Anova for Longitudinal Data with Missing Values
نویسندگان
چکیده
We carry out an ANOVA analysis to compare multiple treatment effects for longitudinal studies with missing values. The treatment effects are modeled semiparametrically via a partially linear regression which is flexible in quantifying the time effects of treatments. The empirical likelihood is employed to formulate nonparametric ANOVA tests for treatment effects with respect to covariates and the nonparametric time-effect functions. The proposed tests can be readily modified for ANOVA tests when the underlying regression model for the longitudinal study is either parametric or nonparametric. The asymptotic distributions of the ANOVA test statistics are derived. A bootstrap procedure is proposed to improve the ANOVA test for the time-effect functions. We analyze an HIV-CD4 data set and compare the effects of four treatments.
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