arXiv subjects

S Palmer

Publications and source records attributed to S Palmer.

3 recordsLinked to original sources

arXiv2026

A framework for assessing value and heterogeneity, illustrated using an early model of population screening with a multi-cancer early detection test

Introduction: We present a framework to assess the economic value of healthcare interventions by disaggregating value and examining heterogeneity. We applied it to an early health-economic model of population screening in England with a multi-cancer early detection (MCED) test. Value for such technologies often includes benefits, such as those associated with earlier detection, alongside potential harms from, for example, false positives or overdiagnosis. Value also varies between individuals, including across cancer types and stages. Understanding these components and heterogeneity is crucial for assessing overall value and prioritising future research. Methods: We adapted an existing decision-analytic model to simulate annual Galleri screening in an asymptomatic population, measuring outcomes in Quality Adjusted Life Years (QALYs) from an English NHS perspective (year of 2024). We disaggregate headroom value (assuming no cost to the test) into key components: cancer identification (pre- and post-diagnosis), false positives, overdiagnosis, and misclassification. Post diagnosis value was further disaggregated by cancer type to explore heterogeneity. Results: Our analysis predicts an overall estimated headroom value of 0.135 QALYs per individual. Early cancer identification was a major contributor, driven by health gains rather than cost savings. Cancers of the colon/rectum, lung and ovary were the largest contributors, accounting for 50% of overall value. These results remained robust across scenario analyses. Conclusion: The value disaggregation can guide decision making by clarifying value drivers, assessing plausibility of overall estimates, exploring heterogeneity, and prioritising future model and evidence development activities.

econ.EM↗

arXiv2026

Modelling multi-cancer screening data to infer on natural history of disease: when can valid, identifiable and precise inference be obtained?

Background: Multistate models (MSMs) applied to screening data can characterise the natural history of cancer and predict "stage-shifts" from screening. However, inferring parameters like mean sojourn time (MST) is challenging as disease onset is inherently unobserved in these data. This is even more challenging when characterising heterogeneity between cancer types in multicancer early detection (MCED) trial data. Methods: We utilised simulated longitudinal MCED screening datasets to evaluate the inferential bounds of MSMs under increasing clinical disaggregation: a 3-state (overall MST), 5-state (early/late stage), and 9-state (stages I-IV) model. Bayesian estimation was performed via Markov chain Monte Carlo. Robustness was assessed through chain convergence, parameter identifiability (via profile likelihood), and precision of estimates. We also explored hierarchical models and the use of informative priors to improve identifiability. Results: Based only on MCED trial data, many cancer types exhibited inferential challenges. Generally, the 5-state model was as robust as the 3-state model, showing slight improvements to convergence and identifiability while maintaining precision for overall MST. In contrast, the 9-state model showed worsened convergence and identifiability, and a significant reduction in the precision of overall MST estimates. Hierarchical models successfully improved performance, as have informative prior models but the latter introduced bias towards the prior values. Conclusions: While disaggregating natural history models by individual cancer stages is desirable for policy, these higher-dimensional models show a greater reliance on external data/assumptions. We recommend explicit identifiability assessments and assessments of the influence of external data/assumptions to support inference for MCED screening evaluations.

stat.ME↗

arXiv2025

Modelling the impact of Multi Cancer Early Detection tests: a review of natural history of disease models

Introduction: The potential for multi-cancer early detection (MCED) tests to detect cancer at earlier stages is currently being evaluated in screening clinical trials. Once trial evidence becomes available, modelling will be necessary to predict impacts on final outcomes (benefits and harms), account for heterogeneity in determining clinical and cost-effectiveness, and explore alternative screening programme specifications. The natural history of disease (NHD) component of a MCED model will use statistical, mathematical or calibration methods. Methods: Modelling approaches for MCED screening that include an NHD component were identified from the literature, reviewed and critically appraised. Purposively selected (non-MCED) cancer screening models were also reviewed. The appraisal focussed on the scope, data sources, evaluation approaches and the structure and parameterisation of the models. Results: Five different MCED NHD models were identified and reviewed, alongside four additional (non-MCED) models. The critical appraisal highlighted several features of this literature. In the absence of trial evidence, MCED effects are based on predictions derived from test accuracy. These predictions rely on simplifying assumptions with unknown impacts, such as the stage-shift assumption used to estimate mortality impacts from predicted stage-shifts. None of the MCED models fully characterised uncertainty in the NHD or examined uncertainty in the stage-shift assumption. Conclusion: MCED technologies are developing rapidly, and large and costly clinical studies are being designed and implemented across the globe. Currently there is no modelling approach that can integrate clinical study evidence and therefore, in support of policy, it is important that similar efforts are made in the development of MCED models that make best use of the available data on benefits and harms.

stat.ME↗