Semiparametric Efficient Estimation for a Class of Generalized Proportional Odds Cure Models

Semiparametric efficient estimation in the generalized odds-rate class of regression models for right-censored time-to-event data.

The generalized odds-rate class of regression models for time to event data is indexed by a non-negative constant rho and assumes that [formula: see text] where g: rho(s) = log(rho-1(s-rho - 1)) for rho > 0, g0(s) = log(-logs), S(t[symbol: see text]Z) is the survival function of the time to event for an individual with q x 1 covariate vector Z, beta is a q x 1 vector of unknown regression param...

A General Transformation Class of Semiparametric Cure Rate Frailty Models

We consider a class of cure rate frailty models for multivariate failure time data with a survival fraction. This class is formulated through a transformation on the unknown population survival function. It incorporates random effects to account for the underlying correlation, and includes the mixture cure model and the proportional hazards cure model as two special cases. We develop efficient ...

Efficient Inference in Semiparametric Generalized Linear Models

for Y and Φ nonempty subsets of l. The covariate (X,Z) has an unknown joint distribution G ∈ G, a nonempty collection of distributions. Suppose from now on that the true but unknown parameters are (θ, ρ). We are interested in the efficient estimation of the regression parameter θ in the presence of the nuisance parameter γ = (G, ρ) based on the independent and identically distributed observatio...

Analysis of cure rate survival data under proportional odds model.

Due to significant progress in cancer treatments and management in survival studies involving time to relapse (or death), we often need survival models with cured fraction to account for the subjects enjoying prolonged survival. Our article presents a new proportional odds survival models with a cured fraction using a special hierarchical structure of the latent factors activating cure. This ne...

Model Diagnostics for Proportional and Partial Proportional Odds Models