The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
The Use of Likelihood-Based Confidence Intervals in Genetic Models
Abstract
This article describes the computation and relative merits of likelihood-based confidence intervals, compared to other measures of error in parameter estimates. Likelihood-based confidence intervals have the advantage of being asymmetric, which is often the case with structural equation models for genetically informative studies. We show how the package Mx provides confidence intervals for parameters and functions of parameters in the context of a simple additive genetic, common, and specific environment threshold model for binary data. Previously published contingency tables for major depression in adult female twins are used for illustration. The support for the model shows a marked skew as the additive genetic parameter is systematically varied from zero to one. The impact of allowing different prevalence rates in MZ vs. DZ twins is explored by fitting a model with separate threshold parameters and comparing the confidence intervals. Despite the improvement in fit of the different prevalence model, the confidence intervals on all parameters broaden, owing to their covariance.
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“Likelihood-based confidence intervals are considered superior to asymptotic confidence intervals for finite samples (Neale and Miller, 1997).”
From Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood (2009) · cites this paper as Neale and Miller, 1997
In the citing paper
“Furthermore, the profile likelihood allows one to assess likelihood based confidence intervals [20-22].”
From Joining forces of Bayesian and frequentist methodology: a study for inference in the presence of non-identifiability (2013) · cites this paper as [22]
Citations
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Methodology for Genetic Studies of Twins and Families
Neale, M. C., & Cardon, L. R. (1992) · doi:10.1007/978-94-015-8018-2 · Cited by 4,005
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Flagged first, then by citations
Research works printed but not linked: 4
- Arbuckle, J. (1994). AMOS 3.51 Documentation Package. Smallwaters Inc., Chicago.
- Edwards, A. W. F. (1972). Likelihood, Cambridge, London.
- Neale, M. C. (1995). Mx: Statistical Modeling, 3rd ed., Box 980126 MCV, Richmond VA 23298.
- SAS (1988). SAS/STAT User's Guide: Release 6.03, SAS Institute, Inc., Cary, NC.
Cited by
Sorted by citations
Genetic Epidemiology of Major Depression: Review and Meta-Analysis
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000) · American Journal of Psychiatry · doi:10.1176/appi.ajp.157.10.1552 · Cited by 3,431
Self-citationRaue, A., Kreutz, C., Maiwald, T., Bachmann, J., Schilling, M., Klingmüller, U., & Timmer, J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp358 · Cited by 1,442
Socioeconomic Status Modifies Heritability of IQ in Young Children
Turkheimer, E., Haley, A., Waldron, M., D'Onofrio, B., & Gottesman, I. I. (2003) · Psychological Science · doi:10.1046/j.0956-7976.2003.psci_1475.x · Cited by 1,373
Cheung, M. W.-L. (2014) · Psychological Methods · doi:10.1037/a0032968 · Cited by 1,342
Structural Equation Modeling: Strengths, Limitations, and Misconceptions
Tomarken, A. J., & Waller, N. G. (2004) · Annual Review of Clinical Psychology · doi:10.1146/annurev.clinpsy.1.102803.144239 · Cited by 946
Metadata
- Authors
- Neale, M. C., & Miller, M. B.
- Affiliations
- Washington University in St. Louis (US)
- Corresponding author
- Neale, Michael C.
- ORCID
- Neale, Michael C.
- Venue
- Behavior Genetics
- Published
- 1997-03
- Volume
- 27
- Issue
- 2
- Pages
- 113-120
- ISSN
- 0001-8244 (ISSN-L)
- 1573-3297
- OpenAlex
- W2109232798
- Cited by
- 310
- References
- 21
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 5 · 2023: 10 · 2022: 7 · 2021: 10 · 2020: 12 · 2019: 7 · 2018: 11 · 2017: 15 · 2016: 9 · 2015: 11 · 2014: 7 · 2013: 8 · 2012: 12
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Behavior Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (5r01rr008123-02)
- National Institute of Mental Health (T32MH017104, MH/AA 49492, MH 17104)
- National Center for Research Resources (RR-08123)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Keywords
- genetic models, structural equation modeling, threshold model, binary data, major depressive disorder, twin studies, additive genetic effects, shared environment, prevalence rates, model fit, parameter estimation
- MeSH terms
- Adult, Depressive Disorder, Diseases in Twins, Female, Humans, Models, Genetic, Social Environment, Twins, Dizygotic, Twins, Monozygotic, Confidence Intervals, Likelihood Functions
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