Evidence map›Paper›PMID 41851043›Full record

ArticleStatistics in medicine2026

Sample Size Recalculation in Adaptive Group Sequential Study Designs for Comparing Restricted Mean Survival Times.

Carolin Herrmann, Paul Blanche

Abstract read
In one paragraph

Article in Statistics in medicine, 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

2 authors.

Carolin HerrmannMathematical Institute, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.ORCID https://orcid.org/0000-0003-2384-7303
Paul BlancheSection of Biostatistics, University of Copenhagen, Copenhagen K, Denmark.ORCID https://orcid.org/0000-0003-1415-7976

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-proportional hazards cases are frequently expected in clinical trials with time-to-event endpoints (e.g., cardiology, oncology). The relevance of hazard ratios to quantify the treatment effect is questionable and potentially misleading in this context. Hence, alternative methods comparing restricted mean survival times are increasingly promoted. Specific challenges arise when planning clinical trials for comparing restricted mean survival times, as several nuisance parameter estimates are needed for calculating the sample size. Precise estimates might be difficult to obtain at the planning stage and might lead to underpowered trials. One way of dealing with this insecurity is to apply adaptive group sequential study designs with the option to adapt the sample size during an ongoing trial. Within this work, we consider such sample size adaptations, with a specific focus on the context of delayed treatment effects. We compare the performance of an adaptive design with the restricted mean survival time as the primary endpoint with other commonly chosen endpoints in this scenario by means of an extensive simulation study. With our proposed method, adaptive designs with the restricted mean survival time as the primary endpoint are now thoroughly explained. The combination test that we describe can also be useful for other adaptations than sample sizes.

Indexed as

Clinical Trials as TopicResearch DesignComputer SimulationHumansProportional Hazards ModelsSample SizeSurvival Analysisadaptive clinical trialdelayed treatment effectnon‐proportional hazardsrestricted mean survival timesample size adaptationtime‐to‐event analysis

Identifiers

PMID41851043
PMCPMC12999550

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Registered trials

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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.