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

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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.

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References

  • Aitken, A. C. (1934). Note on selection from a multivariate normal population.Proc. Edinburgh Math. Soc. B.4:106–110.

    Google Scholar 

  • Curnow, R. N., and Dunnett, C. W. (1962). The numerical evaluation of certain multivariate normal integrals.Ann. Math. Stat. 33:571–579.

    Google Scholar 

  • 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).

  • Edwards, A. W. F. (1972).Likelihood, Cambridge University Press, Cambridge, England.

    Google Scholar 

  • Fisher, R. A. (1921). On the “probable error” of the correlation coefficient deduced from a small sample.Metron. 1:3–32.

    Google Scholar 

  • Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics.Phil. Trans. Roy. Soc. Lond. A 22:309–368.

    Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • Kendall, M., and Stuart, A. (1979).The Advanced Theory of Statistics, Vol. 2, Macmillan, New York.

    Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • Jöreskog, K. G., and Sörbom, D. (1986).PRELIS: A Preprocessor for LISREL; Scientific Software Inc., Chicago.

    Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • Mardia, K. V., Kent, J. T., and Bibby, J. M. (1979).Multivariate Analysis, Academic Press, New York.

    Google Scholar 

  • Martin, N. G., and Eaves, L. J. (1977). The genetical analysis of covariance structures.Heredity 38:79–95.

    PubMed  Google Scholar 

  • Martin, N. G., and Wilson, S. R. (1982). Bias in the estimation of heritability from truncated samples of twins.Behav. Genet. 12:467–472.

    PubMed  Google Scholar 

  • NAG (1990).Numerical Algorithms Group, FORTRAN Library Manual, Mark 14, NAG, Oxford.

    Google Scholar 

  • Neale, M. C. (1986). Handedness in a sample of volunteer twins.Behav. Genet. 18:69–79.

    Google Scholar 

  • Neale, M. C. (1991).Mx: A Package for Statistical Modeling, Genetics and Human Development Technical Report, Box 3 MCV, Richmond, VA.

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

    Google Scholar 

  • 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.

    PubMed  Google Scholar 

  • 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).

  • 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 

  • Schervish, M. J. (1984). Multivariate normal probability with error bounded.Appl. Stat. 33:81–94.

    Google Scholar 

  • 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 

  • Wozniakowski, H. (1986). Information-based complexity.Annu. Rev. Comp. Sci. 1:319–380.

    Google Scholar 

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Neale, M.C., Eaves, L.J. Estimating and controlling for the effects of volunteer bias with pairs of relatives. Behav Genet 23, 271–277 (1993). https://doi.org/10.1007/BF01082466

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