Skip to main content
placeholder image

Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives

Journal Article


Abstract


  • © 2020 Royal Statistical Society The paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose a novel antithetic variable method to generate samples from the posterior distribution of the random effects and regression coefficients, resulting in significant gains in efficiency. We apply the methodology by analysing stated preference data obtained from Australian general practitioners evaluating alternative contraceptive products. Our analysis suggests that the joint probability of discussing combinations of contraceptive products with a patient shows medical practice variation among the general practitioners, which indicates some resistance even to discuss these products, let alone to recommend them.

UOW Authors


  •   Chin, Vincent (external author)
  •   Gunawan, David
  •   Fiebig, Denzil (external author)
  •   Kohn, Robert (external author)
  •   Sisson, Scott (external author)

Publication Date


  • 2020

Citation


  • Chin, V., Gunawan, D., Fiebig, D., Kohn, R. & Sisson, S. (2020). Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives. Journal of the Royal Statistical Society. Series C: Applied Statistics,

Scopus Eid


  • 2-s2.0-85077867937

Place Of Publication


  • United Kingdom

Abstract


  • © 2020 Royal Statistical Society The paper considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high dimensional correlation matrix and improving the overall efficiency of the data augmentation approach. We reparameterize the correlation matrix in a principled way and then carry out efficient Bayesian inference by using Hamiltonian Monte Carlo sampling. We also propose a novel antithetic variable method to generate samples from the posterior distribution of the random effects and regression coefficients, resulting in significant gains in efficiency. We apply the methodology by analysing stated preference data obtained from Australian general practitioners evaluating alternative contraceptive products. Our analysis suggests that the joint probability of discussing combinations of contraceptive products with a patient shows medical practice variation among the general practitioners, which indicates some resistance even to discuss these products, let alone to recommend them.

UOW Authors


  •   Chin, Vincent (external author)
  •   Gunawan, David
  •   Fiebig, Denzil (external author)
  •   Kohn, Robert (external author)
  •   Sisson, Scott (external author)

Publication Date


  • 2020

Citation


  • Chin, V., Gunawan, D., Fiebig, D., Kohn, R. & Sisson, S. (2020). Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives. Journal of the Royal Statistical Society. Series C: Applied Statistics,

Scopus Eid


  • 2-s2.0-85077867937

Place Of Publication


  • United Kingdom