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Poster session 05

1559P - Towards more efficient multi-arm exercise trials in oncology: Application of a Bayesian adaptive decision-theoretic approach

Date

10 Sep 2022

Session

Poster session 05

Topics

Supportive Care and Symptom Management;  Clinical Research;  Survivorship;  Cancer Epidemiology

Tumour Site

Presenters

Laurien Buffart

Citation

Annals of Oncology (2022) 33 (suppl_7): S713-S742. 10.1016/annonc/annonc1075

Authors

L.M. Buffart1, A. Bassi2, M. Stuiver3, N. Aaronson4, G.S. Sonke5, J. Berkhof2, P. van de Ven6

Author affiliations

  • 1 Physiology, Radboud University Medical Center, Nijmegen, 6525 GA - Nijmegen/NL
  • 2 Department Of Epidemiology And Data Science, Amsterdam UMC - Vrije University Medical Centre (VUmc), 1081 HV - Amsterdam/NL
  • 3 Center For Quality Of Life And Division Of Psychosocial Research And Epidemiology, Netherlands Cancer Institute, 1006 BE - Amsterdam/NL
  • 4 Division Of Psychosocial Research & Epidemiology, NKI-AVL - Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, 1066 CX - Amsterdam/NL
  • 5 Medical Oncology, NKI-AVL - Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital, 1066 CX - Amsterdam/NL
  • 6 Department Of Data Science And Biostatistics, UMC - University Medical Center Utrecht, 3508 GA - Utrecht/NL

Resources

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Abstract 1559P

Background

Conducting a traditional, adequately powered multi-arm exercise trial is often challenging in terms of time and available resources. Novel, efficient trial designs are needed for rigorous comparison of multiple treatment arms. This study illustrates a Bayesian adaptive decision-theoretic design using a multi-arm exercise oncology trial.

Methods

In the PACES trial, 230 breast cancer patients receiving adjuvant chemotherapy were randomized to a supervised resistance and aerobic exercise intervention (OnTrack), a homebased physical activity program (OncoMove) or usual care. Data was re-analyzed using a Bayesian adaptive decision-theoretic approach incorporating interim analyses for trial continuation after every stage of 36 patients. Endpoint for the re-analyses was dose modifications (any versus none). We considered a symmetric setting aiming to identify the arm with the lowest proportion of patients requiring dose modifications, and an asymmetric setting aiming to identify exercise arms with an absolute risk reduction in dose modifications of ≥10% compared to usual care. Interim decisions for continuation were based on expected absolute increases in the probability of a correct decision in the next stage, with continuation thresholds of 1% and 0.1%. Settings with and without early dropping of arms were considered.

Results

Dose modifications occurred in 34% of patients in the usual care and OncoMove arms versus 12% in OnTrack. Frequentist analysis showed these proportions to be statistically significant (p=0.002). Using the 0.1% continuation threshold, the Bayesian adaptive decision-theoretic analyses required 72 patients to identify OnTrack as the most effective arm in the symmetric setting. In the asymmetric setting, 144 patients were required to identify OnTrack as the only exercise arm superior to usual care.

Conclusions

A Bayesian adaptive MAMS design for multi-arm trials that makes decisions on efficacy or futility based on the expected change in the posterior probabilities instead of the posterior probabilities itself substantially reduced the sample size of a three-arm exercise oncology trial. This design is worth considering for multi-arm trials.

Clinical trial identification

NTR2159.

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

The PACES trial was funded by the Dutch Cancer Society (grant ALPE 2009-4299). The development of the statistical methods was funded by the Dutch Cancer Society (grant KWF 2012-5711).

Disclosure

G.S. Sonke: Non-Financial Interests, Institutional, Advisory Board: Novartis; Financial Interests, Institutional, Research Grant: AstraZeneca, Merck Sharp & Dohme, Novartis, Roche, Agendia, Seagen. All other authors have declared no conflicts of interest.

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