Evidence map›Paper›PMID 37324576›Full record

ArticleStatistical science : a review journal of the Institute of Mathematical Statistics2023

Response-adaptive randomization in clinical trials: from myths to practical considerations.

David S Robertson, Kim May Lee, Boryana C López-Kolkovska, Sofía S Villar

Abstract read
In one paragraph

Article in Statistical science : a review journal of the Institute of Mathematical Statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing 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

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

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Solving the 'Goldilocks problem' in dementia clinical trials with multimodal AI.The journal of prevention of Alzheimer's disease · 2025
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  14. Review
  15. Randomization-Based Inference for MCP-Mod.Statistics in medicine · 2025
    Article
  16. INCEPT: The Intensive Care Platform Trial-Design and protocol.Acta anaesthesiologica Scandinavica · 2025
    Article
  17. Article
  18. Article
  19. Review
  20. 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

4 authors.

David S RobertsonMRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom.
Kim May LeeMRC Biostatistics Unit.
Boryana C López-KolkovskaMRC Biostatistics Unit and is currently working at AstraZeneca.
Sofía S VillarMRC Biostatistics Unit.

Funding

Medical Research Council MR/N028171/1
6 · The paper itself

Abstract

Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.

Indexed as

ethicspatient allocationpowersample size imbalancetime trendstype I error control

Identifiers

PMID37324576
PMCPMC7614644

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.