Evidence mapPaperPMID 41298333Full record

ReviewInternational journal of technology assessment in health care2025

Approaches to modeling treatment sequencing in practice: a thematic review of prior NICE appraisals.

Abualbishr Alshreef, Fern Woodhouse, Molly Haycock, Hugh Osborne, Dave Harland, Stephen Palmer

Abstract readReview
In one paragraph

Review in International journal of technology assessment in health care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Abualbishr AlshreefAbbVie Inc, USA.ORCID https://orcid.org/0000-0003-2737-1365
Fern WoodhouseCostello Medical Consulting Ltd, UK.
Molly HaycockCostello Medical Consulting Ltd, UK.
Hugh OsborneCostello Medical Consulting Ltd, UK.
Dave HarlandAbbVie, New Zealand.
Stephen PalmerUniversity of York, UK.

Funding

AbbVie
6 · The paper itself

Abstract

backgroundAs the variety of specific treatments in a disease area increases, there may be a growing interest in employing treatment sequencing within health economic models. The aim of this review was to identify and thematically analyze patterns regarding the approaches to modeling treatment sequencing in National Institute for Health and Care Excellence (NICE) appraisals.

methodsA review of NICE technology appraisals (TAs) published between 1 January 2020 and 13 March 2023 was conducted.

resultsA total of twenty-four TAs incorporating treatment sequencing were included, most commonly in autoimmune and oncology indications. Primary justifications for companies employing treatment sequencing were precedence and alignment with clinical practice, whilst lack of appropriate clinical data was cited to justify its exclusion. Relatedly, External Assessment Groups commonly criticized treatment sequences for oversimplifying clinical practice. Notably, almost half of identified TAs assumed that the relative efficacy of an intervention was maintained regardless of disease severity or position within the treatment sequence.

conclusionA substantial proportion of TAs employed treatment sequencing, but it is challenging to determine the impact of current approaches on the overall uncertainty associated with any health economic model. The challenges identified in this review could be used to inform future formal guidance and associated methodology for the implementation of treatment sequencing modeling, which could improve the comparability and reliability of models and their results.

Indexed as

Models, EconomicTechnology Assessment, BiomedicalCost-Benefit AnalysisHumanscost-effectiveness modelsdecision makinghealth policyhealth technology assessmenttreatment sequencing

Identifiers

PMID41298333
PMCPMC12723307

What Socratic holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.