Cited by 311Genetic Associations and EpidemiologyStatistical Methods and Bayesian InferencePsychometric Methodologies and Testing

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The Use of Likelihood-Based Confidence Intervals in Genetic Models

Neale, Michael C., Miller, Michael B.·1997·Behavior Genetics

DOI 10.1023/a:1025681223921

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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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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
Publisher: Springer Science and Business Media LLC
Published: 1997-03
Volume: 27
Issue: 2
Pages: 113-120
ISSN: 0001-8244 (ISSN-L)
1573-3297
DOI: 10.1023/a:1025681223921
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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