Evidence map›Paper›PMID 33927801›Full record

ArticleStatistics in biopharmaceutical research2021

Statistical Considerations for Sequential Analysis of the Restricted Mean Survival Time for Randomized Clinical Trials.

Ying Lu, Lu Tian

Abstract read
In one paragraph

Article in Statistics in biopharmaceutical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

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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.

Ying LuDepartment of Biomedical Data Science and Center for Innovative Study Design, Stanford University, Stanford, CA 94305-5464, USA.
Lu TianDepartment of Biomedical Data Science and Center for Innovative Study Design, Stanford University, Stanford, CA 94305-5464, USA.

Funding

Translational Oncology Research Program (Project-005)P30CA124435 · NCI · STANFORD UNIVERSITY · PI Melinda L. Telli · 2007 to 2026
$71.4M
Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Statistical Methods for Optimizing Personalized Treatment SelectionR01HL089778 · NHLBI · STANFORD UNIVERSITY · PI LU TIAN · 2008 to 2026
$4.8M
NCATS NIH HHS UL1 TR003142NCI NIH HHS P30 CA124435NHLBI NIH HHS R01 HL089778
6 · The paper itself

Abstract

In this paper, we illustrate the method of designing a group-sequential randomized clinical trial based on the difference in restricted mean survival time (RMST). The procedure is based on theoretical formulations of Murray and Tsiatis (1999). We also present a numerical example in designing a cardiology surgical trial. Various practical considerations are discussed. R codes are provided in the Supplementary Materials. We conclude that the group-sequential design for RMST is a viable option in practice. A simulation study is performed to compare the proposed method to the Max-Combo and conventional log-rank tests. The simulation result shows that when there is a delayed treatment benefit and the proportional hazards assumption is untrue, the sequential design based on the RMST can be more efficient than that based on the log-rank test but less efficient than that based on the Max-Combo test. Compared with Max-Combo test, the RMST-based study design yield coherent estimand, statistical inference and result interpretation.

Indexed as

Group Sequential DesignInterim AnalysisRestricted Mean Survival Time (RMST)Sample Size

Identifiers

PMID33927801
PMCPMC8078843

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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.