Bias reduction in effectiveness analyses of longitudinal ordinal doses with a mixed-effects propensity adjustment.
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 x 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.
Leon
Andrew C
AC
Department of Psychiatry, Weill Medical College of Cornell University, New York, NY 10021, USA. acleon@med.cornell.edu
Hedeker
Donald
D
Teres
Jedediah J
JJ
D000328:Adult; D000368:Aged; D000928:Antidepressive Agents / Q000008:administration & dosage; D015982:Bias; D001699:Biometry; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003865:Major Depressive Disorder / Q000188:drug therapy; D006801:Humans; D016015:Logistic Models; D008137:Longitudinal Studies; D008875:Middle Aged; D009010:Monte Carlo Method; D016017:Odds Ratio; D016016:Proportional Hazards Models; D016896:Treatment Outcome
D000928:Antidepressive Agents