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Sensitivity of recommendations based on cost-effectiveness analysis to bias in relative treatment effect estimates


Bristol Medical School

, Applications accepted all year round Self-Funded PhD Students Only

About the Project

Rationale
In the UK, decisions as to which medical treatments and interventions to make available on the NHS take into consideration both the costs and benefits of the treatment over a patient’s lifetime. This is usually based on a cost-effectiveness analysis which identifies the treatment with the highest expected net benefit. Key inputs to cost-effectiveness models are the relative effects of the different treatment options on clinical outcomes. When there are multiple studies available on multiple treatments, these relative effects are estimated using network meta-analysis (Dias et al 2018). The validity of the results of network meta-analysis relies on the validity of the studies on which it is based. Potential biases in the included studies may result in biased relative effect estimates, and in turn biased estimates of cost-effectiveness which may mean sub-optimal decisions are made. Decision makers therefore need to understand how robust cost-effectiveness results are to potential biases in the studies on which the relative effects are based.

Aims & Objectives
This project aims to develop computational methods to assess how robust recommendations based on cost-effectiveness models are to the relative effect inputs from a network meta-analysis.

Methods
A threshold analysis will be conducted to assess the impact of bias in the network meta-analysis evidence from a decision-making perspective. The results of a threshold analysis are a set of invariant thresholds, within which changes to the evidence do not result in a change to the treatment recommendations. A threshold analysis method has previously been developed for the case where the net benefit function is based on a single measure of treatment efficacy (Phillippo et al 2018). This project will extend the methods to the case where decisions are based on more complex net-benefit functions.

The project will draw on recent methodological developments for value of information analysis (Heath et al. 2018), such as generalised additive models, integrated nested Laplace approximations, and multi-level Monte-Carlo methods. These meta-modelling techniques have the potential to obtain fast and accurate computational tools that can evaluate the thresholds of input parameters for which decisions change, based on maximising any kind of net benefit function.

An important output of the project will be the creation of software tools (using R and R Shiny) to allow easy application for health economists without advanced statistical training.

The methods will be applied to examples from the National Institute of Health and Care Excellence (NICE) guidelines. Case studies may include social anxiety, non-small cell lung-cancer, headaches, and atrial fibrillation.

References

1. Phillippo DM, Dias S, Ades AE, Didelez V, Welton NJ. Sensitivity of treatment recommendations to bias in network meta-analysis. JRSSA. 2018. 181:843-867. https://doi.org/10.1111/rssa.12341

2. Heath A, Manolopoulou I, Baio G. A Review of Methods for Analysis of the Expected Value of Information. Med. Dec. Making. 2017. 37: 747-758 https://doi.org/10.1177/0272989X17697692

3. Dias S, Ades AE, Welton NJ, Jansen JP, Sutton AJ. Network Meta-analysis for Comparative Effectiveness Research. Wiley. Hoboken NJ. 2018.

4. https://www.nice.org.uk/guidance/ng122

5. https://www.nice.org.uk/guidance/cg159

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