Dynamic analysis of recurrent event data using the additive hazard model.
We propose a method for analysis of recurrent event data using information on previous occurrences of the event as a time-dependent covariate. The focus is on understanding how to analyze the effect of such a dynamic covariate while at the same time ensuring that the effects of treatment and other fixed covariates are unbiasedly estimated. By applying an additive regression model for the intensity of the recurrent events, concepts like direct, indirect and total effects of the fixed covariates may be defined in an analogous way as for traditional path analysis. Theoretical considerations as well as simulations are presented, and a data set on recurrent bladder tumors is used to illustrate the methodology.
Fosen
Johan
J
Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, N-0317 Oslo. johan.fosen@medisin.uio.no
Borgan
Ornulf
O
Weedon-Fekjaer
Harald
H
Aalen
Odd O
OO
D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000401:mortality*; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D001749:Urinary Bladder Neoplasms / Q000401:mortality*