Parametric relative survival regression using generalized linear models with application to Hodgkin's lymphoma

Changes in survival rates during 1940-1992 for patients with Hodgkin's dise ase are studied by using population-based data. The aim of the analysis is to identify when the breakthrough in clinical trials of chemotherapy treatm ents started to increase population survival rates, and to find how long it took for the increase to level off, indicating that the full population ef fect of the breakthrough had been realized. A Weibull relative survival mod el is used because the model parameters are easily interpretable when asses sing the effect of advances in clinical trials. However, the methods apply to any relative survival model that falls within the generalized linear mod els framework. The model is fitted by using modifications of existing softw are (SAS, GLIM) and profile likelihood methods. The results are similar to those from a cause-specific analysis of the data by Feuer and co-workers. S urvival started to improve around the time that a major chemotherapy breakt hrough (nitrogen mustard, Oncovin, prednisone and procarbazine) was publici zed in the mid-1960s but did not level off for 11 years. For the analysis o f data where the cause of death is obtained from death certificates, the re lative survival approach has the advantage of providing the necessary adjus tment for expected mortality from causes other than the disease without req uiring information on the causes of death.