Modelling causal pathways in health services part 1, Prof Richard Lilford
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The first of a two-part talk from Richard Lilford and Sam Watson on modelling causal pathways in health services for the CLAHRC West Midlands Scientific Advisory Group meeting, 9th June 2015, Birmingham, UK
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Modelling causal pathways in health services part 1, Prof Richard Lilford
- 1. Modelling causal pathways in health services S. Watson and R. Lilford University of Warwick CLAHRC WM Scientific Advisory Group – June 2015
- 2. What is the problem? Causal effects of generic service interventions Multiple data of different types To inform decision models
- 4. Brown et al. Qual Saf Health Care. 2008;17:178-81. Brown & Lilford. BMJ. 2008;337:a2764. Policy Targeted service process Clinical process Patient Outcome Generic service process Classifying Health Interventions
- 5. Generic Processes Death Adverse events Patient satisfaction Brown et al. Qual Saf Health Care. 2008;17:178-81. Lilford et al. BMJ. 2010;341:c4413. Targeted Service Processes Clinical Processes QoL End-Points
- 6. Generic intervention Mediating variable Errors AEs QoL Δ1 Δ2 Δ3 +Δ = +Qualitative +
- 7. How can we make use of all the observations in a multi-level, multi-method study? Bayesian Modelling Lilford & Braunholtz. BMJ. 1996; 313: 603-7. Lilford, et al. BMJ. 2010; 341: c4413. Yao et al. BMJ Qual Saf. 2012; 21: i29-38. Hemming et al. PLoS One. 2012; 7(6): e38306. Lilford et al. BMC Health Serv Res. 2014; 14: 314.
- 8. Method 1: Mental Integration Alone Systematic review Theoretical knowledge Multi-level / multi- method observation Bias
- 9. Bayesian elicitation for intervention to reduce adverse events after discharge from hospital Relative risk reduction preventable adverse events – priors from 24 experts Pooled ‘prior’ for risk reduction of adverse events Yao et al. BMJ Qual Saf 2012; 21: i29-38. Hemming et al. PLoS ONE. 2012; 7(6):e38306.
- 10. Generic intervention Mediating variable Errors AEs Δ1 Δ2 ++ + Method 2: Bayesian Causal Network Analysis
- 11. Generic intervention Mediating variable Errors AEs Method 3:Intermediate methods Qualitative + ++
- 12. Factor Bias Start with meta- regression data Method 1 Method 2 Update mathematically (Turner & Spiegelhalter) Elicit distribution for bias Update mathematically
Editor's Notes
- Title: Bayesian elicitation for intervention to reduce adverse events after discharge from hospital Sub-Title: Relative risk reduction preventable adverse events – priors from 24 experts ------------------------- Title 2: Pooled ‘prior’ for risk reduction of adverse events Sub-Title 2: Yao et al. BMJ Qual Saf 2012; 21: i29-38.