Evidence map›Paper›PMID 29110141›Full record

ArticlePharmacoEconomics2018

Oncology Modeling for Fun and Profit! Key Steps for Busy Analysts in Health Technology Assessment.

Jaclyn Beca, Don Husereau, Kelvin K W Chan, Neil Hawkins, Jeffrey S Hoch

Abstract read
PubMed Publisher
In one paragraph

Article in PharmacoEconomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Jaclyn BecaPharmacoeconomics Research Unit, Cancer Care Ontario, Toronto, ON, Canada.
Don HusereauInstitute of Health Economics, 1200, 10405 Jasper Avenue, Edmonton, AB, T5J 3N4, Canada. dhusereau@ihe.ca.ORCID 0000-0002-4416-6876
Kelvin K W ChanSunnybrook Odette Cancer Centre, University of Toronto, Toronto, ON, Canada.
Neil HawkinsThe University of Glasgow, Glasgow, Scotland, UK.
Jeffrey S HochThe University of California, Davis, Davis, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In evaluating new oncology medicines, two common modeling approaches are state transition (e.g., Markov and semi-Markov) and partitioned survival. Partitioned survival models have become more prominent in oncology health technology assessment processes in recent years. Our experience in conducting and evaluating models for economic evaluation has highlighted many important and practical pitfalls. As there is little guidance available on best practices for those who wish to conduct them, we provide guidance in the form of 'Key steps for busy analysts,' who may have very little time and require highly favorable results. Our guidance highlights the continued need for rigorous conduct and transparent reporting of economic evaluations regardless of the modeling approach taken, and the importance of modeling that better reflects reality, which includes better approaches to considering plausibility, estimating relative treatment effects, dealing with post-progression effects, and appropriate characterization of the uncertainty from modeling itself.

Indexed as

Models, EconomicAntineoplastic AgentsHumansMarkov ChainsNeoplasmsSurvival AnalysisTechnology Assessment, BiomedicalAntineoplastic Agents

Identifiers

What Socratic 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 Socratic graph.