Structural Nested Mean Models for Assessing Time-Varying Effect Moderation
نویسندگان
چکیده
منابع مشابه
Structural nested mean models for assessing time-varying effect moderation.
This article considers the problem of assessing causal effect moderation in longitudinal settings in which treatment (or exposure) is time varying and so are the covariates said to moderate its effect. Intermediate causal effects that describe time-varying causal effects of treatment conditional on past covariate history are introduced and considered as part of Robins' structural nested mean mo...
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Individuals differ in how they respond to a particular treatment or exposure, and social scientists are often interested in understanding how treatment effects are moderated by observed characteristics of individuals. Effect moderation occurs when individual covariates dampen or amplify the effect of some exposure. This article focuses on estimating moderated causal effects in longitudinal sett...
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Coarse structural nested mean models are tools to estimate treatment effects from longitudinal observational data with time-dependent confounding. There is, however, no guidance on how to specify the treatment effect model, and model misspecification can lead to bias. We derive a goodness-of-fit test based on modified overidentification restrictions tests for evaluating a treatment effect model...
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Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across leve...
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ژورنال
عنوان ژورنال: Biometrics
سال: 2009
ISSN: 0006-341X
DOI: 10.1111/j.1541-0420.2009.01238.x