This document summarizes a novel method for incorporating external evidence from other studies into trial-based economic evaluations. It discusses the context of using economic trials to evaluate competing treatments but notes issues like truncated time horizons and failure to include all available evidence. It then presents an example using the OPTIMAL trial on COPD treatments, and describes how the new method allows incorporating external meta-analysis results into the trial-based cost-effectiveness analysis through adjustments to the bootstrap sampling. The method aims to address criticisms of ignoring external data while still using trial data and bootstrap methods familiar to trialists and economists. It concludes the approach is best for sensitivity analyses but may have computational limitations with many parameters.

1. How to integrate external evidence into within trial
economic evaluations?
Mohsen Sadatsafavi MD, PhD
University of British Columbia
2015.04.13

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Conflict of Interest
No apparent or perceived conflict of interest
Received CIHR fellow-ship award for this work
All interpretations are my own
Our Team
Carlo Marra, Shawn Aaron, Stirling Bryan

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Outline
• A novel method for incorporating external
evidence in economic evaluations
• Context in which it is applicable
• What it does
• A running example
• Pros + Cons + conclusions

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Economic trials
as vehicles for evaluations
• Model-based evaluations
• Evidence is ‘parameterized’ and fed into a computer
program (mathematical function)
• simulation
• Trial-based evaluations
• Individuals are assigned to competing treatments and
their experiences is represented through cost and
effectiveness values
• (bivariate) statistical inference
• bootstrap as s popular paradigm

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Issues with trial-based CEAs
 Failure to compare all available options
 A truncated time horizon
 Lack of relevance to the jurisdiction of
interest
 Failure to incorporate all evidence
 the most damning criticism

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Context
An example: OPTIMAL trial in COPD
• Mono, double, and triple inhaler therapy in COPD (T1,
T2, T3)
• N=442
• Follow-up 1 year
• Primary outcome: exacerbation rate
• Prospective economic evaluation component
• Results
– RR of T2 v. T1: 1.01 (0.59 – 0.73)
– RR of T3 v. T1: 0.84 (0.47 – 1.49)

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Context
OPTIMAL CEA
• Sequence of imputation, regression, outcome calculations
within a bootstrap envelope

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Trialist versus economist
Treatment effects (RR=0.84, NS) ICER=243K
Pooled estimate
Regression-based methods Bootstrap methods
OPTIMAL trial
Trialist Economist
Decision maker
Welte et. al. T3 v. T1
RR=0.38 (0.28 – 0.57)
Meta-analyst

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CONTEXT
Reconciling external evidence in RCT-based CEAs
Desist RCT-based CEA and resort to model-based evaluation
Switch to parametric Bayesian evidence synthesis
MCMC using WinBUGS (technical, lots of programing, model
convergence etc)
Stay with the bootstrap-based methods, incorporate external evidence

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Methods
RCT-CEA without external evidence

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Methods
RCT-CEA with external evidence

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Results
Case study in COPD

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Results
Case study in COPD

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Conclusions
• Theoretically, possible to use trials as ‘vehicle’ for
evidence synthesis, yet avoid the ‘most damning criticism’
• Practically, with too many parameters, it can be
computationally prohibitive
• Best use is in sensitivity and secondary analyses
• What if we incorporate the results of the study by xyz?
• The experts in our team believe it is almost impossible
that chemo+radiation increases the risk of metastasis
->half-flat priors

15. Faculty
Mohsen Sadatsafavi
J Mark FitzGerald
Stirling Bryan
Larry Lynd
Research Staff
Hamid Tavakoli
Tania Conte
Roxanne Rousseau
Students
Zafar Zafari
Wenjia Chen
Thank You!
msafavi@mail.ubc.ca
Respiratory Evaluation Sciences Program (resp.med.ubc.ca)