A number of statistical textbooks recommend using an analysis of covariance (ANCOVA) to control for the effects of extraneous factors that might influence the dependent measure of interest. However, it is not generally recognized that serious problems of interpretation can arise when the design contains comparisons of participants sampled from different populations (classification designs). Des...

cervical cancer is the third most common cancer among women in malaysia. the objective of this study was to estimate the effect of explanatory variables on survival time of cervical cancer patients receiving treatment at a hospital in malaysia.in this retrospective record review study, cervical cancer data obtained from hospital universitisains malaysia (husm) was analysed. the data comprises o...

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...

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also...

Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missin...

We describe a design for cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose MTD given patient’s specific dichotomous covariate value. The design is Bayesian adaptive and is an extension of escalation with overdose control EWOC . We will assess the performance of this ...