Evidence map›Paper›PMID 42685018›Full record

ArticlePloS one2026

Does randomization matter in rare disease clinical trials.

Stephanie Wied, Denis Kirilov Razsolkov, Nicole Heussen, Ralf-Dieter Hilgers

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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.

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

4 authors.

Stephanie WiedInstitute of Medical Statistics, RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0000-0003-0616-6610
Denis Kirilov RazsolkovInstitute of Medical Statistics, RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0009-0003-4273-1100
Nicole HeussenInstitute of Medical Statistics, RWTH Aachen University, Aachen, Germany.
Ralf-Dieter HilgersInstitute of Medical Statistics, RWTH Aachen University, Aachen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundConducting Rare Disease Clinical Trials poses a wide variety of research challenges. Many techniques that are usually considered the best standard for evaluating new therapies are more difficult to implement here. This also holds in terms of randomization in clinical trials. The question arises how to randomize and, especially, which randomization procedure (RP) should be used to prevent the influence of bias focussing on allocation bias as efficiently as possible.

methodsWe present guidance that supports users in the selection and implementation of an RP based on the ERDO framework, which is a template for the evaluation of RPs for design optimization. For this purpose, we use the freely available R-package randomizeR, which is an easy-to-use tool to evaluate different RPs for various study designs while quantifying different types of bias. The evaluation is based on different metrics, such as the mean type-1 error probability or the go-no-go criterion, and brings these together with indirect evaluation criteria such as predictability and the number of correct guesses.

resultsFor the evaluation of RPs and the comparison of RPs with each other, both the presented indirect and direct criteria should be considered together. Examining the criteria individually might be misleading and may lead to different decisions regarding the RP. Different study designs may result in different assessments, so there is no universal solution or choice for RPs.

conclusionRandomization is and will continue to be a key methodology for rare disease clinical trials. Quantification of bias in particular with respect to the test decision is an essential issue, and the use of the ERDO framework should be a routine practice in the design of a trial.

Indexed as

Randomized Controlled Trials as TopicRare DiseasesBiasHumansRandom AllocationResearch Design

Identifiers

PMID42685018
PMCPMC13537556

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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.