Generalized log-logistic proportional hazard model with applications in survival analysis

Shahedul A. Khan () and Saima K. Khosa
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Shahedul A. Khan: University of Saskatchewan
Saima K. Khosa: University of Saskatchewan

Journal of Statistical Distributions and Applications, 2016, vol. 3, issue 1, 1-18

Abstract: Abstract Proportional hazard (PH) models can be formulated with or without assuming a probability distribution for survival times. The former assumption leads to parametric models, whereas the latter leads to the semi-parametric Cox model which is by far the most popular in survival analysis. However, a parametric model may lead to more efficient estimates than the Cox model under certain conditions. Only a few parametric models are closed under the PH assumption, the most common of which is the Weibull that accommodates only monotone hazard functions. We propose a generalization of the log-logistic distribution that belongs to the PH family. It has properties similar to those of log-logistic, and approaches the Weibull in the limit. These features enable it to handle both monotone and nonmonotone hazard functions. Application to four data sets and a simulation study revealed that the model could potentially be very useful in adequately describing different types of time-to-event data.

Keywords: Cox PH; Log-logistic distribution; Parametric model; Proportional hazard; Semi-parametric model; Time-to-event data; Weibull distribution; Primary 62N01; Secondary 62P10 (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1186/s40488-016-0054-z

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