Determining the Length of Posttherapeutic Follow-up for Cancer Patients Using Competing Risks Modeling. (February 2014)

• Record Type: Journal Article
• Authors: Somda, Serge M. A.; Leconte, Eve; Kramar, Andrew; Penel, Nicolas; Chevreau, Christine; Delannes, Martine; Rios, Maria; Filleron, Thomas
• Is Part Of: Medical decision making. Volume 34:Number 2(2014)
• Page Start: 168
• Page End: 179
• Publication Date: 2014-02
• Subjects: posttherapeutic follow-up -- competing risks modeling -- Gompertz distribution -- cancer; Medical policy -- Periodicals; Clinical medicine -- Decision making -- Periodicals; Medicine -- Periodicals; Médecine clinique -- Prise de décision -- Périodiques; 362.1
• DOI: 10.1177/0272989X13492015
• Languages: English
• ISSNs: 0272-989X

Abstract

Background/Objective. After a curative treatment for cancer, patients enter into a posttherapeutic surveillance phase. This phase aims to detect relapses as soon as possible to improve the outcome. Mould and others predicted with a simple formula, using a parametric mixture cure model, how long early-stage breast cancer patients should be followed after treatment. However, patients in posttherapeutic surveillance phase are at risk of different events types with different responses according to their prognostic factors and different probabilities to be cured. This paper presents an adaptation of the method proposed by Mould and others, taking into account competing risks. Our loss function estimates, when follow-up is stopped at a given time, the proportion of patients who will fail after this time and who could have been treated successfully.Method. We use the direct approach for cumulative incidence modeling in the presence of competing risks with an improper Gompertz probability distribution as proposed by Jeong and Fine. Prognostic factors can be taken into account, leading to a proportional hazards model. In a second step, the estimates of the Gompertz model are combined with the probability for a patient to be treated successfully in case of relapse for each event type. The method is applied to 2 examples, a numeric fictive example and a real data set on soft tissue sarcoma.Results and Conclusion. The model presented is a good tool for decision making to determine the total length of posttherapeutic surveillance. It can be applied to all cancers regardless of the localizations.