BACKGROUND Treatment by covariate interactions can be explored in reviews using interaction analyses (e.g., subgroup analysis). Such analyses can provide information on how the covariate modifies the treatment effect and is an important methodological approach for personalising medicine. Guidance exists regarding how to apply such analyses but little is known about whether authors follow the gu...

A fundamental task in the analysis of datasets with many variables is screening for associations. This can be cast as a multiple testing task, where objective achieving high detection power while controlling type I error. We consider $m$ hypothesis tests represented by pairs $((P_i, X_i))_{1\leq i \leq m}$ p-values $P_i$ and covariates $X_i$, such that $P_i \perp X_i$ if $H_i$ null. Here, we sh...

Three estimators are proposed for the regression coefficients in Poisson regression model which the covariates are measured with error. The measurement errors are assumed to be normally distributed, while the correlation coefficient between the latent covariate and the observe covariate is assumed to be known. The adjusted estimator is obtained by adjusting the naive estimator without consideri...

We propose Bayesian inference in hazard regression models where the baseline hazard is unknown, covariate e¤ects are possibly agevarying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework incorporates a wide variety of order restrictions on covariate dependence and duration dependence (ageing). We propose estimation and evaluation of age-varying c...

The authors examine some recently proposed criteria for determining when to adjust for covariates related to misclassification, and show these criteria to be incorrect. In particular, they show that when misclassification is present, covariate control can sometimes increase net bias, even when the covariate would have been a confounder under perfect classification, and even if the covariate is ...

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...

We consider a class of semiparametric models for the covariate distribution and missing data mechanism for missing covariate and/or response data for general classes of regression models including generalized linear models and generalized linear mixed models. Ignorable and nonignorable missing covariate and/or response data are considered. The proposed semiparametric model can be viewed as a se...