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Modelling causal pathways in
health services
S. Watson and R. Lilford
University of Warwick
CLAHRC WM Scientific Advisory
...
What is the problem?
Causal effects of generic service interventions
Multiple data of different types
To inform decision m...
Brown et al. Qual Saf Health Care. 2008;17:178-81.
Brown & Lilford. BMJ. 2008;337:a2764.
Policy
Targeted
service
process
C...
Generic
Processes
Death
Adverse
events
Patient
satisfaction
Brown et al. Qual Saf Health Care. 2008;17:178-81.
Lilford et ...
Generic
intervention
Mediating
variable
Errors AEs QoL
Δ1 Δ2
Δ3
+Δ = +Qualitative +
How can we make use of all the
observations in a multi-level,
multi-method study?
Bayesian Modelling
Lilford & Braunholtz....
Method 1: Mental Integration Alone
Systematic review
Theoretical knowledge
Multi-level / multi-
method observation
Bias
Bayesian elicitation for intervention
to reduce adverse events after
discharge from hospital
Relative risk reduction preve...
Generic
intervention
Mediating
variable
Errors AEs
Δ1 Δ2
++ +
Method 2: Bayesian Causal
Network Analysis
Generic
intervention
Mediating
variable
Errors AEs
Method 3:Intermediate methods
Qualitative
+
++
Factor Bias
Start with meta-
regression data
Method 1
Method 2
Update mathematically
(Turner & Spiegelhalter)
Elicit
distr...
Modelling causal pathways in health services part 1, Prof Richard Lilford
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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

Publié dans : Santé & Médecine
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Modelling causal pathways in health services part 1, Prof Richard Lilford

  1. 1. Modelling causal pathways in health services S. Watson and R. Lilford University of Warwick CLAHRC WM Scientific Advisory Group – June 2015
  2. 2. What is the problem? Causal effects of generic service interventions Multiple data of different types To inform decision models
  3. 3. 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
  4. 4. 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
  5. 5. Generic intervention Mediating variable Errors AEs QoL Δ1 Δ2 Δ3 +Δ = +Qualitative +
  6. 6. 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.
  7. 7. Method 1: Mental Integration Alone Systematic review Theoretical knowledge Multi-level / multi- method observation Bias
  8. 8. 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.
  9. 9. Generic intervention Mediating variable Errors AEs Δ1 Δ2 ++ + Method 2: Bayesian Causal Network Analysis
  10. 10. Generic intervention Mediating variable Errors AEs Method 3:Intermediate methods Qualitative + ++
  11. 11. Factor Bias Start with meta- regression data Method 1 Method 2 Update mathematically (Turner & Spiegelhalter) Elicit distribution for bias Update mathematically

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