Behavior Genetics

, Volume 23, Issue 3, pp 271–277

Estimating and controlling for the effects of volunteer bias with pairs of relatives

  • Michael C. Neale
  • Lindon J. Eaves
Article

Abstract

If pairs of relatives correlate in their liability to participate in a research project, it is possible to test for the effects of volunteering on the criterion variable of interest. Much of the information for this test comes from a difference in criterion variable mean between individuals with and those without a cooperative relative. Also, if data are available from more than one class of relative, it may be possible to discriminate between (i) volunteering that occurs as a consequence of the criterion variable and (ii) volunteering as a cause of the criterion. Likelihood formulae are presented that permit quantification and significance testing of volunteer bias. If data are collected from a genetically informative design such as a twin study, it is possible to estimate genetic and environmental parameters independent of the contaminating effects of such bias. We describe some methods of reducing the computational burden of multidimensional integration to allow extension to multivariate data. Implications for research design and management are discussed.

Key Words

Volunteer bias likelihood research design twin methodology family study 

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References

  1. Aitken, A. C. (1934). Note on selection from a multivariate normal population.Proc. Edinburgh Math. Soc. B.4:106–110.Google Scholar
  2. Curnow, R. N., and Dunnett, C. W. (1962). The numerical evaluation of certain multivariate normal integrals.Ann. Math. Stat. 33:571–579.Google Scholar
  3. Duffy, D. A., and Martin, N. G. (1993). Inferring the direction of causation in cross-sectional twin data: theoretical and empirical considerations.Gen Epidem. 10 (in press).Google Scholar
  4. Edwards, A. W. F. (1972).Likelihood, Cambridge University Press, Cambridge, England.Google Scholar
  5. Fisher, R. A. (1921). On the “probable error” of the correlation coefficient deduced from a small sample.Metron. 1:3–32.Google Scholar
  6. Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics.Phil. Trans. Roy. Soc. Lond. A 22:309–368.Google Scholar
  7. Heath, A. C., Neale, M. C., Hewitt, J. K., Eaves, L. J., and Fulker, D. W. (1989). Testing structural equation models for twin data using LISREL.Behav. Genet. 19:9–36.PubMedGoogle Scholar
  8. Heath, A. C., Kessler, R. C., Neale, M. C., Eaves, L. J. and Kendler, K. S. (1993). Testing hypothesis about direction of causation using cross-sectional family data.Behav. Genet. 23:29–49.PubMedGoogle Scholar
  9. Kendall, M., and Stuart, A. (1979).The Advanced Theory of Statistics, Vol. 2, Macmillan, New York.Google Scholar
  10. Kendler, K. S., and Kidd, K. K. (1986). Recurrence risks in an oligogenic threshold model: The effect of alterations in allele frequency.Ann. Hum. Genet. 50:83–91.PubMedGoogle Scholar
  11. Jöreskog, K. G., and Sörbom, D. (1986).PRELIS: A Preprocessor for LISREL; Scientific Software Inc., Chicago.Google Scholar
  12. Lykken, D. T., Tellegen, A., and De Rubeis, R. (1978). Volunteer bias in twin research; The rule of two-thirds.Soc. Biol. 25:1–9.PubMedGoogle Scholar
  13. Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979).Multivariate Analysis, Academic Press, New York.Google Scholar
  14. Martin, N. G., and Eaves, L. J. (1977). The genetical analysis of covariance structures.Heredity 38:79–95.PubMedGoogle Scholar
  15. Martin, N. G., and Wilson, S. R. (1982). Bias in the estimation of heritability from truncated samples of twins.Behav. Genet. 12:467–472.PubMedGoogle Scholar
  16. NAG (1990).Numerical Algorithms Group, FORTRAN Library Manual, Mark 14, NAG, Oxford.Google Scholar
  17. Neale, M. C. (1986). Handedness in a sample of volunteer twins.Behav. Genet. 18:69–79.Google Scholar
  18. Neale, M. C. (1991).Mx: A Package for Statistical Modeling, Genetics and Human Development Technical Report, Box 3 MCV, Richmond, VA.Google Scholar
  19. Neale, M. C. and Cardon, L. R. (1992).Methodology for Genetic Studies of Twins and Families, Kluwer Academic, Dordrecht, NL.Google Scholar
  20. Neale, M. C., Eaves, L. J., Kendler, K. S., and Hewitt, J. K. (1989). Bias in correlations from truncated samples of relatives.Behav. Genet. 19:163–169.PubMedGoogle Scholar
  21. Neale, M. C., Walters, E. W., Heath, A. C., Kessler, R. C., Pérusse, D., Eaves, L. J., and Kendler, K. S. (1993). Depression and parental bonding: cause, consequence, or genetic covariance?Gen. Epidem.10 (in press).Google Scholar
  22. Pearson, K. (1900). Mathematical contributions to the theory of evolution. VIII. On the correlation of characters not quantitatively measurable.Proc. Roy. Soc. 66:316–323.Google Scholar
  23. Schervish, M. J. (1984). Multivariate normal probability with error bounded.Appl. Stat. 33:81–94.Google Scholar
  24. Von Eye, A. (1989). Zero-missing nonexisting data: missing data problems in logitudinal research and categorical data solutions. In M. Brambing, F. Lösel, and H. Skowronek (eds.),Children at Risk: Assessment, Logitudinal Research and Intervention, Walter de Gruyter, New York.Google Scholar
  25. Wozniakowski, H. (1986). Information-based complexity.Annu. Rev. Comp. Sci. 1:319–380.Google Scholar

Copyright information

© Plenum Publishing Corporation 1993

Authors and Affiliations

  • Michael C. Neale
    • 1
  • Lindon J. Eaves
    • 1
    • 2
  1. 1.Department of Human GeneticsMedical College of VirginiaRichmond
  2. 2.Department of PsychiatryMedical College of VirginiaRichmond

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