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Boonstra PS, Owen DR, Kang J. Shrinkage priors for isotonic probability vectors and binary data modeling, with applications to dose-response modeling. Pharm Stat 2024. [PMID: 38400582 DOI: 10.1002/pst.2372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 12/12/2023] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
Motivated by the need to model dose-response or dose-toxicity curves in clinical trials, we develop a new horseshoe-based prior for Bayesian isotonic regression modeling a binary outcome against an ordered categorical predictor, where the probability of the outcome is assumed to be monotonically non-decreasing with the predictor. The set of differences between outcome probabilities in consecutive categories of the predictor is equipped with a multivariate prior having support over simplex. The Dirichlet distribution, which can be derived from a normalized sum of independent gamma-distributed random variables, is a natural choice of prior, but using mathematical and simulation-based arguments, we show that the resulting posterior is prone to underflow and other numerical instabilities, even under simple data configurations. We propose an alternative prior based on horseshoe-type shrinkage that is numerically more stable. We show that this horseshoe-based prior is not subject to the numerical instability seen in the Dirichlet/gamma-based prior and that the horseshoe-based posterior can estimate the underlying true curve more efficiently than the Dirichlet-based one. We demonstrate the use of this prior in a model predicting the occurrence of radiation-induced lung toxicity in lung cancer patients as a function of dose delivered to normal lung tissue. Our methodology is implemented in the R package isotonicBayes and therefore suitable for use in the design of dose-finding studies or other dose-response modeling contexts.
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Affiliation(s)
- Philip S Boonstra
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Daniel R Owen
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jian Kang
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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Kaizer AM, Belli HM, Ma Z, Nicklawsky AG, Roberts SC, Wild J, Wogu AF, Xiao M, Sabo RT. Recent innovations in adaptive trial designs: A review of design opportunities in translational research. J Clin Transl Sci 2023; 7:e125. [PMID: 37313381 PMCID: PMC10260347 DOI: 10.1017/cts.2023.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 06/15/2023] Open
Abstract
Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.
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Affiliation(s)
- Alexander M. Kaizer
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hayley M. Belli
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Zhongyang Ma
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrew G. Nicklawsky
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Samantha C. Roberts
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica Wild
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adane F. Wogu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mengli Xiao
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Roy T. Sabo
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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Bridges AJ, Mehta RK, Shukla S, Schipper MJ, Lawrence TS, Nyati MK. Drug-development, dose-selection, rational combinations from bench-to-bedside: are there any lessons worth revisiting? Oncotarget 2021; 12:1032-1036. [PMID: 34084277 PMCID: PMC8169070 DOI: 10.18632/oncotarget.27931] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2021] [Indexed: 01/01/2023] Open
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