Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis.

By N. Cooper, Paul C. Lambert, K. Abrams, A. Sutton. 2007

54 citations recorded by the source corpus.

Subfields: Genetics, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Anthony OʼHagan
2. S. Thompson
3. Stephen Burgess
4. J. Bowden
5. George Davey Smith
6. E. Sanderson
7. O. Yavors’ka
8. Nuala Sheehan

In Statistics and Probability

1. Anthony OʼHagan
2. A. Briggs
3. B. Mihaylova
4. S. Thompson
5. A. O’Malley
6. Brian H Neelon
7. Sharon-Lise T. Normand
8. Paul T. von Hippel

Citations in the corpus

• A Bayesian model for repeated measures zero-inflated count data with application to outpatient psychiatric service use (2010, methodology): “As an alternative, one can specify a product normal parameterization for the random effects (Spiegelhalter, 1998; Cooper et al., 2007) b1i ∼ N(0, η21) and b2i |b1i ∼ N(ψb1i , η22), with diffuse hyperpriors such as ψ ∼ N(0, 103) and η21, η22 ∼ Inv-Gamma(0.001, 0.001).”
• Review of Statistical Methods for Analysing Healthcare Resources and Costs (2010, methodology): “…included an informal comparison of different methods and approaches applied to the same data (Austin et al., 2003; Briggs and Gray, 1998; Buntin and Zaslavsky, 2004; Chen and Zhou, 2006; Cooper et al., 2007; Lipscomb et al., 1998; Manning and Mullahy, 2001; O’Hagan and Stevens, 2003; Zhou, 2002).”