Behavior Genetics

, Volume 30, Issue 1, pp 41–50 | Cite as

Evaluation and Extensions of a Structural Equation Modeling Approach to the Analysis of Survival Data

  • Samuel F. Posner
  • Laura Baker


Recently a new method for the analysis of survival data using a structural equation modeling approach has been suggested by Pickles and colleagues using twin data they demonstrated the application of this model to study the correlation in age of onset. The purpose of the current research is twofold: 1) to evaluate the statistical performance of the model as presented by Pickles and colleagues, and 2) to expand and evaluate the model in more applications, including both genetically informative data and other multivariate examples. Results evaluated from this study involve three areas of method performance: Type-I error rates, power, and parameter estimates under four different distributions (normal, Gamma-2, Gamma-6 and g-and-h) and four different sample sizes (n = 125, 250, 500 and 750). Results based on the original Pickles model indicated that in all sample size and distribution conditions the Type-I error rate was adequate, in fact below the nominal level of .05. Additionally, power was greater than .80 for sample sizes of 500 or more for all distribution conditions. Parameter estimates were upwardly biased when the population value was ρ = .20. This bias varied across distributions; the g-and-h distribution showed the largest bias. Results from the expanded model indicated that Type-I error rates were adequate. Power results were not affected by distribution type; sample sizes of 500 were above the .80 level. Parameter estimates continued to be upwardly biased in this more general model, although the degree of bias was smaller.

Structural equation modeling survival analysis genetically informative data power Type I error 


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  1. Akaike, H. (1987). Factor analysis and AIC. Psychometrica 52:317–332.Google Scholar
  2. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychol. Bull. 88:588–606.Google Scholar
  3. Bradley, J. V. (1978). Robustness?Brit. J. Math. Stat. Psychol. 31: 144–152.Google Scholar
  4. Cox, D. R. (1972). Regression models and life-tables. J. Royal Stat. Soc. 34:187–220.Google Scholar
  5. Czirjak, L., Nagy, Z., and Szegedi, G. (1993). Survival analysis of 118 patients with systemic sclerosis.J. Int. Med. 234:335–337.Google Scholar
  6. Hoaglin, D. C., Mosteller, F., and Tukey, J. W. (1985). Exploring Data Trends, Tables and Shapes, Wiley Interscience, New York.Google Scholar
  7. Hewitt, J. K., Siberg, J. L., Rutter, M., Simonoff, E., Meyer, J. M., Maes, H., Pickles, A., Neale, M. C., Loeber, R., Erickson, M., Kendler, K. S., Heath, A. C., Truett, K. R., Reynolds, C. A., Eaves, L. J. (1997). Genetics and developmental psychopathology: 1. Phenotypic assessment in the Virginia Twin Study of Adolescent Behavioral Development. J. Child Psychol. Psychiatr. 38:943–963.Google Scholar
  8. Mack, W., Langholz, B., and Thomas, D. (1990). Survival models for familial aggregation of cancer. Environ. Health Perspect. 87:27–35.Google Scholar
  9. Meyer, J., Eaves, L., Heath, A., and Martin, N. (1991). Estimating genetic influences on the age-at-menarche: A survival analysis approach. Am. J. Med. Genet. 392:148–154.Google Scholar
  10. Meyer, J. M., and Neale, M. C. (1992). The relationship between age at first drug use and teenage drug use liability. Behav. Genet. 22:197–213.Google Scholar
  11. Meyer, J. M., Leaves, L. J., Heath, A. C., and Martin, N. G. (1991). Estimating genetic influences on the age-at-menarche: A survival analysis approach. Am. J. Med. Genet. 39:148–154.Google Scholar
  12. Neale, M. (1991). Mx: A Structural Equation Modeling Software, Virginia Commonwealth Medical School.Google Scholar
  13. Neale, M., and Cardon, L. (1992). Methodology for Genetic Studies of Twins and Families, Kluwer Academic Publishers, Boston.Google Scholar
  14. Neale, M., Eaves, L. J., and Kendler, K. (1994). The power of the classical twin study to resolve variation in threshold traits. Behav. Genet. 24:239–258.Google Scholar
  15. Pickles, A., Crouchley, R., Simonoff, E., Eaves, L. J., Meyer, J., Rutter, M., Hewitt, J., and Silberg, J. (1994a). Survival models for developmental genetic data: Age of onset of puberty and antisocial behavior in twins. Genet. Epidemiol. 11:155–170.Google Scholar
  16. Pickles, A., Neale, M. C., Simonoff, E., Rutter, M., Hewitt, J., Meyer, J., Crouchley, R., Silberg, J., and Eaves, L. J. (1994b). A simple method for censored age-of-onset data subject to recall bias: Mothers reports of age of puberty in male twins. Behav. Genet. 24:457–468.Google Scholar

Copyright information

© Plenum Publishing Corporation 2000

Authors and Affiliations

  • Samuel F. Posner
    • 1
  • Laura Baker
    • 2
  1. 1.Department Obstetrics, Gynecology and Reproductive SciencesUniversity of CaliforniaSan Francisco
  2. 2.Department of PsychologyUniversity of Southern CaliforniaUSA

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