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Updated: Jun 9, 2026
04:57
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Analyzing multivariate survival data using composite likelihood and flexible parametric modeling of the hazard
1Southern Center for National Clinical Databases, Odense University Hospital, Sdr. Boulevard 29, Entrance 101, 3rd floor, DK-5000 Odense C, Denmark. jan.nielsen2@ouh.regionsyddanmark.dk
Statistics in Medicine
|September 3, 2010
Summary
This study introduces a new statistical model for analyzing multiple time-to-event data, applicable to family studies and twin pain data, enhancing survival analysis methods.
Area of Science:
- Biostatistics
- Survival Analysis
- Genetic Epidemiology
Background:
- Multivariate time-to-event data presents analytical challenges.
- Accurate modeling is crucial for understanding familial associations and disease occurrence.
Purpose of the Study:
- To develop a novel statistical approach for modeling multivariate time-to-event data.
- To apply this method to family adoption and twin pain studies.
Main Methods:
- Composite likelihood modeling using pairwise frailty likelihoods.
- Incorporation of marginal hazards with natural cubic splines.
- Handling of right- and interval-censored data.
Main Results:
- The methodology effectively models associations in family structures.
- Application to adoption data revealed familial survival patterns.
- Analysis of twin data demonstrated utility in genetic epidemiology for back and neck pain.
Conclusions:
- The proposed composite likelihood approach provides a flexible framework for multivariate survival data.
- This method is valuable for studies involving familial aggregation and genetic influences on health outcomes.
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