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Computations for the familial analysis of binary traits


For familial aggregation of a binary trait, one method that has been used is the GEE2 (generalized estimating equation) method corresponding to a multivariate logit model. We solve the complex estimating equations for the GEE2 method using an automatic differentiation software which computes the derivatives of a function numerically using the chain rule of the calculus repeatedly on the elementary operations of the function. Based on this, we are able to show in a simulation study that the GEE2 estimates are quite close to the maximum likelihood estimates assuming a multivariate logit model, and that the GEE2 method is computationally faster when the dimension or family size is larger than four.

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This research is supported from an NSERC Canada grant and US Army Medical Research grant NF990038. We are grateful to the referees for comments leading to an improved presentation.

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Joe, H., Mahbub-ul Latif, A.H.M. Computations for the familial analysis of binary traits. Computational Statistics 20, 439–448 (2005).

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  • Binary response
  • Multivariate probit models
  • Multivariate logit models
  • Generalized estimating equations
  • Familial aggregation