Competing-risk Prognostic Modelling of Breast Cancer-specific Mortality in Ghana: Stability Selection, Internal Validation, and Risk Stratification
Emmanuel Mensah Baah *
Department of Mathematics, Statistics and Actuarial Science, Takoradi Technical University, Sekondi–Takoradi, Ghana.
Senyefia Bosson-Amedenu
Department of Mathematics, Statistics and Actuarial Science, Takoradi Technical University, Sekondi–Takoradi, Ghana.
Abdulzeid Yen Anafo
Department of Computing and Data Analytics, University of Mines and Technology, Tarkwa, Ghana.
*Author to whom correspondence should be addressed.
Abstract
Background: Breast cancer outcomes are heterogeneous, and disease-specific mortality may be inaccurately estimated when deaths from other causes are treated as simple censoring.
Aim: This study developed and internally validated a competing-risk prognostic model for breast-cancer-specific mortality in a Ghanaian cohort.
Methods: A retrospective cohort of 558 patients diagnosed with breast cancer and followed for up to 60 months between 2010 and 2015 was analysed. Breast-cancer-specific mortality was defined as the event of interest; deaths from other causes were treated as competing events, and patients alive at the end of follow-up were censored. Predictor reproducibility was assessed using Cox-LASSO stability selection. Stable predictors were incorporated into a Fine-Gray subdistribution hazard model, and estimates were compared with those from a cause-specific Cox model. Model performance was assessed using concordance indices, inverse probability of censoring-weighted Brier scores, calibration at 36 months, bootstrap optimism correction, decision-curve analysis, and risk-stratified cumulative incidence functions.
Results: Stability selection identified metastatic status (selection frequency = 1.00) and the ER+/PR+/HER2- subtype (selection frequency = 0.82) as the most reproducible predictors. In the Fine-Gray model, metastatic disease was associated with higher breast-cancer-specific mortality risk (sHR = 46.70, 95% CI: 19.66-110.94, p < 0.001), whereas the ER+/PR+/HER2- subtype was associated with lower risk (sHR = 0.56, 95% CI: 0.48-0.66, p < 0.001). The model showed good discrimination (Harrell's C-index = 0.83, 95% CI: 0.81-0.85), minimal bootstrap optimism, reasonable 36-month calibration, and positive net benefit across threshold probabilities of 0.05-0.25.
Conclusion: Metastatic status and molecular subtype were the principal prognostic factors in this cohort. External validation is required before broader clinical application.
Keywords: Breast cancer, breast-cancer-specific mortality, competing risks, Fine-Gray model, metastatic status, molecular subtype, Cox-LASSO stability selection, internal validation, calibration, decision-curve analysis, cumulative incidence