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Journal of the American Statistical Association Volume 93, 1998 - Issue 444
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Theory and Methods

Nonparametric Regression Analysis of Longitudinal Data

Joan G. Staniswalis Department of Mathematical Sciences, University of Texas, El Paso, TX, 79968–0514, USA
&
J. Jack Lee Department of Biomathematics, M.D. Anderson Cancer Center, University of Texas, Houston, TX, 77030, USA
Pages 1403-1418 | Received 01 Feb 1997, Published online: 17 Feb 2012
 
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Abstract

Nonparametric methods are developed for estimating the dose effect when a response consists of correlated observations over time measured in a dose–response experiment. The methods can also be applied to data collected from a completely randomized design experiment. Methods are developed for the detection and description of the effects of dose, time, and their interaction. The methods allow for individual variation in the timing and number of observations. A generalization allowing baseline covariates to be incorporated is addressed. These results may be used in an exploratory fashion in the process of building a random-effects model for longitudinal data.

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