Parametric survival models for interval-censored data with time-dependent covariates.
We present a parametric family of regression models for interval-censored event-time (survival) data that accomodates both fixed (e.g. baseline) and time-dependent covariates. The model employs a three-parameter family of survival distributions that includes the Weibull, negative binomial, and log-logistic distributions as special cases, and can be applied to data with left, right, interval, or non-censored event times. Standard methods, such as Newton-Raphson, can be employed to estimate the model and the resulting estimates have an asymptotically normal distribution about the true values with a covariance matrix that is consistently estimated by the information function. The deviance function is described to assess model fit and a robust sandwich estimate of the covariance may also be employed to provide asymptotically robust inferences when the model assumptions do not apply. Spline functions may also be employed to allow for non-linear covariates. The model is applied to data from a long-term study of type 1 diabetes to describe the effects of longitudinal measures of glycemia (HbA1c) over time (the time-dependent covariate) on the risk of progression of diabetic retinopathy (eye disease), an interval-censored event-time outcome.
Sparling
Yvonne H
YH
The Biostatistics Center, Department of Biostatistics and Epidemiology, School of Public Health and Health Services, The George Washington University, Rockville, MD 20852, USA.
Younes
Naji
N
Lachin
John M
JM
Bautista
Oliver M
OM
D001699:Biometry / Q000379:methods; D003627:Data Interpretation, Statistical; D003922:Diabetes Mellitus, Type 1 / Q000097:blood; D003930:Diabetic Retinopathy / Q000097:blood / Q000209:etiology; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors
D006442:Glycated Hemoglobin