Population adjustment with limited access to patient-level data
Health technology assessment (HTA) typically takes place late in the drug development process, after a new medical technology has obtained regulatory approval, in which new interventions are typically compared with placebo or standard of care in a randomised controlled trial. In this case, the policy question of interest is whether the drug is effective or not. In HTA, the relevant policy question is: “given that there are finite resources available to finance or reimburse health care, which is the best treatment of all available options in the market?”. However, many competing technologies have never been trialled against each other. In the absence of data from head-to-head controlled trials, indirect treatment comparisons are at the top of the hierarchy of evidence when assessing the relative effectiveness of interventions.
Standard indirect comparisons assume that there are no between-trial differences in the distribution of covariates influencing outcome. If this assumption is broken, the methods are liable to bias and over-precision. In addition, access to individual patient data is often limited, and popular balancing methods such as propensity score matching are unfeasible. In order to overcome these major limitations, several methods, labelled indirect comparisons have been proposed.
The use of population adjustment in HTA, both in published literature as well as in submissions for reimbursement, and its acceptability by national HTA bodies, is rapidly increasing across diverse therapeutic areas, particularly in oncology. Popular methods include matching-adjusted indirect comparison (MAIC) and simulated treatment comparison (STC). While these are well motivated and useful tools, they also have potential shortcomings. As a result, we propose a novel method based on multiple imputation called predictive-adjusted indirect comparison (PAIC).
This research project will focus on the following aspects:
- The development of novel methodology for population-adjusted indirect comparisons, including PAIC
- The modulation of information flow between each individual step of PAIC, which is a multi-stage modelling procedure
- Carrying out comprehensive simulation studies which examine the statistical properties of competing methods and benchmark the performance of PAIC in a wide range of settings. A complementary objective of these is to inform under which circumstances population adjustment should be applied and which specific method is preferable in different situations.
- Assessing the robustness of population adjustment to failures in assumptions under different degrees of data availability and model misspecification
- The preparation of software allowing the analyst to perform population adjustment routinely in specific case studies
- Examining potential strategies to alleviate the burden of limited patient-level data, e.g. the release of artificial versions of the data by manufacturers or trial reporting recommendations which facilitate approximating the original data at the individual level
- Bayesian Tree-Based Learners for Individualized Treatment Effects Estimation
- A Bayesian hierarchical framework to evaluate policy effects through quasi-experimental designs
- Bayesian survival modelling in health economic evaluation
- Full Bayesian methods to handle missing data in health economic evaluations
- Bayesian computations for Value of Information measures using Gaussian processes, INLA and Moment Matching