Bayesian Models for Partly Interval-Censored Data
PH model with random intercept for clustered general interval-censored...
PH model with random intercept for clustered general interval-censored...
PH model with random intercept and random treatment for clustered gene...
PH model with random intercept and random treatment for clustered gene...
Mixed effects PH model for clustered general interval-censored data
Mixed effects PH model for clustered general interval-censored data
PH model with random intercept for clustered partly interval-censored ...
PH model with random intercept for clustered partly interval-censored ...
PH model with random intercept and random treatment for clustered part...
PH model with random intercept and random treatment for clustered part...
Mixed effects PH model for clustered partly interval-censored data
Mixed effects PH model for clustered partly interval-censored data
Coef method for a PICBayes model
PH model for general interval-censored data
LogLik method for a PICBayes model
PH model for partly interval-censored data
Bayesian Models for Partly Interval-Censored Data and General Interval...
Bayesian models for partly interval-censored data and general interval...
Plot method for a PICBayes model
PH model for spatial general interval-censored data
PH model for spatial partly interval-censored data
Summary method for a PICBayes model
Transform Surv object to data matrix with L and R columns
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.