Evidence map›Paper›PMID 35768553›Full record

ArticleHandbook of experimental pharmacology2023

Economics and Precision Medicine.

Katherine Payne, Sean P Gavan

Abstract read
PubMed Publisher
In one paragraph

Article in Handbook of experimental pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Katherine PayneManchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. Katherine.Payne@manchester.ac.uk.
Sean P GavanManchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introducing precision medicine strategies into routine practice will require robust economic evidence. Decision-makers need to understand the value of a precision medicine strategy compared with alternative ways to treat patients. This chapter describes health economic analysis techniques that are needed to generate this evidence. The value of any precision medicine strategy can be demonstrated early to inform evidence generation and improve the likelihood of translation into routine practice. Advances in health economic analysis techniques are also explained and their relevance to precision medicine is highlighted. Ensuring that constraints on delivery are resolved to increase uptake and implementation will improve the value of a new precision medicine strategy. Empirical methods to quantify stakeholders' preferences can be effective to inform the design of a precision medicine intervention or service delivery model. A range of techniques to generate relevant economic evidence are now available to support the development and translation of precision medicine into routine practice. This economic evidence is essential to inform resource allocation decisions and will enable patients to benefit from cost-effective precision medicine strategies in the future.

Indexed as

Precision MedicineCost-Benefit AnalysisHumansBudget impact analysisCost-effectiveness analysisDecision-analytic modelDiscrete choice experimentEconomic evaluationMicrocostingPharmacoeconomicsStated preferencesValue of implementationValue of information

Identifiers

PMID35768553

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 OpenQuestion graph.