A shared parameter model for the estimation of longitudinal concomitant intervention effects.
We investigate a change-point approach for modeling and estimating the regression effects caused by a concomitant intervention in a longitudinal study. Since a concomitant intervention is often introduced when a patient's health status exhibits undesirable trends, statistical models without properly incorporating the intervention and its starting time may lead to biased estimates of the intervention effects. We propose a shared parameter change-point model to evaluate the pre- and postintervention time trends of the response and develop a likelihood-based method for estimating the intervention effects and other parameters. Application and statistical properties of our method are demonstrated through a longitudinal clinical trial in depression and heart disease and a simulation study.
Office of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA. wuc@nhlbi.nih.gov
Colin O
CO
Wu
Xin
X
Tian
Wenhua
W
Jiang
D000928:Antidepressive Agents / Q000627:therapeutic use; D015982:Bias; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003327:Coronary Disease / Q000628:therapy; D003863:Depression / Q000188:drug therapy; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies / Q000706:statistics & numerical data*; D015233:Models, Statistical; D012044:Regression Analysis
D000928:Antidepressive Agents
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