Evidence map›Paper›PMID 27803594›Full record

ArticleCurrent oncology (Toronto, Ont.)2016

A time-and-motion approach to micro-costing of high-throughput genomic assays.

S Costa, D A Regier, B Meissner, I Cromwell, S Ben-Neriah, E Chavez, S Hung, C Steidl, D W Scott, M A Marra and 2 more

Abstract read
In one paragraph

Article in Current oncology (Toronto, Ont.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Value-based genomics.Oncotarget · 2018
    Review
  9. Article
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

12 authors.

S CostaCanadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC.
D A RegierCanadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC; School of Population and Public Health, University of British Columbia, Vancouver, BC.
B MeissnerCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
I CromwellCanadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC.
S Ben-NeriahCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
E ChavezCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
S HungCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC.
C SteidlCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC.
D W ScottCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medicine, University of British Columbia, Vancouver, BC.
M A MarraCanada's Michael Smith Genome Sciences Centre, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medical Genetics, University of British Columbia, Vancouver, BC.
S J PeacockCanadian Centre for Applied Research in Cancer Control, Vancouver, BC; Department of Cancer Control Research, BC Cancer Agency, Vancouver, BC; Faculty of Health Sciences, Simon Fraser University, Burnaby, BC.
J M ConnorsCentre for Lymphoid Cancer, BC Cancer Agency, University of British Columbia, Vancouver, BC; Department of Medicine, University of British Columbia, Vancouver, BC.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenomic technologies are increasingly used to guide clinical decision-making in cancer control. Economic evidence about the cost-effectiveness of genomic technologies is limited, in part because of a lack of published comprehensive cost estimates. In the present micro-costing study, we used a time-and-motion approach to derive cost estimates for 3 genomic assays and processes-digital gene expression profiling (gep), fluorescence

methodsThe setting for the study was the Department of Lymphoid Cancer Research laboratory at the BC Cancer Agency in Vancouver, British Columbia. Mean per-case hands-on time and resource measurements were determined from a series of direct observations of each assay. Per-case cost estimates were calculated using a bottom-up costing approach, with labour, capital and equipment, supplies and reagents, and overhead costs included.

resultsThe most labour-intensive assay was found to be fish at 258.2 minutes per case, followed by targeted capture sequencing (124.1 minutes per case) and digital gep (14.9 minutes per case). Based on a historical case throughput of 180 cases annually, the mean per-case cost (2014 Canadian dollars) was estimated to be $1,029.16 for targeted capture sequencing and bioinformatics analysis, $596.60 for fish, and $898.35 for digital gep with an 807-gene code set.

conclusionsWith the growing emphasis on personalized approaches to cancer management, the need for economic evaluations of high-throughput genomic assays is increasing. Through economic modelling and budget-impact analyses, the cost estimates presented here can be used to inform priority-setting decisions about the implementation of such assays in clinical practice.

Indexed as

economic evaluationsgenomic technologymicro-costingPersonalized medicinetime-and-motion analyses

Identifiers

PMID27803594
PMCPMC5081006

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.