Adjusting for age-related competing mortality in long-term cancer clinical trials.
Mortality related to causes other than the treated disease may have a significant impact on overall survival in long-term clinical trials. We present a model that adjusts for age-related competing mortality when cause of death is missing or only partially available. Through use of a piecewise exponential survival model, we extend relative survival methods to continuous follow-up data, allowing the competing mortality to differ from that of the general population by a scale parameter. An EM algorithm provides a simple way to compute the maximum likelihood estimators (MLEs) and to test hypotheses using widely available software. We compare the bias and relative efficiency of this model to a piecewise exponential Cox model for overall survival. Theoretical results are confirmed by simulations and illustrated with data from a clinical trial in colorectal cancer. This example also shows how age-related and disease-related mortality can be confounded in an analysis of overall survival. We conclude with a discussion of the advantages and disadvantages of the model.
Cheuvart
B
B
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.
Ryan
L
L
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015982:Bias; D002423:Cause of Death; D002986:Clinical Trials as Topic / Q000379:methods*; D015179:Colorectal Neoplasms / Q000401:mortality; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000401:mortality*; D016019:Survival Analysis