Evidence map›Paper›PMID 24176050›Full record

ArticleBMC health services research2013

Genomic testing to determine drug response: measuring preferences of the public and patients using Discrete Choice Experiment (DCE).

Mehdi Najafzadeh, Karissa M Johnston, Stuart J Peacock, Joseph M Connors, Marco A Marra, Larry D Lynd, Carlo A Marra

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 5 pooled it
3.5field-weighted citation impact, top 6% of its field
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

19 citing papers in PubMed, 5 syntheses or guidelines pooled it, 31 citations in OpenAlex.

  1. Pooled it
  2. Willingness-to-pay for cancer treatment and outcome: a systematic review.The European journal of health economics : HEPAC : health economics in prevention and care · 2022
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  6. Do preferences differ based on respondent experience of a health issue and its treatment? A case study using a public health intervention.The European journal of health economics : HEPAC : health economics in prevention and care · 2023
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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

7 authors at 5 institutions in 2 countries.

Mehdi Najafzadeh
Karissa M Johnston
Stuart J Peacock
Joseph M Connors
Marco A Marra
Larry D Lynd
Carlo A MarraFaculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada. carlo.marra@ubc.ca.
University of British Columbia · CABC Cancer Agency · CAGenome British Columbia · CAHarvard University · USSt. Paul's Hospital · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe extent to which a genomic test will be used in practice is affected by factors such as ability of the test to correctly predict response to treatment (i.e. sensitivity and specificity of the test), invasiveness of the testing procedure, test cost, and the probability and severity of side effects associated with treatment.

methodsUsing discrete choice experimentation (DCE), we elicited preferences of the public (Sample 1, N = 533 and Sample 2, N = 525) and cancer patients (Sample 3, N = 38) for different attributes of a hypothetical genomic test for guiding cancer treatment. Samples 1 and 3 considered the test/treatment in the context of an aggressive curable cancer (scenario A) while the scenario for sample 2 was based on a non-aggressive incurable cancer (scenario B).

resultsIn aggressive curable cancer (scenario A), everything else being equal, the odds ratio (OR) of choosing a test with 95% sensitivity was 1.41 (versus a test with 50% sensitivity) and willingness to pay (WTP) was $1331, on average, for this amount of improvement in test sensitivity. In this scenario, the OR of choosing a test with 95% specificity was 1.24 times that of a test with 50% specificity (WTP = $827). In non-aggressive incurable cancer (scenario B), the OR of choosing a test with 95% sensitivity was 1.65 (WTP = $1344), and the OR of choosing a test with 95% specificity was 1.50 (WTP = $1080). Reducing severity of treatment side effects from severe to mild was associated with large ORs in both scenarios (OR = 2.10 and 2.24 in scenario A and B, respectively). In contrast, patients had a very large preference for 95% sensitivity of the test (OR = 5.23).

conclusionThe type and prognosis of cancer affected preferences for genomically-guided treatment. In aggressive curable cancer, individuals emphasized more on the sensitivity rather than the specificity of the test. In contrast, for a non-aggressive incurable cancer, individuals put similar emphasis on sensitivity and specificity of the test. While the public expressed strong preference toward lowering severity of side effects, improving sensitivity of the test had by far the largest influence on patients' decision to use genomic testing.

Indexed as

Genetic TestingAntineoplastic AgentsChoice BehaviorConsumer BehaviorFemaleHumansMaleMiddle AgedNeoplasmsPatient PreferencePharmacogeneticsPrognosisSensitivity and SpecificitySurveys and QuestionnairesTreatment OutcomeAntineoplastic Agents

Identifiers

PMID24176050
PMCPMC3827922
OpenAlexW2138335482

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.