Evidence map›Paper›PMID 39394852›Full record

ArticleClinical trials (London, England)2025

Using non-inferiority test of proportions in design of randomized non-inferiority trials with time-to-event endpoint with a focus on low-event-rate setting.

Lingyun Ji, Todd A Alonzo

Abstract read
In one paragraph

Article in Clinical trials (London, England), 2025. 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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0citing papers 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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Lingyun JiDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.ORCID 0000-0003-2500-1993
Todd A AlonzoDepartment of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Funding

NCTN BIQSFP ANBL1531 (NRT)U10CA180886 · NCI · PUBLIC HEALTH INSTITUTE · PI Douglas S. Hawkins · 2014 to 2026
$390.6M
COG SDMC - Statistics CoreU10CA180899 · NCI · UNIVERSITY OF SOUTHERN CALIFORNIA · PI TODD A ALONZO · 2014 to 2026
$132.8M
NCI NIH HHS U10 CA180886NCI NIH HHS U10 CA180899
6 · The paper itself

Abstract

Background/aimsFor cancers with low incidence, low event rates, and a time-to-event endpoint, a randomized non-inferiority trial designed based on the logrank test can require a large sample size with significantly prolonged enrollment duration, making such a non-inferiority trial not feasible. This article evaluates a design based on a non-inferiority test of proportions, compares its required sample size to the non-inferiority logrank test, assesses whether there are scenarios for which a non-inferiority test of proportions can be more efficient, and provides guidelines in usage of a non-inferiority test of proportions.MethodsThis article describes the sample size calculation for a randomized non-inferiority trial based on a non-inferiority logrank test or a non-inferiority test of proportions. The sample size required by the two design methods are compared for a wide range of scenarios, varying the underlying Weibull survival functions, the non-inferiority margin, and loss to follow-up rate.ResultsOur results showed that there are scenarios for which the non-inferiority test of proportions can have significantly reduced sample size. Specifically, the non-inferiority test of proportions can be considered for cancers with more than 80% long-term survival rate. We provide guidance in choice of this design approach based on parameters of the Weibull survival functions, the non-inferiority margin, and loss to follow-up rate.ConclusionFor cancers with low incidence and low event rates, a non-inferiority trial based on the logrank test is not feasible due to its large required sample size and prolonged enrollment duration. The use of a non-inferiority test of proportions can make a randomized non-inferiority Phase III trial feasible.

Indexed as

Endpoint DeterminationEquivalence Trials as TopicNeoplasmsRandomized Controlled Trials as TopicResearch DesignHumansSample SizeTime Factorslogrank testnon-inferiority designPhase IIIsample sizetest of proportionstime-to-event endpoint

Identifiers

PMID39394852
PMCPMC11991896

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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.