Adjusting for age‐related competing mortality in long‐term cancer clinical trials

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Abstract
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 dis‐advantages of the model.