Estimating a treatment effect from multidimensional longitudinal data.
Multidimensional longitudinal data result when researchers measure an outcome through time that is quantified by many different response variables. These response variables are often defined on different numerical scales. The objective of this paper is to present a method to summarize and estimate an overall treatment effect from this type of longitudinal data. A regression model is proposed that assumes the treatment effect can be parameterized as an acceleration or deceleration of the time scale of each response variable's trajectory. Generalized estimating equations are used to estimate the model parameters. Cognitive and functional ability data from Alzheimer's disease patients and quality of life data from an AIDS clinical trial are used to illustrate the model.
Gray
S M
SM
Department of Biostatistics, Johns Hopkins University, School of Hygiene and Public Health, Baltimore, Maryland 21205, USA. gray@jhu.edu
Brookmeyer
R
R
D000163:Acquired Immunodeficiency Syndrome / Q000523:psychology / Q000628:therapy; D000483:Alleles; D000544:Alzheimer Disease / Q000235:genetics / Q000503:physiopathology / Q000523:psychology; D001057:Apolipoproteins E / Q000235:genetics; D001699:Biometry / Q000379:methods*; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data; D003071:Cognition; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D011788:Quality of Life; D012044:Regression Analysis; D016896:Treatment Outcome
D001057:Apolipoproteins E