title: Modeling clustered, discrete, or grouped time survival data with covariates.
abstract: We have developed methods for modeling discrete or grouped time, right-censored survival data collected from correlated groups or clusters. We assume that the marginal hazard of failure for individual items within a cluster is specified by a linear log odds survival model and the dependence structure is based on a gamma frailty model. The dependence can be modeled as a function of cluster-level covariates. Likelihood equations for estimating the model parameters are provided. Generalized estimating equations for the marginal hazard regression parameters and pseudolikelihood methods for estimating the dependence parameters are also described. Data from two clinical trials are used for illustration purposes.
authors: Department of Biostatistics, Fox Chase Cancer Center, Cheltenhanm, Pennsylvania 19012, USA. e_ross@fccc.edu
authors: E A
authors: EA
authors: Ross
authors: D
authors: D
authors: Moore
mesh_terms: D000971:Antineoplastic Combined Chemotherapy Protocols / Q000009:adverse effects / Q000627:therapeutic use; D001699:Biometry; D001766:Blindness / Q000517:prevention & control; D001943:Breast Neoplasms / Q000188:drug therapy; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D003930:Diabetic Retinopathy / Q000601:surgery; D004361:Drug Tolerance; D005260:Female; D006801:Humans; D008028:Light Coagulation; D016013:Likelihood Functions; D016014:Linear Models; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D016019:Survival Analysis