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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/3173

Title: A study of interval censoring in parametric regression models
Authors: LINDSEY, James
Issue Date: 1998
Publisher: KLUWER ACADEMIC PUBL
Citation: LIFETIME DATA ANALYSIS, 4(4). p. 329-354
Abstract: 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 non-parametric models.
Notes: Limburgs Univ Ctr, B-3590 Diepenbeek, Belgium.Lindsey, JK, Limburgs Univ Ctr, Univ Campus, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/3173
ISI #: 000077738900002
ISSN: 1380-7870
Type: Journal Contribution
Validation: ecoom, 2000
Appears in Collections: Research publications

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