Evidence map›Paper›PMID 39516724›Full record

ArticleBMC medical research methodology2024

Determining doses for backfill cohorts based on patient-reported outcome.

Xin Chen, Jingyi Zhang, Bosheng Li, Fangrong Yan

Abstract read
In one paragraph

Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Xin ChenDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China.
Jingyi ZhangDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China.
Bosheng LiDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China.
Fangrong YanDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China. f.r.yan@163.com.

Funding

National Natural Science Foundation of China 82273735
6 · The paper itself

Abstract

backgroundIncorporating backfill cohorts in phase I oncology trials is a recently developed strategy for dose optimization. However, the efficacy assessment window is long in general, causing a lag in identifying ineffective doses and more patients being backfilled to those doses. There is necessity to investigate how to use patient-reported outcomes (PRO) to determine doses for backfill cohorts.

methodsWe propose a unified Bayesian design framework, called 'Backfill-QoL', to utilize patient-reported quality of life (QoL) data into phase I oncology trials with backfill cohorts, including methods for trial monitoring, algorithm for dose-finding, and criteria for dose selection. Simulation studies and sensitivity analyses are conducted to evaluate the proposed Backfill-QoL design.

resultsThe simulation studies demonstrate that the Backfill-QoL design is more efficient than traditional dose-expansion strategy, and fewer patients would be allocated to doses with unacceptable QoL profiles. A user-friendly Windows desktop application is developed and freely available for implementing the proposed design.

conclusionsThe Backfill-QoL design enables continuous monitoring of safety, efficacy and QoL outcomes, and the recommended phase II dose (RP2D) can be identified in a more patient-centered perspective.

Indexed as

AlgorithmsBayes TheoremClinical Trials, Phase I as TopicPatient Reported Outcome MeasuresQuality of LifeAntineoplastic AgentsCohort StudiesComputer SimulationDose-Response Relationship, DrugHumansNeoplasmsResearch DesignAntineoplastic AgentsBackfill cohortsDose optimizationPhase I clinical trialsQuality of life

Identifiers

PMID39516724
PMCPMC11546322

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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.