Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in real world trials raises important practical questions, often neglected in the technical literature. Motivated by an innovative phase-II stratified RAR rare-disease trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are desirable, that is, both acceptable and faithful to the intended probabilities, particularly in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? To answer (1), we propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we answer (2) by analysing the impact of missing data on operating characteristics in selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.

Implementing response-adaptive randomisation in stratified rare-disease trials: Design challenges and practical solutions / Das, Rajenki; Deliu, Nina; Toshner, Mark R; Villar, Sofía S. - In: STATISTICAL METHODS IN MEDICAL RESEARCH. - ISSN 0962-2802. - (2025). [10.1177/09622802251380625]

Implementing response-adaptive randomisation in stratified rare-disease trials: Design challenges and practical solutions

Deliu, Nina
Co-primo
Methodology
;
2025

Abstract

Although response-adaptive randomisation (RAR) has gained substantial attention in the literature, it still has limited use in clinical trials. Amongst other reasons, the implementation of RAR in real world trials raises important practical questions, often neglected in the technical literature. Motivated by an innovative phase-II stratified RAR rare-disease trial, this paper addresses two challenges: (1) How to ensure that RAR allocations are desirable, that is, both acceptable and faithful to the intended probabilities, particularly in small samples? and (2) What adaptations to trigger after interim analyses in the presence of missing data? To answer (1), we propose a Mapping strategy that discretises the randomisation probabilities into a vector of allocation ratios, resulting in improved frequentist errors. Under the implementation of Mapping, we answer (2) by analysing the impact of missing data on operating characteristics in selected scenarios. Finally, we discuss additional concerns including: pooling data across trial strata, analysing the level of blinding in the trial, and reporting safety results.
2025
Adaptive designs; adaptive randomisation; implementation; mapping; rare disease
01 Pubblicazione su rivista::01a Articolo in rivista
Implementing response-adaptive randomisation in stratified rare-disease trials: Design challenges and practical solutions / Das, Rajenki; Deliu, Nina; Toshner, Mark R; Villar, Sofía S. - In: STATISTICAL METHODS IN MEDICAL RESEARCH. - ISSN 0962-2802. - (2025). [10.1177/09622802251380625]
File allegati a questo prodotto
File Dimensione Formato  
ISeliu_mplementing-response-adaptive_2025.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 1.61 MB
Formato Adobe PDF
1.61 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1750709
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact