期刊
BEHAVIOR GENETICS
卷 32, 期 3, 页码 221-227出版社
KLUWER ACADEMIC/PLENUM PUBL
DOI: 10.1023/A:1016025229858
关键词
twin modeling; parsimony; AIC; statistics; simulation; twin study; genetic epidemiology
资金
- NCI NIH HHS [CA85739] Funding Source: Medline
- NIAID NIH HHS [AI38429] Funding Source: Medline
- NINDS NIH HHS [NS41483] Funding Source: Medline
Akiake's Information Criterion (AIC) is commonly used in univariate twin modeling of a discrete trait to prune a full model into a more parsimonious submodel. It is possible that this practice could introduce bias and inaccuracy, and we could identify no prior systematic study of these issues. Thus, we used simulation to investigate the performance of AIC-guided modeling across a broad range of parameters. Our simulations indicated that the use of the AIC to determine the best univariate model for a discrete trait tended to yield the incorrect model rather frequently. Moreover the parameter estimates of the best model by AIC were biased sharply upward as were the associated 95% confidence intervals. These results suggest that the use of AIC to guide twin modeling for univariate discrete traits should either be abandoned or used with great caution.
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