Evidence map›Paper›PMID 40415442›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2025

[A comparative study of different methods for treatment switching analysis in clinical trials].

Zhiyue Liang, Lishan Xu, Keke Li, Milai Yu, Shengli An

Abstract readComparative StudyEnglish Abstract
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2025. 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
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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

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

5 authors.

Zhiyue LiangDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Lishan XuDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Keke LiDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Milai YuDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.
Shengli AnDepartment of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo compare the commonly used methods for analyzing treatment switching in clinical trials to facilitate selection of optimal methods in different scenarios.

methodsBased on the data characteristics of patient conversion in oncology clinical trials, we simulated the survival time of patients across different scenarios and compared the bias, mean square error and coverages of the treatment effects derived from different methods.

resultsThe sample size had an almost negligible impact on the outcomes of the various methods. Compared to conventional methods, more complex methods (RPSFTM, IPCW, TSE, and IPE) resulted in lower errors across different scenarios. The IPCW method could cause a significant increase in errors in cases where the probability of conversion was high. The TSE method had the lowest error and mean squared error when the risk was low and the probability of conversion was high. The IPE method had an obvious advantage in the scenario with a low probability of conversion, but it may slightly underestimate the treatment effect when the inflation factor was small.

conclusionsThe choice of a specific method for analyzing cohort transition should be made based on considerations of both the probability of conversion and inflation factor in different scenarios.

Indexed as

Clinical Trials as TopicNeoplasmsHumansinverse probability censoring weightingiterative parameter estimation methodrank preserving structural failure time modelstreatment switchingtwo-stage estimation method

Identifiers

PMID40415442
PMCPMC12104727

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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.