov 1;166(9):994-1002; discussion 1003-4. doi: 10.1093/aje/kwm231. Epub 2007 Sep 17.
Marginal structural models (MSMs) allow estimation of effect modification by baseline covariates, but they are less useful for estimating effect modification by evolving time-varying covariates. Rather, structural nested models (SNMs) were specifically designed to estimate effect modification by time-varying covariates. In their paper, Petersen et al. (Am J Epidemiol 2007;166:985-993) describe history-adjusted MSMs as a generalized form of MSM and argue that history-adjusted MSMs allow a researcher to easily estimate effect modification by time-varying covariates. However, history-adjusted MSMs can result in logically incompatible parameter estimates and hence in contradictory substantive conclusions. Here the authors propose a more restrictive definition of history-adjusted MSMs than the one provided by Petersen et al. and compare the advantages and disadvantages of using history-adjusted MSMs, as opposed to SNMs, to examine effect modification by time-dependent covariates.
边际结构模型(MSMs)能够估计基线协变量的效应修正,但在估计随时间变化的协变量的效应修正方面用处较小。相反,结构嵌套模型(SNMs)是专门为估计随时间变化的协变量的效应修正而设计的。在他们的论文中,彼得森等人(《美国流行病学杂志》2007年;166:985 - 993)将历史调整后的MSMs描述为MSM的一种广义形式,并认为历史调整后的MSMs使研究人员能够轻松估计随时间变化的协变量的效应修正。然而,历史调整后的MSMs可能会导致逻辑上不相容的参数估计,从而得出相互矛盾的实质性结论。在此,作者提出了一个比彼得森等人所提供的定义更为严格的历史调整后的MSMs定义,并比较了使用历史调整后的MSMs而非SNMs来检验随时间变化的协变量的效应修正的优缺点。