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Bias reduction in effectiveness analyses of longitudinal ordinal doses with a mixed‐effects propensity adjustment
Author(s) -
Leon Andrew C.,
Hedeker Donald,
Teres Jedediah J.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2458
Subject(s) - propensity score matching , statistics , ordered logit , mixed model , econometrics , observational study , mathematics , logistic regression , interaction
A mixed‐effects propensity adjustment is described that can reduce bias in longitudinal studies involving non‐equivalent comparison groups. There are two stages in this data analytic strategy. First, a model of propensity for treatment intensity examines variables that distinguish among subjects who receive various ordered doses of treatment across time using mixed‐effects ordinal logistic regression. Second, the effectiveness model examines multiple times until recurrence to compare the ordered doses using a mixed‐effects grouped‐time survival model. Effectiveness analyses are initially stratified by propensity quintile. Then the quintile‐specific results are pooled, assuming that there is not a propensity × treatment interaction. A Monte Carlo simulation study compares bias reduction in fully specified propensity model relative to misspecified models. In addition, type I error rate and statistical power are examined. The approach is illustrated by applying it to a longitudinal, observational study of maintenance treatment of major depression. Copyright © 2005 John Wiley & Sons, Ltd.