Extended hazard regression model for reliability and survival analysis.
We propose an extended hazard regression model which allows the spread parameter to be dependent on covariates. This allows a broad class of models which includes the most common hazard models, such as the proportional hazards model, the accelerated failure time model and a proportional hazards/accelerated failure time hybrid model with constant spread parameter. Simulations based on sub-classes of this model suggest that maximum likelihood performs well even when only small or moderate-size data sets are available and the censoring pattern is heavy. The methodology provides a broad framework for analysis of reliability and survival data. Two numerical examples illustrate the results.
Louzada-Neto
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Department of Statistics, University of Oxford, UK.
D016018:Least-Squares Analysis; D015993:Life Tables; D016013:Likelihood Functions; D016016:Proportional Hazards Models; D016019:Survival Analysis