Evidence map›Paper›PMID 36250912›Full record

ArticleStatistics in medicine2022

Dose finding studies for therapies with late-onset toxicities: A comparison study of designs.

Helen Barnett, Oliver Boix, Dimitris Kontos, Thomas Jaki

Abstract read
In one paragraph

Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Estimands for Early-Phase Dose Optimization Trials in Oncology.Biometrical journal. Biometrische Zeitschrift · 2025
    Article
  3. Article
  4. Article
  5. 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

4 authors.

Helen BarnettMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0001-7466-7033
Oliver BoixBayer AG, Leverkusen, Germany.
Dimitris KontosClinBAY, Limassol, Cyprus.
Thomas JakiMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.ORCID 0000-0002-1096-188X

Funding

Department of Health BRC-1215-20014Department of Health NIHR-SRF-2015-08-001Medical Research Council MC_UU_00002/14
6 · The paper itself

Abstract

An objective of phase I dose-finding trials is to find the maximum tolerated dose; the dose with a particular risk of toxicity. Frequently, this risk is assessed across the first cycle of therapy. However, in oncology, a course of treatment frequently consists of multiple cycles of therapy. In many cases, the overall risk of toxicity for a given treatment is not fully encapsulated by observations from the first cycle, and hence it is advantageous to include toxicity outcomes from later cycles in phase I trials. Extending the follow up period in a trial naturally extends the total length of the trial which is undesirable. We present a comparison of eight methods that incorporate late onset toxicities while not extensively extending the trial length. We conduct simulation studies over a number of scenarios and in two settings; the first setting with minimal stopping rules and the second setting with a full set of standard stopping rules expected in such a dose finding study. We find that the model-based approaches in general outperform the model-assisted approaches, with an interval censored approach and a modified version of the time-to-event continual reassessment method giving the most promising overall performance in terms of correct selections and trial length. Further recommendations are made for the implementation of such methods.

Indexed as

Research DesignBayes TheoremComputer SimulationDose-Response Relationship, DrugHumansLongitudinal StudiesMaximum Tolerated Dosedose-findinglate-onset toxicitiesmodel-assistedmodel-basedphase I trials

Identifiers

PMID36250912
PMCPMC10092569

What OpenQuestion holds

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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.