ANOVA for longitudinal data with missing values

Anova for Longitudinal Data with Missing Values

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 t...

Robust within Groups Anova: Dealing with Missing Values

The paper considers the problem of testing the hypothesis that J ≥ 2 dependent groups have equal population measures of location when using a robust estimator and there are missing values. For J = 2, methods have been studied based on trimmed means. But the methods are not readily extended to the case J > 2. Here, two alternative test statistics were considered, one of which performed poorly in...

Robust likelihood-based analysis of longitudinal data with missing values

Regression imputation of missing values in longitudinal data sets.

A stand-alone, menu-driven PC program, written in GAUSS, which can be used to estimate missing observations in longitudinal data sets is described and male available to interested readers. The program is limited to the situation in which we have complete data on N cases at each of the planned times of measurement t1, t2,..., tT; and we wish to use this information, together with the non-missing...

Multiple Partial Imputation for Longitudinal Data with Missing Values in Clinical Trials