A decision-analytic model for early stage breast cancer: lumpectomy vs mastectomy.
The purpose was to construct a decision model that incorporated patient preferences over differing health state prospects and to analyze the decision context of early stage breast cancer patients in relation to two main surgical treatment options. A Markov chain was constructed to project the clinical history of breast carcinoma following surgery. A Multi Attribute Utility Model was developed for outcome evaluation. Transition probabilities were obtained by using subjective probability assessment. This study was performed on the sample population of female university students and utilities were elicited from these healthy volunteers. The results were validated by using Standard Gamble technique. Finally, Monte Carlo Simulation was utilized in Treeage-Pro 2006-Suit software program in order to calculate expected utility generated by each treatment option. The results showed that, if the subject had mastectomy, mean value for the quality adjusted life years gained was 6.42; on the other hand, if the preference was lumpectomy, it was 7.00 out of a possible 10 years. Sensitivity analysis on transition probabilities to local recurrence and salvaged states was performed and two threshold values were observed. Additionally, sensitivity analysis on utilities showed that the model was more sensitive to no evidence of disease state; however, was not sensitive to utilities of local recurrence and salvaged states. The decision model was developed with reasonable success for early stage breast cancer patients, and tested by using general public data. The results obtained from these data showed that lumpectomy was more favourable for these participants.
Büyükdamgaci-Alogan
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G
Scientific and Technological Research Council of Turkey, Ankara, Turkey.
Elele
T
T
Hayran
M
M
Erman
M
M
Kiliçkap
S
S
D001943:Breast Neoplasms / Q000601:surgery*; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D008390:Markov Chains; D008408:Mastectomy; D015412:Mastectomy, Segmental; D008954:Models, Biological; D009010:Monte Carlo Method; D017060:Patient Satisfaction; D012680:Sensitivity and Specificity