ArticlePharmaceutical statistics2021
Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.
Article in Pharmaceutical statistics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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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.
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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.
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Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Evolution of clinical benefit in cancer trials: comparing pivotal approval data with updated efficacy.Frontiers in public health · 2026Pooled it
- Methods for non-proportional hazards in clinical trials: A systematic review.Statistical methods in medical research · 2024Pooled it
- Avelumab first-line maintenance in advanced urothelial carcinoma: Complete screening for prognostic and predictive factors using machine learning in the JAVELIN Bladder 100 phase 3 trial.Cancer medicine · 2024Trial
- Radiotherapy and paclitaxel plus pazopanib or placebo in anaplastic thyroid cancer (NRG/RTOG 0912): a randomised, double-blind, placebo-controlled, multicentre, phase 2 trial.The Lancet. Oncology · 2023Trial
- Clinical Impact of NOTCH3 Variant Location After First Stroke in CADASIL.Annals of clinical and translational neurology · 2026Article
- A multistate transition model for survival estimation in randomized trials with treatment switching and a cured subgroup.BMC medical research methodology · 2025Article
- The Impact of Design Misspecifications on Survival Outcomes in Cancer Clinical Trials.Cancers · 2025Article
- One-step parametric network meta-analysis models using the exact likelihood that allow for time-varying treatment effects.Research synthesis methods · 2025Article
- A Comparison of Statistical Methods for Time-To-Event Analyses in Randomized Controlled Trials Under Non-Proportional Hazards.Statistics in medicine · 2025Article
- Oncology Clinical Trial Design Planning Based on a Multistate Model That Jointly Models Progression-Free and Overall Survival Endpoints.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Comparison between asymptotic and re-randomisation tests under non-proportional hazards in a randomised controlled trial using the minimisation method.BMC medical research methodology · 2024Article
- Simultaneous inference procedures for the comparison of multiple characteristics of two survival functions.Statistical methods in medical research · 2024Article
- Futility monitoring for randomized clinical trials with non-proportional hazards: An optimal conditional power approach.Clinical trials (London, England) · 2023Article
- Examining evidence of time-dependent treatment effects: an illustration using regression methods.Trials · 2022Article
- Delayed treatment effects, treatment switching and heterogeneous patient populations: How to design and analyze RCTs in oncology.Pharmaceutical statistics · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the analysis of survival times, the logrank test and the Cox model have been established as key tools, which do not require specific distributional assumptions. Under the assumption of proportional hazards, they are efficient and their results can be interpreted unambiguously. However, delayed treatment effects, disease progression, treatment switchers or the presence of subgroups with differential treatment effects may challenge the assumption of proportional hazards. In practice, weighted logrank tests emphasizing either early, intermediate or late event times via an appropriate weighting function may be used to accommodate for an expected pattern of non-proportionality. We model these sources of non-proportional hazards via a mixture of survival functions with piecewise constant hazard. The model is then applied to study the power of unweighted and weighted log-rank tests, as well as maximum tests allowing different time dependent weights. Simulation results suggest a robust performance of maximum tests across different scenarios, with little loss in power compared to the most powerful among the considered weighting schemes and huge power gain compared to unfavorable weights. The actual sources of non-proportional hazards are not obvious from resulting populationwise survival functions, highlighting the importance of detailed simulations in the planning phase of a trial when assuming non-proportional hazards.We provide the required tools in a software package, allowing to model data generating processes under complex non-proportional hazard scenarios, to simulate data from these models and to perform the weighted logrank tests.
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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.