Attenuation caused by infrequently updated covariates in survival analysis.
This paper deals with hazard regression models for survival data with time-dependent covariates consisting of updated quantitative measurements. The main emphasis is on the Cox proportional hazards model but also additive hazard models are discussed. Attenuation of regression coefficients caused by infrequent updating of covariates is evaluated using simulated data mimicking our main example, the CSL1 liver cirrhosis trial. We conclude that the degree of attenuation depends on the type of stochastic process describing the time-dependent covariate and that attenuation may be substantial for an Ornstein-Uhlenbeck process. Also trends in the covariate combined with non-synchronous updating may cause attenuation. Simple methods to adjust for infrequent updating of covariates are proposed and compared to existing techniques using both simulations and the CSL1 data. The comparison shows that while existing, more complicated methods may work well with frequent updating of covariates the simpler techniques may have advantages in larger data sets with infrequent updatings.
Andersen
Per Kragh
PK
Department of Biostatistics, University of Copenhagen, Blegdamsvej 3, DK 2200 Copenhagen N, Denmark. pka@biostat.ku.dk
Liestøl
Knut
K
D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004311:Double-Blind Method; D005500:Follow-Up Studies; D006801:Humans; D008103:Liver Cirrhosis / Q000188:drug therapy; D015337:Multicenter Studies as Topic / Q000706:statistics & numerical data; D011241:Prednisone / Q000627:therapeutic use; D016016:Proportional Hazards Models; D011516:Prothrombin / Q000276:immunology; D012306:Risk; D013269:Stochastic Processes; D016019:Survival Analysis; D013997:Time Factors
D011516:Prothrombin; D011241:Prednisone