Evidence map›Paper›PMID 32797380›Full record

ArticlePharmacoEconomics2020

Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers.

Risha Gidwani, Louise B Russell

Erratum issuedAbstract read
In one paragraph

Article in PharmacoEconomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 56 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed, 2 pooled it
–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

56 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  13. Reporting aged care quality data monthly, quarterly, or annually? A cross-sectional study of 349 613 residents and 2803 long-term care facilities.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Risha GidwaniDepartment of Health Management and Policy, UCLA School of Public Health, Los Angeles, CA, USA. rishag@rand.org.ORCID http://orcid.org/0000-0002-3548-2332
Louise B RussellDepartment of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.ORCID http://orcid.org/0000-0003-3599-0991

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This tutorial presents practical guidance on transforming various types of information published in journals, or available online from government and other sources, into transition probabilities for use in state-transition models, including cost-effectiveness models. Much, but not all, of the guidance has been previously published in peer-reviewed journals. Our purpose is to collect it in one location to serve as a stand-alone resource for decision modelers who draw most or all of their information from the published literature. Our focus is on the technical aspects of manipulating data to derive transition probabilities. We explain how to derive model transition probabilities from the following types of statistics: relative risks, odds, odds ratios, and rates. We then review the well-known approach for converting probabilities to match the model's cycle length when there are two health-state transitions and how to handle the case of three or more health-state transitions, for which the two-state approach is not appropriate. Other topics discussed include transition probabilities for population subgroups, issues to keep in mind when using data from different sources in the derivation process, and sensitivity analyses, including the use of sensitivity analysis to allocate analyst effort in refining transition probabilities and ways to handle sources of uncertainty that are not routinely formalized in models. The paper concludes with recommendations to help modelers make the best use of the published literature.

Indexed as

Decision MakingCost-Benefit AnalysisHumansProbabilityPublishing

Identifiers

PMID32797380
PMCPMC7426391

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.