Evidence map›Paper›PMID 41482706›Full record

ArticleClinical trials (London, England)2026

A note on rank-preserving structural failure time models to account for crossover.

Pedro A Torres-Saavedra, Boris Freidlin, Jong-Hyeon Jeong, Edward L Korn

Abstract read
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Article in Clinical trials (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Pedro A Torres-SaavedraBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0003-1021-6625
Boris FreidlinBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0002-2359-8063
Jong-Hyeon JeongBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
Edward L KornBiometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.ORCID 0000-0002-3298-437X

Funding

Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

backgroundIn randomized trials where some standard-treatment arm patients cross to the experimental treatment, it is frequently of interest to estimate the between-arm survival difference as if no patients on the standard-treatment arm had crossed over to the experimental treatment. Rank-preserving structural failure time models, an extension of semiparametric accelerated-failure-time models, are a popular method for accomplishing this because they do not require modeling which patients will crossover.

methodsIn trying to apply the rank-preserving structural failure time model in practice, we noted some unusual behavior of the estimated acceleration parameter (differential treatment effect). Simple examples and limited simulations are provided to examine and understand this behavior.

resultsThe simulations show that rank-preserving structural failure time model estimator of the acceleration parameter can take on extreme values, especially when the intent-to-treat analysis favors the standard-treatment arm. Furthermore, the addition of censoring is paradoxically shown to reduce the estimator's variability compared to the uncensored data when the underlying observations are exponentially distributed. Use of a Weibull distribution with short tails for the survival times eliminates this unusual behavior.

conclusionThe rank-preserving structural failure time model estimators of the acceleration parameter are not based on the joint ranks of the original data, and it is suggested that this makes acceleration-parameter estimator unstable with long-tailed survival distributions.

Indexed as

Models, StatisticalRandomized Controlled Trials as TopicComputer SimulationCross-Over StudiesHumansTime Factorsrank-preserving structural failure time modelTreatment crossovertreatment switching

Identifiers

PMID41482706
PMCPMC12853195

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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.