Inference for a linear regression model with an interval-censored covariate

Interval‐censored observations of a response variable are a common occurrence in medical studies, and usually result when the response is the elapsed time until some event whose occurrence is periodically monitored. In this paper we consider a multivariate regression setting in which the explanatory...

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Bibliographic Details
Published in:Statistics in medicine 2003-02, Vol.22 (3), p.409-425
Main Authors: Gómez, Guadalupe, Espinal, Anna, W. Lagakos, Stephen
Format: Article
Language:English
Subjects:
Adult
Biological and medical sciences
Computer Simulation
Computerized, statistical medical data processing and models in biomedicine
HIV - growth & development
HIV - metabolism
HIV Infections - drug therapy
HIV Infections - virology
HIV Protease Inhibitors - therapeutic use
Humans
Indinavir - therapeutic use
interval censoring
Likelihood Functions
Linear Models
Medical sciences
Medical statistics
Middle Aged
Multivariate Analysis
non-parametric estimation
Recurrence
self-consistency
Statistics as Topic - methods
Viral Load
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Summary:Interval‐censored observations of a response variable are a common occurrence in medical studies, and usually result when the response is the elapsed time until some event whose occurrence is periodically monitored. In this paper we consider a multivariate regression setting in which the explanatory variable is interval censored. Use of an ad hoc method of analysis for such data, such as taking the midpoint of the interval‐censored covariate and applying ordinary least‐squares, is not in general valid. We develop a likelihood approach, together with a two‐step conditional algorithm, to jointly estimate the regression coefficients as well as the marginal distribution of the covariate. The resulting estimators are asymptotically normal. The performance of the method is assessed via simulations, and illustrated using data from a recent HIV/AIDS clinical trial to assess the association between waiting time between indinavir failure and subsequent viral load at enrolment. Extensions of the procedure to other parametric distributions are discussed. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1326