Linear mixed effects models under inequality constraints with applications.
Constraints arise naturally in many scientific experiments/studies such as in, epidemiology, biology, toxicology, etc. and often researchers ignore such information when analyzing their data and use standard methods such as the analysis of variance (ANOVA). Such methods may not only result in a loss of power and efficiency in costs of experimentation but also may result poor interpretation of the data. In this paper we discuss constrained statistical inference in the context of linear mixed effects models that arise naturally in many applications, such as in repeated measurements designs, familial studies and others. We introduce a novel methodology that is broadly applicable for a variety of constraints on the parameters. Since in many applications sample sizes are small and/or the data are not necessarily normally distributed and furthermore error variances need not be homoscedastic (i.e. heterogeneity in the data) we use an empirical best linear unbiased predictor (EBLUP) type residual based bootstrap methodology for deriving critical values of the proposed test. Our simulation studies suggest that the proposed procedure maintains the desired nominal Type I error while competing well with other tests in terms of power. We illustrate the proposed methodology by re-analyzing a clinical trial data on blood mercury level. The methodology introduced in this paper can be easily extended to other settings such as nonlinear and generalized regression models.
Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Laura
L
Farnan
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States of America.
Anastasia
A
Ivanova
Biostatistics Branch, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, United States of America.
Shyamal D
SD
Peddada
D000704:Analysis of Variance; D000931:Antidotes / Q000627:therapeutic use; D002614:Chelating Agents / Q000627:therapeutic use; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D008628:Mercury / Q000097:blood*; D008630:Mercury Poisoning / Q000097:blood* / Q000188:drug therapy; D012107:Research Design / Q000706:statistics & numerical data*; D004113:Succimer / Q000627:therapeutic use
D000931:Antidotes; D002614:Chelating Agents; D004113:Succimer; D008628:Mercury