Evidence map›Paper›PMID 28295513›Full record

ArticleStatistics in medicine2017

Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase I cancer trials.

Graham M Wheeler, Michael J Sweeting, Adrian P Mander

Abstract read
In one paragraph

Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Combining cytotoxic agents with continuous dose levels in seamless phase I-II clinical trials.Journal of the Royal Statistical Society. Series C, Applied statistics · 2022
    Article
  5. Article
  6. Article
  7. Article
  8. Article
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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

3 authors.

Graham M WheelerCancer Research UK and UCL Cancer Trials Centre, University College London, U.K.ORCID http://orcid.org/0000-0002-3254-5991
Michael J SweetingCardiovascular Epidemiology Unit, Strangeways Research Laboratory University of Cambridge, U.K.ORCID http://orcid.org/0000-0003-0980-8965
Adrian P ManderMRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge, U.K.ORCID http://orcid.org/0000-0002-0742-9040

Funding

British Heart Foundation RG/08/014/24067British Heart Foundation RG/13/13/30194Cancer Research UK 15953European Research Council 268834Medical Research Council G0800270Medical Research Council MC_UP_1302/2Medical Research Council MC_UU_00002/3Medical Research Council MR/L003120/1
6 · The paper itself

Abstract

Phase I trials of anti-cancer therapies aim to identify a maximum tolerated dose (MTD), defined as the dose that causes unacceptable toxicity in a target proportion of patients. Both rule-based and model-based methods have been proposed for MTD recommendation. The escalation with overdose control (EWOC) approach is a model-based design where the dose assigned to the next patient is one that, given all available data, has a posterior probability of exceeding the MTD equal to a pre-specified value known as the feasibility bound. The aim is to conservatively dose-escalate and approach the MTD, avoiding severe overdosing early on in a trial. The EWOC approach has been applied in practice with the feasibility bound either fixed or varying throughout a trial, yet some of the methods may recommend incoherent dose-escalation, that is, an increase in dose after observing severe toxicity at the current dose. We present examples where varying feasibility bounds have been used in practice, and propose a toxicity-dependent feasibility bound approach that guarantees coherent dose-escalation and incorporates the desirable features of other EWOC approaches. We show via detailed simulation studies that the toxicity-dependent feasibility bound approach provides improved MTD recommendation properties to the original EWOC approach for both discrete and continuous doses across most dose-toxicity scenarios, with comparable performance to other approaches without recommending incoherent dose escalation. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Indexed as

Maximum Tolerated DoseAntineoplastic AgentsBayes TheoremBiostatisticsClinical Trials, Phase I as TopicComputer SimulationDrug OverdoseFeasibility StudiesHumansNeoplasmsAntineoplastic AgentsBayesian adaptive designsdose-escalationmaximum tolerated dosephase I trials

Identifiers

PMID28295513
PMCPMC5462100

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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.