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Updated: Aug 27, 2025
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
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Using mortality to predict incidence for rare and lethal cancers in very small areas
Jaione Etxeberria1,2, Tomás Goicoa1,2,3, Maria D Ugarte1,2
1Department of Statistics, Computer Science and Mathematics, Public University of Navarre (UPNA), Campus Arrosadia, Pamplona, Navarre, Spain.
Biometrical Journal. Biometrische Zeitschrift
|September 30, 2022
Summary
Predicting cancer incidence is crucial for understanding cancer burden. This study introduces a novel spatio-temporal modeling approach to accurately predict incidence rates for less common cancers, like brain cancer, using joint incidence and mortality data.
Area of Science:
- Epidemiology
- Biostatistics
- Cancer Research
Background:
- Cancer burden assessment requires both incidence and mortality data.
- Cancer incidence data often lag behind mortality data due to registry complexities.
- Predictive models typically exclude rare cancers due to data scarcity.
Conclusions:
- Jointly modeling incidence and mortality data is effective for predicting rare cancer incidence.
- The spatio-temporal modeling approach enhances predictive accuracy by leveraging correlations between incidence and mortality.
- This methodology provides a valuable tool for cancer surveillance and planning, particularly for underrepresented cancer types.
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