Skip to main content
SpringerLink
Log in

The Use of Likelihood-Based Confidence Intervals in Genetic Models

  • Published:
Behavior Genetics Aims and scope Submit manuscript

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 prevalences model, the confidence intervals on all parameters broaden, owing to their covariance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  • Arbuckle, J. (1994). AMOS 3.51 Documentation Package. Smallwaters Inc., Chicago.

    Google Scholar 

  • Agresti, A. (1990). Categorical Data Analysis, Wiley, New York.

    Google Scholar 

  • Bentler, P. M. (1989). EQS: Structural Equations Program Manual, BMDP Statistical Software, Los Angeles.

    Google Scholar 

  • Bollen, K. A. (1989). Structural Equations with Latent Variables, John Wiley, New York.

    Google Scholar 

  • Cohen, J. (1994). The earth is round. (P <.05) Am. Psychol. 49:997–1003.

    Google Scholar 

  • Dolan, C. V., and Molenaar, P. C. M. (1991). A comparison of four methods of calculating standard errors of maximum likelihood estimates in the analysis of covariance structure. Br. J. Math. Stat. Psychol. 44:359–368.

    Google Scholar 

  • Edwards, A. W. F. (1972). Likelihood, Cambridge, London.

    Google Scholar 

  • Falconer, D. (1965). The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann. Hum. Genet. 29:51–76.

    Google Scholar 

  • Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Phil. Trans. Roy. Soc. London Ser. A 222:309–368.

    Google Scholar 

  • Gill, P. E., Murray, W., and Wright, M. H. (1991). Numerical Linear Algebra and Optimization, Vol. 1, Addison-Wesley, New York.

    Google Scholar 

  • Hopper, J. L., and Culross, P. R. (1983). Covariation between family members as a function of cohabitation history. Behav. Genet. 13:459–471.

    Google Scholar 

  • Jöreskog, K. G., and Sörbom, D. (1989). LISREL 7: A Guide to the Program and Applications, 2nd ed., SPSS, Inc., Chicago.

    Google Scholar 

  • Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., and Eaves, L. J. (1992). A population based twin study of major depression in women: Varying definitions of illness. Arch. Gen. Psychiatry 49:257–266.

    Google Scholar 

  • Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., and Eaves, L. J. (1993). A test of the equal-environment assumption in twin studies of psychiatric illness. Behav. Genet. 23:21–27.

    Google Scholar 

  • Meeker, W. Q., and Escobar, L. A. (1995). Teaching about approximate confidence regions based on maximum likelihood estimation. Am. Stat. 49(1):48–53.

    Google Scholar 

  • Neale, M. C. (1995). Mx: Statistical Modeling, 3rd ed., Box 980126 MCV, Richmond VA 23298.

    Google Scholar 

  • Neale, M. C., and Cardon, L. R. (1992). Methodology for Genetic Studies of Twins and Families, Kluwer Academic, Dordrecht.

    Google Scholar 

  • Neale, M. C., Heath, A. C., Hewitt, J. K., Eaves, L. J., and Fulker, D. W. (1989). Fitting genetic models with LISREL: Hypothesis testing. Behav. Genet. 19:37–69.

    Google Scholar 

  • Popper, K. R. (1961). The Logic of Scientific Discovery, Science Editions (translation: original work published 1934).

  • Rowe, D. C. (1994). The Limits of Family Influence: Genes, Experience, and Behavior, Guilford Press, New York.

    Google Scholar 

  • SAS (1988). SAS/STAT User's Guide: Release 6.03, SAS Institute, Inc., Cary, NC.

    Google Scholar 

  • Savitz, D. A. (1993). Is statistical significance testing useful in interpreting data? Reprod Toxicol. 7:95–100.

    Google Scholar 

  • Silvey, S. D. (1975). Statistical Inference, Chapman and Hall, London.

    Google Scholar 

  • Venzon, D. J., and Moolgavkar, S. H. (1988). A method for computing profile-likelihood-based confidence intervals. ApplStat, 3.

Download references

Author information

Authors and Affiliations

  1. Department of Psychiatry, Medical College of Virginia, Box 980126, Richmond, Virginia, 23298

    Michael C. Neale

  2. Department of Psychiatry, Washington University School of Medicine, Box 8134, 4940 Children's Place, St. Louis, Missouri, 63110

    Michael C. Neale & Michael B. Miller

Authors
  1. Michael C. Neale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael B. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Neale, M.C., Miller, M.B. The Use of Likelihood-Based Confidence Intervals in Genetic Models. Behav Genet 27, 113–120 (1997). https://doi.org/10.1023/A:1025681223921

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1025681223921

Search

Navigation