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A New Inverse Probability of Selection Weighted Cox Model to Deal With Outcome-Dependent Sampling in Survival
Vera H Arntzen1, Marta Fiocco1,2, Inge M M Lakeman3
1Mathematical Institute, Section of Statistics, Leiden University, Leiden, The Netherlands.
Biometrical Journal. Biometrische Zeitschrift
|June 12, 2025
Summary
We developed a generalized weighted approach to fix ascertainment bias in survival analysis for genetic studies. This method improves upon existing techniques, offering more accurate results when analyzing cancer susceptibility loci.
Area of Science:
- Genetics and Epidemiology
- Biostatistics
- Cancer Research
Background:
- Outcome-dependent sampling in genetic epidemiology can cause ascertainment bias, overrepresenting young, affected individuals.
- Existing inverse probability-weighted Cox models struggle with bias correction when oversampling varies across age groups.
Purpose of the Study:
- To propose a more generalizable weighting approach to correct for ascertainment bias in survival analysis.
- To evaluate the performance of the new generalized weighted cohort method compared to existing approaches.
Main Methods:
- Developed the generalized weighted approach, a novel method for correcting ascertainment bias.
- Utilized simulations and two real-world datasets (colorectal and breast cancer) for validation.
- Applied the method to assess associations between genetic susceptibility loci and cancer risk.
Main Results:
- The generalized weighted approach demonstrated advantages over current methods in simulations.
- The method proved effective in real-world applications involving cancer genetic data.
- Validated the utility of the approach in analyzing genome-wide association studies (GWAS) findings.
Conclusions:
- The generalized weighted approach offers a more robust and flexible solution for addressing ascertainment bias in survival analysis.
- This method enhances the accuracy of genetic association studies, particularly in cancer research.
- The findings support the use of this new method for analyzing data from clinical genetics centers.
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