Title: Target trial emulation for transparent and robust estimation of treatment effects for health technology assessment using real-world data.
Funder: Department of Health and Social Care

Abstract: Research question How can target trial emulation (TTE) improve the estimation of treatment effects for health technology assessment (HTA) using real-world data (RWD)? Background HTA agencies, such as NICE, are increasingly using RWD to estimate the effectiveness and cost-effectiveness of health interventions, but unless HTA studies appropriately address the potential pitfalls arising from RWD, they will lead to biased estimates of treatment effects. By applying design and analysis principles from randomised controlled trials (RCTs), TTE can enable a more transparent and robust estimation of treatment effects from RWD. However, there are several methodological and practical challenges that need to be addressed to enable the adoption of TTE in HTA. AimTo exploit the target trial approach to enable transparent and robust estimation of treatment effects for HTA using RWD, and develop a roadmap for its implementation in HTA. Objectives 1. Extend the target trial approach to evaluate personalised, adaptive treatment strategies 2. Devise a framework to aid the analysis and interpretation of uncontrolled studies in HTA. 3. Exploit target trial emulation to improve indirect treatment comparisons. 4. Develop a roadmap for the adoption of TTE in HTA. MethodsWork-package 1 (months 1-15): Extend TTE to accommodate the personalisation of treatment strategies by incorporating: i) adaptive treatment strategies, ii) stratification of target trial according to key prognostic factors, and iii) new machine learning methods to improve the targeting of the right treatments for the right patients. Work-package 2 (months 12-27): Devise and illustrate a framework to improve the relevance and interpretation of uncontrolled studies, such as single-arm trials, and contextualise them with RWD. This will involve: i) develop protocol of target trial in line with research question of uncontrolled study, ii) derive relevant control group from RWD, iii) devise a strategy for sensitivity analysis to address discrepancies between the protocol and emulated target trial, and iv) develop general recommendations to inform future data collection. Work-package 3 (months 21-36): Demonstrate how the target trial approach can improve indirect treatment comparisons in the absence of head-to-head RCTs: i) allow head-to-head comparisons using routine datasets, ii) assess the plausibility of assumptions made by the evidence synthesis approaches, and iii) include comparisons of relevant outcomes not included across all RCTs. Work-package 4 (months 1-36): Develop accessible guidance, software tools, and training to facilitate the implementation of TTE in HTA. ImpactThis research will lead to better clinical and resource allocation decisions by improving treatment recommendations within NICE's HTA programme and clinical guideline development. By exploiting large-scale RWD, this research will provide measures of treatment effectiveness and cost-effectiveness that are of greater relevance to patients, practitioners and commissioners, because they will reflect more closely the diversity of patients and the care they receive in routine practice. More generally, the project will help researchers gain greater utility from routinely-collected data through more rigorous design and analysis of observational studies in HTA.

Lay abstract: Background: Evidence about the relative benefits of new treatments is often taken from well-conducted randomised controlled trials (RCTs). However, for many treatment decisions, RCT data are insufficient. For example, RCTs may not run for a long enough period to capture the longer-term effects of treatments. Government agencies are increasingly using information routinely collected about patients and the care they receive, for instance treatments prescribed by their GP, to complement RCT evidence on treatment effects. Unlike RCT data, routine data are not collected for research purposes, so investigators have less control over the way patients are selected into the study and allocated to the different treatment groups. In such studies, results about treatment effects may be misleading because (i) patients included in the study tend to be sicker than those not included, and (ii) doctors tend to give more innovative treatments to patients in poorer health. Therefore, policy-makers are worried that the inappropriate use and interpretation of routinely-collected data may lead to incorrect treatment decisions and poor use of taxpayer's money. A promising approach to address these concerns is to use design and analysis principles from RCTs in order to analyse routinely-collected data. This approach, known as 'target trial emulation', explicitly describes and imitates the RCT that would answer the question of interest, for example treatment effects, using routinely-collected data. While the potential benefits of target trial emulation are enormous, this approach has not been adopted in healthcare decision-making. Aims: This project will exploit the target trial approach to the design and analysis of studies that use routinely-collected data for evaluating treatment effects. The proposed research will enable more transparent and robust (less prone to errors) use of routinely-collected data to inform decisions about which treatments are most beneficial to NHS patients. Design and methods: I will demonstrate how target trial emulation can help address important evidence gaps in healthcare decision-making, where RCT evidence is insufficient. This will include: (i) targeting the right treatments to the right patients, (ii) treatment decisions using primarily non-RCT studies, and (iii) indirect comparisons of competing treatments. I will illustrate how the target trial approach can help improve the evaluation of treatments across important clinical areas, such as diabetes, arthritis and cancer. An important part of this project is to provide implementation tools and accessible guidance on target trial emulation to encourage its adoption by studies used for healthcare decision-making. Patient and Public involvement: Patient and public involvement (PPI) representatives at the NIHR Applied Research Collaboration (ARC) North Thames have contributed to the planning of this proposal. This PPI panel has assisted me with writing the lay summary, refining the research plans, and planning the dissemination strategy. The PPI representatives will continue to advise on the project as ensure it meets the needs of the patients. The patient viewpoint is particularly important for this research because it will help improve my understanding of the ways in which the data are collected in routine settings, and potential biases that may arise in that context. Dissemination: The findings of this research will be disseminated broadly to decision makers, policy advisors, clinical and academic communities, and patients and the public. For example, I will organise seminars and workshops with government agencies to discuss how the proposed work could help improve decisions about which treatments to provide in the NHS. I will run a practical workshop to illustrate the implementation of the proposed methods and encourage their uptake. As the research progresses, I will discuss with NIHR ARC North Thames PPI representatives what the results from this research mean, and how they can be best communicated to the public.

Research activity codes: 8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: GENERIC HEALTH RELEVANCE