Developing multilevel logistic regression models for predicting cancer outcomes from population health surveys.

Predicting cancer outcomes using population-level data has become increasingly important for guiding public health interventions and optimizing healthcare resource allocation.Population health surveys, which capture a wide range of demographic, behavioral, and clinical indicators, offer a rich foundation for understanding the complex interplay of factors influencing cancer prognosis.However, the hierarchical nature of such data-where individuals are nested within communities, regions, or healthcare systems-necessitates advanced analytical methods.This study develops multilevel logistic regression models to predict cancer outcomes while accounting for both individual-level and contextual-level influences.Utilizing nationally representative health survey datasets linked with cancer registry outcomes, we explore predictors of binary outcomes such as cancer survival (alive vs. deceased) and late-stage diagnosis (early vs. advanced stage).The multilevel approach enables simultaneous modeling of fixed effects (e.g., age, sex, comorbidities, healthcare utilization) and random effects (e.g., regional healthcare quality, socioeconomic status by area), thus capturing both micro and macro-level determinants of health.Model performance is evaluated using measures such as the area under the receiver operating characteristic curve (AUC), Akaike Information Criterion (AIC), and calibration plots.Cross-validation techniques are applied to ensure model generalizability and reduce overfitting.The results demonstrate that accounting for contextual variables significantly improves prediction accuracy and reveals disparities in cancer outcomes driven by geographic and structural inequities.This research underscores the utility of multilevel logistic regression in analyzing complex health data and producing reliable, policy-relevant insights.It advances predictive modeling practices in public health and supports data-driven strategies to address cancer-related health disparities and improve outcome prediction at both the individual and population levels.