Response‐adaptive randomization for multiarm clinical trials using context‐dependent information measures

Abstract: Theoretical‐information approach applied to the clinical trial designs appeared to bring several advantages when tackling a problem of finding a balance between power and expected number of successes (ENS). In particular, it was shown that the built‐in parameter of the weight function allows finding the desired trade‐off between the statistical power and number of treated patients in the context of small population Phase II clinical trials. However, in real clinical trials, randomized designs are more preferable. The goal of this research is to introduce randomization to a deterministic entropy‐based sequential trial procedure generalized to multiarm setting. Several methods of randomization applied to an entropy‐based design are investigated in terms of statistical power and ENS. Namely, the four design types are considered: (a) deterministic procedures, (b) naive randomization using the inverse of entropy criteria as weights, (c) block randomization, and (d) randomized penalty parameter. The randomized entropy‐based designs are compared to randomized Gittins index (GI) and fixed randomization (FR). After the comprehensive simulation study, the following conclusion on block randomization is made: for both entropy‐based and GI‐based block randomization designs the degree of randomization induced by forward‐looking procedures is insufficient to achieve a decent statistical power. Therefore, we propose an adjustment for the forward‐looking procedure that improves power with almost no cost in terms of ENS. In addition, the properties of randomization procedures based on randomly drawn penalty parameter are also thoroughly investigated.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Response‐adaptive randomization for multiarm clinical trials using context‐dependent information measures ; day:10 ; month:10 ; year:2023 ; extent:19 ; Biometrical journal ; (10.10.2023) (gesamt 19)

Beteiligte, Orts- und Zeitangaben

• Urheber: Kasianova, Ksenia ; Kelbert, Mark ; Mozgunov, Pavel

Weitere Informationen

• DOI: 10.1002/bimj.202200301
• URN: urn:nbn:de:101:1-2023101115160239448971
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 14.08.2025, 10:58 MESZ