title: The application of cure models in the presence of competing risks: a tool for improved risk communication in population-based cancer patient survival.
abstract: Quantifying cancer patient survival from the perspective of cure is clinically relevant. However, most cure models estimate cure assuming no competing causes of death. We use a relative survival framework to demonstrate how flexible parametric cure models can be used in combination with competing-risks theory to incorporate noncancer deaths. Under a model that incorporates statistical cure, we present the probabilities that cancer patients (1) have died from their cancer, (2) have died from other causes, (3) will eventually die from their cancer, or (4) will eventually die from other causes, all as a function of time since diagnosis. We further demonstrate how conditional probabilities can be used to update the prognosis among survivors (eg, at 1 or 5 years after diagnosis) by summarizing the proportion of patients who will not die from their cancer. The proposed method is applied to Swedish population-based data for persons diagnosed with melanoma, colon cancer, or acute myeloid leukemia between 1973 and 2007.
authors.affiliation: From the aDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; bDepartment of Health Sciences, University of Leicester, Leicester, UK; and cDepartment of Medicine, Division of Hematology, Karolinska University Hospital Solna and Karolinska Institutet, Stockholm, Sweden.
authors.forename: Sandra
authors.initials: S
authors.lastname: Eloranta
authors.forename: Paul C
authors.initials: PC
authors.lastname: Lambert
authors.forename: Therese M-L
authors.initials: TM
authors.lastname: Andersson
authors.forename: Magnus
authors.initials: M
authors.lastname: Björkholm
authors.forename: Paul W
authors.initials: PW
authors.lastname: Dickman
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002423:Cause of Death; D003110:Colonic Neoplasms / Q000175:diagnosis / Q000401:mortality; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000175:diagnosis / Q000401:mortality; D008297:Male; D008545:Melanoma / Q000175:diagnosis / Q000401:mortality; D008875:Middle Aged; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000401:mortality*; D011336:Probability; D011379:Prognosis; D012042:Registries; D012306:Risk; D018570:Risk Assessment / Q000379:methods; D016019:Survival Analysis; D013548:Sweden / Q000453:epidemiology