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Title page for ETD etd-07172015-165311

Type of Document Master's Thesis
Author Fan, Run
URN etd-07172015-165311
Title Development of prognostic model for breast cancer in Shanghai Breast Cancer Survival Study (SBCSS)
Degree Master of Science
Department Biostatistics
Advisory Committee
Advisor Name Title
Fei Ye Committee Chair
Tatsuki Koyama Committee Member
  • prediction
  • breast cancer
  • modeling
Date of Defense 2015-07-17
Availability unrestricted
We developed prognostic models to predict five-year overall survival (OS), ten-year overall survival (OS), and five-year relapse-free survival (RFS) from Shanghai Breast Cancer Survival Study (SBCSS). SBCSS is a large, population-based cohort study of 4,858 female breast cancer patients aged 20 to 75 years at diagnoses. Patients were recruited between March 2002 and April 2006 and were followed up through December 2012. In addition to patients' demographic, clinical, pathological, treatment information, our model incorporates novel modifiable lifestyle information. Number of events of five-year OS, ten-year OS, and five-year RFS model is 535 (11.0%), 950 (19.6%), and 845 (17.4%). Missing outcome and variables were completed by single and multiple imputation to reduce bias and increase precision. A multivariate Cox proportional hazard model was developed for each survival outcome. Performance of the models was assessed by both discrimination and calibration. We internally validated our models using the .632 bootstrap method with 200 repetitions. Identical modeling procedures were repeated on these datasets. C-statistics of the full model was 0.754, 0.725, and 0.717 for five-year OS, ten-year OS, and five-year RFS, respectively. To further simplify full model for routine practice, model approximation was performed using backward step down method. We built a novel prognostic prediction model among Asian women. Comparing to existing prognostic tools, we expanded the model by incorporating lifestyle predictors, PR and HER2 status. 5-yr OS, 10-yr OS, and 5-yr RFS can be predicted accurately with good calibration and discrimination.
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