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

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Abstract:

Changes in survival rates during 1940–1992 for patients with Hodgkin's disease are studied by using population-based data. The aim of the analysis is to identify when the breakthrough in clinical trials of chemotherapy treatments started to increase population survival rates, and to find how long it took for the increase to level off, indicating that the full population effect of the breakthrough had been realized. A Weibull relative survival model is used because the model parameters are easily interpretable when assessing the effect of advances in clinical trials. However, the methods apply to any relative survival model that falls within the generalized linear models framework. The model is fitted by using modifications of existing software (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. Survival started to improve around the time that a major chemotherapy breakthrough (nitrogen mustard, Oncovin, prednisone and procarbazine) was publicized in the mid 1960s but did not level off for 11 years. For the analysis of data where the cause of death is obtained from death certificates, the relative survival approach has the advantage of providing the necessary adjustment for expected mortality from causes other than the disease without requiring information on the causes of death.

Keywords: Parametric model; Relative survival; Weibull generalized linear model

Document Type: Research Article

Affiliations: 1: Harvard School of Public Health and Dana-Farber Cancer Institute, Boston, USA 2: National Cancer Institute, Bethesda, USA 3: Geisinger Medical Center, Danville, USA 4: Information Management Systems, Silver Spring, USA

Publication date: January 1, 1999

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