ReviewBMC medical research methodology2022
Which test for crossing survival curves? A user's guideline.
Review in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Methods for non-proportional hazards in clinical trials: A systematic review.Statistical methods in medical research · 2024Pooled it
- Strategies to address non-proportional hazards between survival curves - Lessons from phase III trials in hepatocellular carcinoma.Journal of hepatology · 2026Article
- Beyond Bonferroni: new multiple contrast tests for time-to-event data under non-proportional hazards.Lifetime data analysis · 2026Article
- MOGAN for LUAD Subtype Classification by Integrating Three Omics Data Types.Cancer innovation · 2025Article
- Article
- Introducing the Event-Adjusted Rank Sum (EARS) Test: A Simple Approach to Survival Analysis Independent of Proportional Hazards.BioMed research international · 2025Article
- Statistics Primer for Radiologists: Part 2-Advanced statistics for Enhancing Diagnostic Precision and Research Validity.The Indian journal of radiology & imaging · 2025Article
- Multicenter, Real-World Clinical Evaluation of Alemtuzumab and Anti-Thymocyte Globulin for Severe Acute T Cell-Mediated Kidney Transplant Rejection.Clinical transplantation · 2024Article
- Risk Factors for Developing Metachronous Superficial Gastric Epithelial Neoplasms after Endoscopic Submucosal Dissection.Journal of clinical medicine · 2024Article
- The Inverse Log-Rank Test: A Versatile Procedure for Late Separating Survival Curves.International journal of environmental research and public health · 2023Article
- Carfilzomib, lenalidomide and dexamethasone followed by a second ASCT is an effective strategy in first relapse multiple myeloma: a study on behalf of the Chronic malignancies working party of the EBMT.Bone marrow transplantation · 2023Article
- Investigating non-inferiority or equivalence in time-to-event data under non-proportional hazards.Lifetime data analysis · 2023Article
- Identification and development of TP53 mutation-associated Long non-coding RNAs signature for optimized prognosis assessment and treatment selection in hepatocellular carcinoma.International journal of immunopathology and pharmacologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe exchange of knowledge between statisticians developing new methodology and clinicians, reviewers or authors applying them is fundamental. This is specifically true for clinical trials with time-to-event endpoints. Thereby, one of the most commonly arising questions is that of equal survival distributions in two-armed trial. The log-rank test is still the gold-standard to infer this question. However, in case of non-proportional hazards, its power can become poor and multiple extensions have been developed to overcome this issue. We aim to facilitate the choice of a test for the detection of survival differences in the case of crossing hazards.
methodsWe restricted the review to the most recent two-armed clinical oncology trials with crossing survival curves. Each data set was reconstructed using a state-of-the-art reconstruction algorithm. To ensure reproduction quality, only publications with published number at risk at multiple time points, sufficient printing quality and a non-informative censoring pattern were included. This article depicts the p-values of the log-rank and Peto-Peto test as references and compares them with nine different tests developed for detection of survival differences in the presence of non-proportional or crossing hazards.
resultsWe reviewed 1400 recent phase III clinical oncology trials and selected fifteen studies that met our eligibility criteria for data reconstruction. After including further three individual patient data sets, for nine out of eighteen studies significant differences in survival were found using the investigated tests. An important point that reviewers should pay attention to is that 28% of the studies with published survival curves did not report the number at risk. This makes reconstruction and plausibility checks almost impossible.
conclusionsThe evaluation shows that inference methods constructed to detect differences in survival in presence of non-proportional hazards are beneficial and help to provide guidance in choosing a sensible alternative to the standard log-rank test.
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Registered trials
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.