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Log-Logistic Regression Models for Survival Data

Steve Bennett
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 32, No. 2 (1983), pp. 165-171
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2347295
Stable URL: http://www.jstor.org/stable/2347295
Page Count: 7
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Log-Logistic Regression Models for Survival Data
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Abstract

The log-logistic distribution has a non-monotonic hazard function which makes it suitable for modelling some sets of cancer survival data. A log-logistic regression model is described in which the hazard functions for separate samples converge with time. This also provides a linear model for the log odds on survival by any chosen time. The model is fitted on GLIM and an example is given of its use with lung cancer survival data.

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