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Likelihood-based concordance tests for analysis of ascertained twin pair multinomial data.
Jason Kunming Huang1, Alyssa Yanzhen Liu, John Jen Tai
1Department of Banking and Finance, College of Finance, Takming University of Science and Technology, Taipei, 11451, Taiwan.
Statistics in Medicine
|November 5, 2011
Summary
This study evaluates statistical tests for twin studies, comparing monozygotic (MZ) and dizygotic (DZ) twin concordances. The alternative Wald test (WaldA) is recommended for its robustness in analyzing genetic and environmental influences on disease.
Area of Science:
- Human genetics
- Biostatistics
- Epidemiology
Background:
- The classic twin model is crucial for understanding genetic and environmental influences on diseases.
- Casewise concordance analysis in twin studies requires careful consideration of ascertainment, especially for low-prevalence diseases.
Purpose of the Study:
- To investigate and compare the performance of several statistical tests for assessing the equality of casewise concordances between ascertained monozygotic (MZ) and dizygotic (DZ) twin pairs.
- To identify the most robust and reliable statistical approach for twin concordance analysis.
Main Methods:
- Utilized multinomial models to analyze ascertained MZ and DZ twin pair data.
- Evaluated likelihood ratio (LR) test, score (Score) test, and two Wald tests (Wald, WaldA) through simulation studies with small sample sizes.
Main Results:
- LR and Score tests showed stable performance only with moderate overall casewise concordances.
- The Wald test demonstrated higher power but had inflated type I error rates.
- The alternative Wald test (WaldA) exhibited the most robust performance across various conditions.
Conclusions:
- The alternative Wald test (WaldA) is the recommended statistical approach for testing concordance equality in ascertained twin studies.
- The choice of statistical test impacts the reliability of conclusions regarding genetic and environmental factors in disease.
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