Evidence map›Paper›PMID 31303865›Full record

ArticleCost effectiveness and resource allocation : C/E2019

A technique for approximating transition rates from published survival analyses.

Markian A Pahuta, Joel Werier, Eugene K Wai, Roy A Patchell, Doug Coyle

Abstract read
In one paragraph

Article in Cost effectiveness and resource allocation : C/E, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

5 authors.

Markian A Pahuta1Department of Orthopaedic Surgery, Henry Ford Health System, Detroit, MI USA.
Joel Werier2Division of Orthopaedic Surgery, The University of Ottawa, Ottawa, ON Canada.
Eugene K Wai2Division of Orthopaedic Surgery, The University of Ottawa, Ottawa, ON Canada.
Roy A Patchell4Departments of Neurology and Neurosurgery, University of Kentucky Medical Center, Lexington, KY USA.
Doug Coyle3School of Epidemiology and Public Health, The University of Ottawa, Ottawa, ON Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundQuality-adjusted-life-years (QALYs) are used to concurrently quantify morbidity and mortality within a single parameter. For this reason, QALYs can facilitate the discussion of risks and benefits during patient counseling regarding treatment options. QALYs are often calculated using partitioned-survival modelling. Alternatively, QALYs can be calculated using more flexible and informative state-transition models populated with transition rates estimated using multistate modelling (MSM) techniques. Unfortunately the latter approach is considered not possible when only progression-free survival (PFS) and overall survival (OS) analyses are reported.

methodsWe have developed a method that can be used to estimate

resultsThe approximation method is more accurate for estimating the transition rates out of health than the transition rate out of illness. The method tends to under-estimate true transition rates as censoring increases.

conclusionsIn this article we present the basis for and use of the transition rate approximation method. We then apply the method to a case study and evaluate the method in a simulation study.

Indexed as

CancerMultistate modelQuality adjusted life yearSurvival analysis

Identifiers

PMID31303865
PMCPMC6604134

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LicenceCC BY
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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 OpenQuestion graph.