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We develop a flexible Erlang mixture model for survival analysis. The modelfor the survival density is built from a structured mixture of Erlangdensities, mixing on the integer shape parameter with a common scale parameter.The mixture weights are constructed through increments of a distributionfunction on the positive real line, which is assigned a Dirichlet processprior. The model has a relatively simple structure, balancing flexibility withefficient posterior computation. Moreover, it implies a mixture representationfor the hazard function that involves time-dependent mixture weights, thusoffering a general approach to hazard estimation. We extend the model to handlesurvival responses corresponding to multiple experimental groups, using adependent Dirichlet process prior for the group-specific distributions thatdefine the mixture weights. Model properties, prior specification, andposterior simulation are discussed, and the methodology is illustrated withsynthetic and real data examples.

Bayesian Nonparametric Erlang Mixture Modeling for Survival Analysis