BACKGROUND The number of clusters in a cluster randomized trial is often low. It is therefore likely random assignment of clusters to treatment conditions results in covariate imbalance. There are no studies that quantify the consequences of covariate imbalance in cluster randomized trials on parameter and standard error bias and on power to detect treatment effects. METHODS The consequences ...

An Appendix with proofs and tuning details has been added here. Abstract Classical penalized likelihood regression problems deal with the case that the independent variables data are known exactly. In practice, however, it is common to observe data with incomplete covariate information. We are concerned with a fundamentally important case where some of the observations do not represent the exac...

Studies of chronic diseases routinely sample individuals subject to conditions on an event time of interest. In epidemiology, for example, prevalent cohort studies aiming to evaluate risk factors for survival following onset of dementia require subjects to have survived to the point of screening. In clinical trials designed to assess the effect of experimental cancer treatments on survival, pat...

When modeling covariate measurement error in regression, it is typically assumed that the measurement error acts in an additive or multiplicative manner on the true covariate value. However, such assumptions do not hold for the measurement error of sleep-disordered breathing (SDB). The true covariate is the severity of SDB, and the observed surrogate covariate is the number of breathing pauses ...

Misspecified models and noisy covariate measurements are two common sources of bias in statistical inferences. While there is considerable literature on the consequences of each problem in isolation, this article investigates the effect of both problems in tandem. In the context of linear models, the large-sample error in estimating the regression function is partitioned into two terms, one res...

Covariate adjustment methods are frequently used when baseline covariate information is available for randomized controlled trials. Using a simulation study, we compared the analysis of covariance (ANCOVA) with three nonparametric covariate adjustment methods with respect to point and interval estimation for the difference between means. The three alternative methods were based on important mem...

Within the latent growth curve model, time-invariant covariates are generally modeled on the subject level, thereby estimating the effect of the covariate on the latent growth parameters. Incorporating the time-invariant covariate in this manner may have some advantages regarding the interpretation of the effect but may also be incorrect in certain instances. In this article we discuss a more g...

The difference in restricted mean survival times between two groups is a clinically relevant summary measure. With observational data, there may be imbalances in confounding variables between the two groups. One approach to account for such imbalances is estimating a covariate-adjusted restricted mean difference by modeling the covariate-adjusted survival distribution and then marginalizing ove...

Missing covariate values is a common problem in a survival data research. The aim of this study is to compare the use of the multiple imputation (MI) and last observation carried forward (LOCF) methods for handling missing covariate values in the Cox proportional hazards (PH) regression model. The comparisons between the methods are based on simulated data. The missingness mechanism is assumed ...

A compact graphical device for combining survival and time-varying covariate information is proposed. The proposed graph contains the Kaplan-Meier estimator for right-censored data and a simultaneous display of the behaviour of time-dependent covariate(s) and the lifetime for each subject in the sample. The observed levels of time-dependent covariates are possibly subjected to an initial dimens...