Machine Learning Methods to Estimate Individualised Treatment Effects for Use in Health Technology Assessment

Yingying Zhang, Noemi Kreif, Vijay S. GC, Andrea Manca

Research output: Contribution to journalArticlepeer-review

Abstract

Background Recent developments in causal inference and machine learning (ML) allow for the estimation of individualised treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients’ observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment.
Methods In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorised by key challenges in health technology assessment using observational data, including handling time-varying confounding
and time-to event data, and quantifying uncertainty.
Results We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates.
Limitations This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving.
Conclusions Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates.
Implications ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments.
Original languageEnglish
JournalMedical Decision Making
Publication statusAccepted/In press - 18 May 2024

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