Evidence map›Paper›PMID 36735855›Full record

ArticleJournal of biopharmaceutical statistics2023

Response adaptive randomization design for a two-stage study with binary response.

Guogen Shan

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of biopharmaceutical statistics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
1.0field-weighted citation impact, top 25% of its field
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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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

1 author at 1 institution in 1 country.

Guogen ShanDepartment of Biostatistics, University of Florida, Gainesville, FL, USA.ORCID 0000-0001-8690-6599
University of Florida · US

Funding

Alzheimer's Disease: New Trial Designs for Emerging ChallengesR01AG070849 · NIA · UNIVERSITY OF NEVADA LAS VEGAS · PI SHAN, GUOGEN · 2021 to 2025
$1.5M
Adaptive randomized designs for cancer clinical trials by using integer algorithms and exact Monte Carlo methodsR03CA248006 · NCI · UNIVERSITY OF FLORIDA · PI SHAN, GUOGEN · 2021 to 2022
$153k
NCI NIH HHS R03 CA248006NIA NIH HHS R01 AG070849
6 · The paper itself

Abstract

Response adaptive randomization has the potential to treat more participants in better treatments in a trial to benefit participants. We propose optimal response adaptive randomization designs for a two-stage study with binary response, having the smallest expected sample size or the fewest expected number of failures. Equal randomization is used in the first stage, and data from the first stage is used to determine the adaptive sample size ratio in the second stage. In the proposed optimal designs, the type I error rate and the statistical power are calculated from the asymptotic normal distributions. The new designs that minimize the expected number of failures have the advantage over the existing optimal randomized designs to substantially reduce the number of failures.

Indexed as

Research DesignHumansRandom AllocationSample SizeBinary responseOptimal two-stage designParallel designResponse adaptive randomizationSample size

Identifiers

PMID36735855
PMCPMC10397367
OpenAlexW4319068454

What OpenQuestion holds

Textmetadata
LicenceTDM
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.