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A SEMIPARAMETRIC HIERARCHICAL METHOD FOR A REGRESSION MODEL WITH AN INTERVAL‐CENSORED COVARIATE
Author(s) -
Calle M. Luz,
Gómez Guadalupe
Publication year - 2005
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/j.1467-842x.2005.00400.x
Subject(s) - covariate , mathematics , statistics , dirichlet distribution , semiparametric regression , interval (graph theory) , bayesian probability , econometrics , semiparametric model , confidence interval , regression analysis , parametric statistics , mathematical analysis , combinatorics , boundary value problem
Summary A Bayesian framework is proposed for analysing regression models in which one of the covariates is interval‐censored. Such a situation was encountered in an AIDS clinical trial in which the goal was to examine the association between delays in initiating a new treatment after Indinavir failure and the subsequent viral load level of patients at the time of enrolment into the new treatment. The new method uses a mixture of Dirichlet processes allowing all the components in the model to be specified parametrically, except for the distribution of the interval‐censored covariate, which is treated non‐parametrically. The paper explains the proposed method for the linear regression model in detail. The performance of the method is assessed by simulations and illustrated using the AIDS clinical trial.