A Study of Interval Censoring in Parametric Regression Models
 J. K. Lindsey
 … show all 1 hide
Rent the article at a discount
Rent now* Final gross prices may vary according to local VAT.
Get AccessAbstract
Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation.
Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding nonparametric models.
 Title
 A Study of Interval Censoring in Parametric Regression Models
 Journal

Lifetime Data Analysis
Volume 4, Issue 4 , pp 329354
 Cover Date
 19981001
 DOI
 10.1023/A:1009681919084
 Print ISSN
 13807870
 Online ISSN
 15729249
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 AIC
 dispersion regression
 exponential distribution
 finite mixture model
 gamma distribution
 intensity function
 interval censoring
 inverse Gaussian distribution
 log Cauchy distribution
 log Laplace distribution
 log logistic distribution
 log normal distribution
 log Student distribution
 normed profile likelihood
 robustness
 Weibull distribution
 Industry Sectors
 Authors

 J. K. Lindsey ^{(1)}
 Author Affiliations

 1. Biostatistics, Limburgs Universitair Centrum, 3590, Diepenbeek, Belgium