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Zbl 0986.62094
Robins, James M.
Marginal structural models versus structural nested models as tools for causal inference. (English)
[A] Halloran, M. Elizabeth (ed.) et al., Statistical models in epidemiology, the environment, and clinical trials. IMA summer program on Statistics in the health sciences, Univ. of Minnesota, Minneapolis, MN, USA, 1997. New York, NY: Springer. IMA Vol. Math. Appl. 116, 95-133 (2000). ISBN 0-387-98924-2

Summary: The author [see, e.g., Lect. Notes Stat 120, 69-117 (1997; Zbl 0969.62072); Commun. Stat., Theory Methods 23, No. 8, 2379-2412 (1994; Zbl 0825.62203)] has developed a set of causal or counterfactual models, the structural nested models (SNMs). This paper describes an alternative new class of causal models -- the (non-nested) marginal structural models (MSMs). We will then describe a class of semiparametric estimators for the parameters of these new models under a sequential randomization (i.e., ignorability) assumption. We then compare the strengths and weaknesses of MSMs versus SNMs for causal inference from complex longitudinal data with time-dependent treatments and confounders. Our results provide an extension to continuous treatments of propensity score estimators of an average treatment effect.

MSC 2000:: *62P10 Appl. of statistics to biology
62G05 Nonparametric estimation
62N99 Survival analysis and censored data

Keywords: AIDS clinicial trial group; causal models; marginal structural models; sequential randomization

Citations: Zbl 0969.62072; Zbl 0825.62203

Cited in: Zbl 1233.62072 Zbl 1102.62030

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