Modelling causal pathways in health services

S. Watson and R. LilfordUniversity of Warwick

CLAHRC WM Scientific Advisory Group – June 2015

What is the problem?

Causal effects of generic service interventions

Multiple data of different types

To inform decision models

Brown et al. Qual Saf Health Care. 2008;17:178-81.Brown & Lilford. BMJ. 2008;337:a2764.

PolicyTargeted service process

Clinical process

PatientOutcome

Genericserviceprocess

Classifying Health Interventions

GenericProcesses

Death

Adverseevents

Patientsatisfaction

Brown et al. Qual Saf Health Care. 2008;17:178-81.Lilford et al. BMJ. 2010;341:c4413.

TargetedService

Processes

ClinicalProcesses

QoL

End-Points

Genericintervention

Mediatingvariable

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. 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.

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 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.

Genericintervention

Mediatingvariable

Errors AEs

Δ1 Δ2

++ +

Method 2: Bayesian Causal Network Analysis

Genericintervention

Mediatingvariable

Errors AEs

Method 3:Intermediate methods

Qualitative

+++

Factor Bias

Start with meta-regression data

Method 1

Method 2

Update mathematically (Turner & Spiegelhalter)

Elicit distribution for

bias

Update mathematically