or search on
|Title:||Estimating the proportion cured of cancer: Some practical advice for users|
|Authors:||Yu XQ; De Angelis R; Andersson TM; Lambert PC; O'Connell DL; Dickman PW|
|Categories:||Cancer Type - All Cancers combined|
Cancer Control, Survivorship, and Outcomes Research - Resources and Infrastructure
|Keywords:||Aged; Public Health; relative survival; survival; Wales; Australia; breast; cancer; Colon; Female; methods; New South Wales; Other|
|Journal Title:||Cancer Epidemiology|
|Page number start:||836|
|Page number end:||842|
|Abstract:||BACKGROUND: Cure models can provide improved possibilities for inference if used appropriately, but there is potential for misleading results if care is not taken. In this study, we compared five commonly used approaches for modelling cure in a relative survival framework and provide some practical advice on the use of these approaches. PATIENTS AND METHODS: Data for colon, female breast, and ovarian cancers were used to illustrate these approaches. The proportion cured was estimated for each of these three cancers within each of three age groups. We then graphically assessed the assumption of cure and the model fit, by comparing the predicted relative survival from the cure models to empirical life table estimates. RESULTS: Where both cure and distributional assumptions are appropriate (e.g., for colon or ovarian cancer patients aged <75 years), all five approaches led to similar estimates of the proportion cured. The estimates varied slightly when cure was a reasonable assumption but the distributional assumption was not (e.g., for colon cancer patients >/=75 years). Greater variability in the estimates was observed when the cure assumption was not supported by the data (breast cancer). CONCLUSIONS: If the data suggest cure is not a reasonable assumption then we advise against fitting cure models. In the scenarios where cure was reasonable, we found that flexible parametric cure models performed at least as well, or better, than the other modelling approaches. We recommend that, regardless of the model used, the underlying assumptions for cure and model fit should always be graphically assessed|
|Programme:||Health Services Research|
|Division:||Cancer Research Division|
|Appears in Collections:||Research Articles|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.