BMA and Dynamic Nomogram for Survival Prediction in Patients With CRC

Colon Cancer, Model Disease

Colorectal cancer; Translational statistics; Online dynamic nomogram; Bayesian variable selection; Survival prediction

This project will examine the outstanding statistical techniques for predicting the survival of patients with colorectal cancer (CRC) (colorectal neoplasia database). The motivating clinical question that led to proposing this project is based on the general assumption that: Right-sided colorectal cancer (CRC) has worse survival than left-sided CRC. The question is, which aspects of the patient's characteristics are responsible for this difference? This led us to BMA model selection and provide a clinician-friendly online nomogram.

Translational statistics merges biostatistics and clinical research to communicate research findings effectively. Nomograms, graphical representations integrating independent prognostic factors, are valuable tools in colorectal cancer (CRC) research. Bayesian models for variable selection in survival outcome prediction offer advantages through Bayesian model averaging (BMA). This study aimed to utilise BMA for variable selection and develop a clinician-friendly online dynamic nomogram for survival prediction. A retrospective study utilised the Cabrini Monash colorectal neoplasia database, including colon cancer patients who underwent surgery. Data on demographics, perioperative risks, treatment details, mortality, morbidity, and survival were collected. BMA was employed for Bayesian variable selection to identify effective risk factors for survival prediction. Sensitivity analyses using Cox-LASSO and imputation of missing data were performed. Prognostic online dynamic nomograms were constructed using selected risk factors and the R-package DynNom.

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