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A comparison of statistical methods to predict the residual lifetime risk
Sarah C Conner1, Alexa Beiser2,3,4, Emelia J Benjamin3,5,6
1Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. sconner@bu.edu.
European Journal of Epidemiology
|January 3, 2022
Summary
Predicting lifetime disease risk requires advanced statistical methods. A pseudo-observation approach showed the least bias for estimating cumulative disease incidence, especially with changing risk factors over time.
Area of Science:
- Biostatistics
- Epidemiology
- Medical Statistics
Background:
- Lifetime risk assessment is crucial for understanding disease burden.
- Accurate modeling must address challenges like left truncation and competing risks.
- Time-varying covariate effects complicate lifetime risk prediction.
Purpose of the Study:
- To review and compare statistical methodologies for predicting lifetime disease risk.
- To evaluate methods accounting for left truncation, competing risks, and time-varying covariates.
- To provide practical guidance for lifetime risk prediction in epidemiological studies.
Main Methods:
- Utilized a generalized linear model with pseudo-observations of the Aalen-Johansen estimator for left-truncated data.
- Investigated subdistribution hazard modeling using Fine-Gray and Royston-Parmar flexible parametric models with time-covariate interactions.
- Conducted simulation studies to compare method performance under various scenarios.
Main Results:
- The pseudo-observation approach demonstrated the least bias in lifetime risk estimation.
- This method proved particularly effective when cumulative incidence curves crossed or converged.
- The study successfully applied the method to model atrial fibrillation lifetime risk in the Framingham Heart Study.
Conclusions:
- Pseudo-observation methods offer a robust approach for lifetime risk prediction, handling complexities like left truncation and time-varying effects.
- These methods are valuable for accurate disease risk assessment in longitudinal studies.
- The findings provide a foundation for improved clinical and public health risk prediction models.
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