We propose a flexible regression model to study the association between a functional response and multiple functional covariates that are observed on the same domain. Specifically, we relate the mean of current response to current values of the covariates by sum of smooth unknown bi-variate functions, where each of the functions depends on the current value of the covariate and the time point i...

Estimation of the regression parameters and variance components in a longitudinal mixed model with measurement error in a time-varying covariate is considered. The positive bias in variance estimators caused by covariate measurement error in a normal linear mixed model has recently been identified and studied (Tosteson, Buonaccorsi and Demidenko (1997)). The methods suggested there for correcti...

The potential for bias due to misclassification error in regression analysis is well understood by statisticians and epidemiologists. Assuming little or no available data for estimating misclassification probabilities, investigators sometimes seek to gauge the sensitivity of an estimated effect to variations in the assumed values of those probabilities. We present an intuitive and flexible appr...

We consider the problem of robust estimation of the regression relationship between a response and a covariate based on sample in which precise measurements on the covariate are not available but error-prone surrogates for the unobserved covariate are available for each sampled unit. Existing methods often make restrictive and unrealistic assumptions about the density of the covariate and the d...

Quasi score equations derived from corrected mean and variance functions allow for consistent parameter estimation under measure ment error However the practical use of some approaches relying on this general methodological principle was strongly limited by the assumptions underlying them only one covariate was allowed to be measured with non negligible error and additionally this covariate had...

We evaluate by simulation three model-based methods to test the influence of a single nucleotide polymorphism on a pharmacokinetic parameter of a drug: ANOVA on the empirical Bayes estimates of the individual parameters, likelihood ratio test between models with and without genetic covariate, and Wald tests on the parameters of the model with covariate. Analyses are performed using the FO and F...

Covariate-specific treatment effects (CSTEs) represent heterogeneous across subpopulations defined by certain selected covariates. In this article, we consider marginal structural models where CSTEs are linearly represented using a set of basis functions the We develop new approach in high-dimensional settings to obtain not only doubly robust point estimators CSTEs, but also model-assisted conf...