Statistical Modelling 1 (2001), 319–331
Fitting complex random
effect models with standard software using data augmentation: Application to a
study of male and female fecundability.
René Ecochard
Département d'Information Médicale
Hospices Civils de Lyon, France
email: rene.ecochard@chu-lyon.fr
David G. Clayton
Medical Research Council
Biostatistics Unit, Cambridge, U.K.
Abstract:
We discuss fitting
of a complex random effect model using a standard statistical package (Stata)
to carry out block-wise Gibbs sampling within a multi-processor computing
environment. The application involves a dataset concerning artificial
insemination by donor (AID). Success or failure at each of 12 100 menstrual
cycles is modelled with a mixed model with random effects due to woman,
conception attempt within woman, semen donor, donation within donor, and
physician who carries out the treatment. Given the availability of software
within Stata to fit a model with single random effect, the full model can be
fitted by an alternating imputation algorithm (Clayton and Rasbash, 1999)
implemented with five copies of Stata running on separate processors and
communicating via disk files.
Keywords:
Fecundability, Frailty models, Gibbs sampling, Parallel algorithms,
Random effects, Statistical Computing.
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