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    Rights statement: The final, definitive version of this article has been published in the Journal, Clinical Trials, 17 (5), 2020, © SAGE Publications Ltd, 2020 by SAGE Publications Ltd at the Clinical Trials page: https://journals.sagepub.com/home/CTJ on SAGE Journals Online: http://journals.sagepub.com/

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A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation

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A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation. / Abbas, R.; Rossoni, C.; Jaki, T.; Paoletti, X.; Mozgunov, P.

In: Clinical Trials, Vol. 17, No. 5, 01.10.2020, p. 522-534.

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Abbas, R. ; Rossoni, C. ; Jaki, T. ; Paoletti, X. ; Mozgunov, P. / A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation. In: Clinical Trials. 2020 ; Vol. 17, No. 5. pp. 522-534.

Bibtex

@article{99a18aacab27493094d5c3fd61d96223,
title = "A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation",
abstract = "Background/Aims: In oncology, new combined treatments make it difficult to order dose levels according to monotonically increasing toxicity. New flexible dose-finding designs that take into account uncertainty in dose levels ordering were compared with classical designs through simulations in the setting of the monotonicity assumption violation. We give recommendations for the choice of dose-finding design. Methods: Motivated by a clinical trial for patients with high-risk neuroblastoma, we considered designs that require a monotonicity assumption, the Bayesian Continual Reassessment Method, the modified Toxicity Probability Interval, the Bayesian Optimal Interval design, and designs that relax monotonicity assumption, the Bayesian Partial Ordering Continual Reassessment Method and the No Monotonicity Assumption design. We considered 15 scenarios including monotonic and non-monotonic dose–toxicity relationships among six dose levels. Results: The No Monotonicity Assumption and Partial Ordering Continual Reassessment Method designs were robust to the violation of the monotonicity assumption. Under non-monotonic scenarios, the No Monotonicity Assumption design selected the correct dose level more often than alternative methods on average. Under the majority of monotonic scenarios, the Partial Ordering Continual Reassessment Method selected the correct dose level more often than the No Monotonicity Assumption design. Other designs were impacted by the violation of the monotonicity assumption with a proportion of correct selections below 20% in most scenarios. Under monotonic scenarios, the highest proportions of correct selections were achieved using the Continual Reassessment Method and the Bayesian Optimal Interval design (between 52.8% and 73.1%). The costs of relaxing the monotonicity assumption by the No Monotonicity Assumption design and Partial Ordering Continual Reassessment Method were decreases in the proportions of correct selections under monotonic scenarios ranging from 5.3% to 20.7% and from 1.4% to 16.1%, respectively, compared with the best performing design and were higher proportions of patients allocated to toxic dose levels during the trial. Conclusions: Innovative oncology treatments may no longer follow monotonic dose levels ordering which makes standard phase I methods fail. In such a setting, appropriate designs, as the No Monotonicity Assumption or Partial Ordering Continual Reassessment Method designs, should be used to safely determine recommended for phase II dose. {\textcopyright} The Author(s) 2020.",
keywords = "Dose escalation, monotonicity assumption, oncology, partial ordering, phase I",
author = "R. Abbas and C. Rossoni and T. Jaki and X. Paoletti and P. Mozgunov",
note = "The final, definitive version of this article has been published in the Journal, Clinical Trials, 17 (5), 2020, {\textcopyright} SAGE Publications Ltd, 2020 by SAGE Publications Ltd at the Clinical Trials page: https://journals.sagepub.com/home/CTJ on SAGE Journals Online: http://journals.sagepub.com/ ",
year = "2020",
month = oct,
day = "1",
doi = "10.1177/1740774520932130",
language = "English",
volume = "17",
pages = "522--534",
journal = "Clinical Trials",
issn = "1740-7745",
publisher = "SAGE Publications Ltd",
number = "5",

}

RIS

TY - JOUR

T1 - A comparison of phase I dose-finding designs in clinical trials with monotonicity assumption violation

AU - Abbas, R.

AU - Rossoni, C.

AU - Jaki, T.

AU - Paoletti, X.

AU - Mozgunov, P.

N1 - The final, definitive version of this article has been published in the Journal, Clinical Trials, 17 (5), 2020, © SAGE Publications Ltd, 2020 by SAGE Publications Ltd at the Clinical Trials page: https://journals.sagepub.com/home/CTJ on SAGE Journals Online: http://journals.sagepub.com/

PY - 2020/10/1

Y1 - 2020/10/1

N2 - Background/Aims: In oncology, new combined treatments make it difficult to order dose levels according to monotonically increasing toxicity. New flexible dose-finding designs that take into account uncertainty in dose levels ordering were compared with classical designs through simulations in the setting of the monotonicity assumption violation. We give recommendations for the choice of dose-finding design. Methods: Motivated by a clinical trial for patients with high-risk neuroblastoma, we considered designs that require a monotonicity assumption, the Bayesian Continual Reassessment Method, the modified Toxicity Probability Interval, the Bayesian Optimal Interval design, and designs that relax monotonicity assumption, the Bayesian Partial Ordering Continual Reassessment Method and the No Monotonicity Assumption design. We considered 15 scenarios including monotonic and non-monotonic dose–toxicity relationships among six dose levels. Results: The No Monotonicity Assumption and Partial Ordering Continual Reassessment Method designs were robust to the violation of the monotonicity assumption. Under non-monotonic scenarios, the No Monotonicity Assumption design selected the correct dose level more often than alternative methods on average. Under the majority of monotonic scenarios, the Partial Ordering Continual Reassessment Method selected the correct dose level more often than the No Monotonicity Assumption design. Other designs were impacted by the violation of the monotonicity assumption with a proportion of correct selections below 20% in most scenarios. Under monotonic scenarios, the highest proportions of correct selections were achieved using the Continual Reassessment Method and the Bayesian Optimal Interval design (between 52.8% and 73.1%). The costs of relaxing the monotonicity assumption by the No Monotonicity Assumption design and Partial Ordering Continual Reassessment Method were decreases in the proportions of correct selections under monotonic scenarios ranging from 5.3% to 20.7% and from 1.4% to 16.1%, respectively, compared with the best performing design and were higher proportions of patients allocated to toxic dose levels during the trial. Conclusions: Innovative oncology treatments may no longer follow monotonic dose levels ordering which makes standard phase I methods fail. In such a setting, appropriate designs, as the No Monotonicity Assumption or Partial Ordering Continual Reassessment Method designs, should be used to safely determine recommended for phase II dose. © The Author(s) 2020.

AB - Background/Aims: In oncology, new combined treatments make it difficult to order dose levels according to monotonically increasing toxicity. New flexible dose-finding designs that take into account uncertainty in dose levels ordering were compared with classical designs through simulations in the setting of the monotonicity assumption violation. We give recommendations for the choice of dose-finding design. Methods: Motivated by a clinical trial for patients with high-risk neuroblastoma, we considered designs that require a monotonicity assumption, the Bayesian Continual Reassessment Method, the modified Toxicity Probability Interval, the Bayesian Optimal Interval design, and designs that relax monotonicity assumption, the Bayesian Partial Ordering Continual Reassessment Method and the No Monotonicity Assumption design. We considered 15 scenarios including monotonic and non-monotonic dose–toxicity relationships among six dose levels. Results: The No Monotonicity Assumption and Partial Ordering Continual Reassessment Method designs were robust to the violation of the monotonicity assumption. Under non-monotonic scenarios, the No Monotonicity Assumption design selected the correct dose level more often than alternative methods on average. Under the majority of monotonic scenarios, the Partial Ordering Continual Reassessment Method selected the correct dose level more often than the No Monotonicity Assumption design. Other designs were impacted by the violation of the monotonicity assumption with a proportion of correct selections below 20% in most scenarios. Under monotonic scenarios, the highest proportions of correct selections were achieved using the Continual Reassessment Method and the Bayesian Optimal Interval design (between 52.8% and 73.1%). The costs of relaxing the monotonicity assumption by the No Monotonicity Assumption design and Partial Ordering Continual Reassessment Method were decreases in the proportions of correct selections under monotonic scenarios ranging from 5.3% to 20.7% and from 1.4% to 16.1%, respectively, compared with the best performing design and were higher proportions of patients allocated to toxic dose levels during the trial. Conclusions: Innovative oncology treatments may no longer follow monotonic dose levels ordering which makes standard phase I methods fail. In such a setting, appropriate designs, as the No Monotonicity Assumption or Partial Ordering Continual Reassessment Method designs, should be used to safely determine recommended for phase II dose. © The Author(s) 2020.

KW - Dose escalation

KW - monotonicity assumption

KW - oncology

KW - partial ordering

KW - phase I

U2 - 10.1177/1740774520932130

DO - 10.1177/1740774520932130

M3 - Journal article

VL - 17

SP - 522

EP - 534

JO - Clinical Trials

JF - Clinical Trials

SN - 1740-7745

IS - 5

ER -