Choice of time-scale in Cox's model analysis of epidemiologic cohort data: a simulation study.
Cox's regression model is widely used for assessing associations between potential risk factors and disease occurrence in epidemiologic cohort studies. Although age is often a strong determinant of disease risk, authors have frequently used time-on-study instead of age as the time-scale, as for clinical trials. Unless the baseline hazard is an exponential function of age, this approach can yield different estimates of relative hazards than using age as the time-scale, even when age is adjusted for. We performed a simulation study in order to investigate the existence and magnitude of bias for different degrees of association between age and the covariate of interest. Age to disease onset was generated from exponential, Weibull or piecewise Weibull distributions, and both fixed and time-dependent dichotomous covariates were considered. We observed no bias upon using age as the time-scale. Upon using time-on-study, we verified the absence of bias for exponentially distributed age to disease onset. For non-exponential distributions, we found that bias could occur even when the covariate of interest was independent from age. It could be severe in case of substantial association with age, especially with time-dependent covariates. These findings were illustrated on data from a cohort of 84,329 French women followed prospectively for breast cancer occurrence. In view of our results, we strongly recommend not using time-on-study as the time-scale for analysing epidemiologic cohort data.
Thiébaut
Anne C M
AC
INSERM, E3N-EPIC, Institut Gustave Roussy, Villejuif, France. biostatistique@chu-rouen.fr
Bénichou
Jacques
J
D000328:Adult; D000367:Age Factors; D000368:Aged; D001943:Breast Neoplasms / Q000453:epidemiology; D015331:Cohort Studies; D003198:Computer Simulation; D004812:Epidemiologic Methods; D005260:Female; D005602:France / Q000453:epidemiology; D006801:Humans; D008593:Menopause; D008875:Middle Aged; D016016:Proportional Hazards Models; D011446:Prospective Studies