Evidence map›Paper›PMID 40483399›Full record

ArticleBMC medical research methodology2025

Power and sample size calculation for non-inferiority trials with treatment switching in intention-to-treat analysis comparing RMSTs.

Austin Shih, Chih-Yuan Hsu, Yu Shyr

Abstract read
In one paragraph

Article in BMC medical research methodology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. [A comparative study of different methods for treatment switching analysis in clinical trials].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Austin Shih *Department of Mathematics, Vanderbilt University, Nashville, TN, 37240, USA.
Chih-Yuan Hsu *Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. chih-yuan.hsu@vumc.org.
Yu ShyrDepartment of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, 37203, USA. yu.shyr@vumc.org.

Funding

Tumor Immunology and Microenvironment Research ProgramP30CA068485 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Ben Ho Park · 1995 to 2026
$172.8M
TrainingU54CA163072 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Karen Marie Winkfield · 2011 to 2026
$20.1M
Molecular, Cellular and Tissue Characterization UnitU2CCA233291 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI COFFEY, ROBERT J., SHRUBSOLE, MARTHA J. · 2018 to 2023
$12.2M
Vulnerability of SARS- CoV-2 Infection in Lung Cancer Based on Serological Antibody AnalysesU54CA260560 · NCI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI SHYR, YU · 2020 to 2024
$10.0M
Clinical Utility of Biomarkers Driven Management of Indeterminate Pulmonary NodulesR01CA252964 · NCI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Eric L Grogan, Alexander Mark Kaizer · 2021 to 2026
$3.3M
NCI NIH HHS P30 CA068485NCI NIH HHS R01 CA252964NCI NIH HHS U2C CA233291NCI NIH HHS U54 CA163072NCI NIH HHS U54 CA260560NIH HHS P30 CA068485, U2C CA233291, R01 CA252964, and U54 CA260560, CA163072
6 · The paper itself

Abstract

backgroundDifference in Restricted Mean Survival Time (DRMST) has attracted attention and is increasingly used in non-inferiority (NI) trials because of its superior power in detecting treatment effects compared to hazard ratio. However, when treatment switching (also known as crossover) occurs, the widely used intention-to-treat (ITT) analysis can underpower or overpower NI trials.

methodsWe propose a simulation-based approach, named nifts, to calculate powers and determine the necessary sample size to achieve a desired power for non-inferiority trials that allow treatment switching, in ITT analysis using DRMST.

resultsThe nifts approach offers three options for a non-inferiority margin, assumes three entry patterns and generalized gamma distributions for event time, incorporates two distributions for dropout censoring, and provides five distribution options for switching. Real-world and simulated examples are used to illustrate the proposed method and examine how switching probability, switching time, the relative effectiveness of treatments, allocation ratio, entry patterns, and event time distribution influence powers and sample sizes. nifts adjusts the non-inferiority margins intended for NI trials without treatment switching to accommodate the presence of treatment switching in the designs. With the adjusted margins, the type I errors are well-controlled. The ratios of sample sizes with treatment switching to those without switching are close to 1, indicating no significant change in power at sample sizes without switching when using adjusted margins. The performance on power and sample sizes is not sensitive to the choice of switch time distributions.

conclusionsThis simulation-based approach provides power and sample size calculation in NI trials with treatment switching, when comparing the RMSTs of two treatment groups in ITT analysis. With its comprehensive parameter settings, nifts will be useful for designing NI trials that allow for treatment switching. nifts is freely available at https://github.com/cyhsuTN/nifts .

Indexed as

Equivalence Trials as TopicIntention to Treat AnalysisAlgorithmsComputer SimulationHumansModels, StatisticalResearch DesignSample SizeSurvival AnalysisCrossoverIntention-to-treat analysisNon-inferiority trialsRestricted mean survival time

Identifiers

PMID40483399
PMCPMC12144734

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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.