Residual analysis in linear regression models with an interval-censored covariate.
Residual analysis is a useful class of techniques for the evaluation of the goodness of a fitted model. Checking the underlying assumptions is important since most linear regression estimators require a correctly specified regression function and independent and identically distributed errors to be consistent. For uncensored data, the examination of the residuals of the fitted model is a standard tool for checking whether or not the underlying model assumptions hold. Such analysis has not been widely developed for censored data. Hillis (Statistics in Medicine 1995; 14:2023-2036) developed a residual plot for model checking when the response variable of a linear model is right-censored, and Gomez et al. (Statistics in Medicine 2003; 22:409-425) proposed residuals in models with interval-censored covariates. In this paper, we propose a new definition of residuals for linear models that incorporate interval-censored covariates. This definition can be also applied when the response variable is interval-censored. These new residuals are shown to perform better in model checking than other types of residuals in this context. We illustrate them with a data set from an AIDS clinical trial study.
Topp
Rebekka
R
Fachbereich Statistik, Universität Dortmund, Vogelpothsweg 87, Dortmund 44227, Germany.
Gómez
Guadalupe
G
D019380:Anti-HIV Agents / Q000627:therapeutic use; D004359:Drug Therapy, Combination; D006678:HIV / Q000254:growth & development; D015658:HIV Infections / Q000188:drug therapy; D006801:Humans; D019469:Indinavir / Q000627:therapeutic use; D016013:Likelihood Functions; D016014:Linear Models; D008954:Models, Biological; D015999:Multivariate Analysis; D013997:Time Factors; D017211:Treatment Failure
D019380:Anti-HIV Agents; D019469:Indinavir