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Augmented composite likelihood for copula modeling in family studies under biased sampling.
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada N2L 3G1 zyujie@uwaterloo.ca.
Biostatistics (Oxford, England)
|January 29, 2016
Summary
This study introduces new statistical methods to analyze chronic disease heritability by examining family disease onset times. Findings suggest a potential for paternal transmission of psoriatic arthritis risk.
Area of Science:
- Biostatistics
- Genetics
- Epidemiology
Background:
- Chronic diseases exhibit heritability, influenced by familial disease onset patterns.
- Family studies often use biased sampling, including affected individuals and relatives with censored data.
Purpose of the Study:
- To develop statistical methods for modeling within-family associations in chronic disease onset times.
- To improve the estimation of heritability and dependence parameters in family studies.
Main Methods:
- Utilized copula models to characterize dependencies in disease onset times within families.
- Developed likelihood and composite likelihood methods for analyzing familial disease data.
- Incorporated auxiliary data from independent individuals to enhance parameter estimation precision.
Main Results:
- The methods effectively model within-family associations in disease onset times.
- Demonstrated increased precision in marginal and dependence parameter estimation.
- An application to psoriatic arthritis suggested excessive paternal transmission of risk.
Conclusions:
- The developed statistical framework enhances the study of chronic disease heritability in families.
- The findings highlight the importance of considering familial dependencies and sampling schemes.
- Evidence suggests a potential role for paternal factors in psoriatic arthritis transmission.
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