A novel log-logistic risk-adjusted CUSUM control chart for monitoring therapeutic processes in breast cancer patients

Breast cancer remains a leading malignancy among women globally, necessitating precise monitoring of therapeutic outcomes to improve survival and quality of life. Conventional statistical quality control tools often face limitations due to patient heterogeneity. This study proposes a novel log-logistic risk-adjusted cumulative sum (RACUSUM) control chart for monitoring breast cancer treatments, incorporating an accelerated failure time (AFT) model to adjust for individual patient risk factors. This risk adjustment enhances the chart’s sensitivity and specificity in detecting deviations in treatment effectiveness. A significant advancement is the multi-objective optimization of the RACUSUM design, balancing rapid detection of process shifts with cost-efficiency. Multi-objective particle swarm optimization (MOPSO) generates optimal solution sets, which are prioritized using the data envelopment analysis (DEA) and weighted aggregated sum product assessment (WASPAS) methods to support clinical decision-making. Validation through a case study at a breast cancer center demonstrated superior performance compared to traditional models, achieving improved monitoring accuracy with minimal incremental cost. This integrated approach offers clinicians and healthcare administrators a robust tool for enhancing breast cancer care quality, ultimately aiming to reduce adverse events and improve patient outcomes.
Introduces a novel log-logistic RACUSUM chart for breast cancer treatment monitoring.
Integrates AFT model for precise risk adjustment of patient survival times.
Employs multi-objective optimization balancing detection speed and cost-efficiency.
Utilizes MOPSO, DEA, and WASPAS methods for optimal parameter selection.
Validated with clinical data, showing improved accuracy and economic viability.