Many surveys are often complex cross-sectional studies that involve clustered data. Such can have the additional complexity of measurement error problem. Ignoring problem and clustering aspect may lead to incorrect inferences conclusions. The purpose this study was demonstrate application regression calibration correct for covariate in a survey generalized estimating equations (GEE) framework. ...

Monte Carlo simulation was used to assess the type I error rate and rank order of power for six different metrics using linear mixed-effect models, including two variables recommended by the European Agency for the Evaluation of Medicinal Products (EMEA) in the analysis of QTc interval data. The metrics analyzed were maximal change in QTc interval from baseline, maximal QTc interval, area under...

Meta-analyses that synthesize statistical evidence across studies have become important analytical tools for genetic studies. Inspired by the success of genome-wide association studies of the genetic main effect, researchers are searching for gene × environment interactions. Confounders are routinely included in the genome-wide gene × environment interaction analysis as covariates; however, thi...

An important feature of linear mixed models and generalized linear mixed models is that the conditional mean of the response given the random effects, after transformed by a link function, is linearly related to the fixed covariate effects and random effects. Therefore, it is of practical importance to test the adequacy of this assumption, particularly the assumption of linear covariate effects...

SUMMARY In many problems one wants to model the relationship between a response Y and a covariate X. Sometimes it is diicult, expensive, or even impossible to observe X directly, but one can instead observe a substitute variable W which is easier to obtain. By far the most common model for the relationship between the actual covariate of interest X and the substitute W is W = X + U, where the v...

BACKGROUND AND OBJECTIVE Cox model is a popular model in survival analysis, which assumes linearity of the covariate on the log hazard function, While continuous covariates can affect the hazard through more complicated nonlinear functional forms and therefore, Cox models with continuous covariates are prone to misspecification due to not fitting the correct functional form for continuous covar...

We consider the estimation of a multiple regression model in which the coefficients change slowly in “time”, with “time” being an additional covariate. Under reasonable smoothness conditions, we prove the usual expected mean square error bounds for the smoothing spline estimators of the coefficient functions.