ESTIMATION OF THE CAUSAL EFFECT OF A TIME-VARYING EXPOSURE ON THE MARGINAL MEAN OF A REPEATED BINARY OUTCOME
Resource
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION v.94 n.447 pp.687-700
Journal
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
Journal Volume
v.94
Journal Issue
n.447
Pages
687-700
Date Issued
1999
Date
1999
Author(s)
Robins, James M.
Greenland, Sander
Hu, Fu-Chang
Abstract
We provide sufficient conditions for estimating from
longitudinal data the causal effect of a time-dependent
exposure or treatment on the marginal probability of
response for a dichotomous outcome. We then show how one can
estimate this effect under these conditions using the g-
computation algorithm of Robins. We also derive the
conditions under which some current approaches to the
analysis of longitudinal data, such as the generalized
estimating equations (GEE) approach of Zeger and Liang, the
feedback model techniques of Liang and Zeger, and within-
subject conditional methods, can provide valid tests and
estimates of causal effects. We use our methods to estimate
the causal effect of maternal stress on the marginal
probability of a child's illness from the Mothers' Stress
and Children's Morbidity data and compare our results with
those previously obtained by Zeger and Liang using a GEE
approach.
Subjects
causal effects
g-computation algorithm
generalized estimating equation
longitudinal data
marginal structural models
Markov chain
Type
journal article