Evidence map›Paper›PMID 42715903›Full record

ArticleClinics (Sao Paulo, Brazil)2026

Methodological considerations for analyzing enriched enrolment randomized withdrawal trials.

Philippe Lehert

Abstract read
In one paragraph

Article in Clinics (Sao Paulo, Brazil), 2026. 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
–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

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

1 author.

Philippe LehertSchool of Management, UC Louvain University, Belgium; Faculty of Medicine, The University of Melbourne, Australia. Electronic address: Philippe.lehert@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe Enriched Enrolment Randomized Withdrawal (EERW) design has been widely employed under various names across multiple disease areas. The methodology underlying these trials remains subject to ongoing discussion. A review of the literature identified key criticisms associated with the EERW design, notably the Open-Label Effect (OLE) resulting from the initial Open-Label (OL) phase and the Selection Effect (SLE) arising from selecting responders at the conclusion of the OL period. The aim of this study was to quantify these effects using historical data and to propose a straightforward approach to mitigate their impact.

methodsBased on proposals to estimate SLE and OLE effects, we conducted a retrospective analysis on a sample of 42 EERWs studies in various disorders and pathologies.

resultsThe OLE effect was confirmed with a highly significant additional increase of the main final endpoint (Standardized effect β

conclusionWhile EERW designs are widely utilized, the impact of the Open-Label (OL) phase on statistical outcomes remains under-addressed. This study quantifies two distinct and specific OLE and SLE effects increasing the residual variability and reducing the precision of the studied treatment estimate. By incorporating these effects as adjustments within the primary inferential model, researchers can mitigate extraneous noise and improve the precision and robustness of EERW trial results.

Indexed as

ANCOCA analysis of covarianceEnriched Enrolment randomized withdrawal (EERW)Randomized withdrawal Trial RWTStatistical power

Identifiers

PMID42715903
PMCPMC13573709

What OpenQuestion holds

Textmetadata
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.