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

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.
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.
Generic
intervention
Mediating
variable
Errors AEs QoL
Δ1 Δ2
Δ3
+Δ = +Qualitative +

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.
Method 1: Mental Integration Alone
Systematic review
Theoretical knowledge
Multi-level / multi-
method observation
Bias

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.
Generic
intervention
Mediating
variable
Errors AEs
Δ1 Δ2
++ +
Method 2: Bayesian Causal
Network Analysis

10.
Generic
intervention
Mediating
variable
Errors AEs
Method 3:Intermediate methods
Qualitative
+
++

11.
Factor Bias
Start with meta-
regression data
Method 1
Method 2
Update mathematically
(Turner & Spiegelhalter)
Elicit
distribution for
bias
Update
mathematically