14 Further reading

NICE process and methods

The following is a list of useful references on the methods outlined in this section.

Ades AE, Lu G, Higgins JPT (2005) The interpretation of random effects meta-analysis in decision models. Medical Decision Making 25: 646–54

Dias S, Welton NJ, Marinho V et al. (2010) Estimation and adjustment of bias in randomised evidence using mixed treatment comparison meta-analysis. Journal of the Royal Statistical Society Series A 173: 613–29

Higgins JPT, Thompson SG, Spiegelhalter DA (2009) A re-evaluation of random effects meta-analysis. Journal of the Royal Statistical Society Series A 172: 137–59

Kotiadis K, Tako AA, Vasilakis C. (2014) A participative and facilitative conceptual modelling framework for discrete event simulation studies in healthcare. JORS 65, 197–213

Robinson S. (2008) Conceptual modelling for simulation Part I: definition and requirements. JORS 59:278-290

Salanti G, Dias S, Welton NJ et al. (2010) Evaluating novel agent effects in multiple-treatments meta-regression. Statistics in Medicine 29: 2369–83

Spiegelhalter D (2002) Funnel plots for institutional comparison. Quality and Safety in Health Care 11: 390–2

Spiegelhalter DJ (2005) Funnel plots for comparing institutional performance. Statistics in Medicine. 24: 1185–202

Spiegelhalter DJ (2005) Handling over-dispersion of performance indicators. Quality and Safety in Health Care 14: 347–51

Spiegelhalter, DJ, Abrams, KR, Myles, JP (2004) Bayesian approaches to clinical trials and health-care evaluation. Chichester: Wiley

Turner RM, Spiegelhalter D, Smith GCS et al. (2009) Bias modelling in evidence synthesis. Journal of the Royal Statistical Society Series A 172: 21–47

Welton NJ, Ades AE, Caldwell DM et al. (2008) Research prioritisation based on expected value of partial perfect information: a case-study on interventions to increase uptake of breast cancer screening. Journal of the Royal Statistical Society Series A 171: 807–41 (with discussion)

Welton NJ, Ades AE, Carlin JB, et al (2009) A bias model for the combination of low and high quality evidence: empirically based priors. Journal of the Royal Statistical Society Series A 172: 119—36