# Methods of multi-indication meta-analysis for health technology assessment: A simulation study

## TL;DR

This paper explores new statistical methods to improve health assessments by combining evidence across multiple cancer treatments and patient groups.

## Contribution

The study introduces and evaluates multi-indication synthesis models for health technology assessment, particularly in oncology.

## Key findings

- Univariate multi-indication methods reduce uncertainty without increasing bias when OS data are available in the target indication.
- Bivariate surrogacy models show promise in correcting bias in scenarios with missing OS data and outlier indications.
- Mixture models do not significantly improve performance and are not recommended for health technology assessment.

## Abstract

A growing number of oncology treatments, such as bevacizumab, are used across multiple indications. However, in health technology assessment (HTA), their clinical and cost-effectiveness are typically appraised within a single target indication. This approach excludes a broader evidence base across other indications. To address this, we explored multi-indication meta-analysis methods that share evidence across indications.

We conducted a simulation study to evaluate alternative multi-indication synthesis models. This included univariate (mixture and non-mixture) methods synthesizing overall survival (OS) data and bivariate surrogacy models jointly modeling treatment effects on progression-free survival (PFS) and OS, pooling surrogacy parameters across indications. Simulated datasets were generated using a multistate disease progression model under various scenarios, including different levels of heterogeneity within and between indications, outlier indications, and varying data on OS for the target indication. We evaluated the performance of the synthesis models applied to the simulated datasets in terms of their ability to predict OS in a target indication.

The results showed univariate multi-indication methods could reduce uncertainty without increasing bias, particularly when OS data were available in the target indication. Compared with univariate methods, mixture models did not significantly improve performance and are not recommended for HTA. In scenarios where OS data in the target indication is absent and there are also outlier indications, bivariate surrogacy models showed promise in correcting bias relative to univariate models, though further research under realistic conditions is needed.

Multi-indication methods are more complex than traditional approaches but can potentially reduce uncertainty in HTA decisions.