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Msaouel P, Lee J, Thall PF. Risk-benefit trade-offs and precision utilities in phase I-II clinical trials. Clin Trials 2024; 21:287-297. [PMID: 38111231 PMCID: PMC11132955 DOI: 10.1177/17407745231214750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Abstract
BACKGROUND Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life. PURPOSE While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups. METHODS AND CONCLUSIONS Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Juhee Lee
- Department of Statistics, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Zang Y, Guo B, Qiu Y, Liu H, Opyrchal M, Lu X. Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies. Clin Trials 2024; 21:298-307. [PMID: 38205644 PMCID: PMC11132954 DOI: 10.1177/17407745231220661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.
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Affiliation(s)
- Yong Zang
- Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University
- Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University
| | - Beibei Guo
- Department of Experimental Statistics, Louisiana State University
| | - Yingjie Qiu
- Department of Biostatistics and Health Data Sciences, School of Medicine, Indiana University
| | - Hao Liu
- Department of Biostatistics and Epidemiology, Cancer Institute of New Jersey, Rutgers University
| | | | - Xiongbin Lu
- Department of Medical and Molecular Genetics, School of Medicine, Indiana University
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Thall PF, Zang Y, Chapple AG, Yuan Y, Lin R, Marin D, Msaouel P. Novel Clinical Trial Designs with Dose Optimization to Improve Long-term Outcomes. Clin Cancer Res 2023; 29:4549-4554. [PMID: 37725573 PMCID: PMC10841062 DOI: 10.1158/1078-0432.ccr-23-2222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/25/2023] [Accepted: 09/14/2023] [Indexed: 09/21/2023]
Abstract
Conventional designs for choosing a dose for a new therapy may select doses that are unsafe or ineffective and fail to optimize progression-free survival time, overall survival time, or response/remission duration. We explain and illustrate limitations of conventional dose-finding designs and make four recommendations to address these problems. When feasible, a dose-finding design should account for long-term outcomes, include screening rules that drop unsafe or ineffective doses, enroll an adequate sample size, and randomize patients among doses. As illustrations, we review three designs that include one or more of these features. The first illustration is a trial that randomized patients among two cell therapy doses and standard of care in a setting where it was assumed on biological grounds that dose toxicity and dose-response curves did not necessarily increase with cell dose. The second design generalizes phase I-II by first identifying a set of candidate doses, rather than one dose, randomizing additional patients among the candidates, and selecting an optimal dose to maximize progression-free survival over a longer follow-up period. The third design combines a phase I-II trial and a group sequential randomized phase III trial by using survival time data available after the first stage of phase III to reoptimize the dose selected in phase I-II. By incorporating one or more of the recommended features, these designs improve the likelihood that a selected dose or schedule will be optimal, and thus will benefit future patients and obtain regulatory approval.
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Affiliation(s)
- Peter F. Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Yong Zang
- Department of Biostatistics and Health Data Science, Center for Computational Biology and Bioinformatics, School of Medicine, Indiana University, Indianapolis, Indiana, USA
| | - Andrew G. Chapple
- Department of Interdisciplinary Oncology, School of Medicine, LSU Health Sciences Center, New Orleans, USA
| | - Ying Yuan
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Ruitao Lin
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - David Marin
- Department of Stem Cell Transplantation and Cellular Therapy, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Pavlos Msaouel
- Department of Genitourinary Medical Oncology, M.D. Anderson Cancer Center, Houston, Texas, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, USA
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Msaouel P, Lee J, Thall PF. Interpreting Randomized Controlled Trials. Cancers (Basel) 2023; 15:4674. [PMID: 37835368 PMCID: PMC10571666 DOI: 10.3390/cancers15194674] [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: 08/30/2023] [Revised: 09/19/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Juhee Lee
- Department of Statistics, University of California Santa Cruz, Santa Cruz, CA 95064, USA;
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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Msaouel P. Less is More? First Impressions From COSMIC-313. Cancer Invest 2023; 41:101-106. [PMID: 36239611 DOI: 10.1080/07357907.2022.2136681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The COSMIC-313 phase 3 randomized controlled trial tested the triplet combination of cabozantinib with nivolumab and ipilimumab in comparison with nivolumab plus ipilimumab control as fist-line systemic therapy in metastatic clear cell renal cell carcinoma. The first results presented at the 2022 European Society of Medical Oncology Congress are a milestone for the renal cell carcinoma field because they signal the advent of triplet combinations as potential treatment options for our patients. The present commentary highlights some considerations and potential next steps based on these first impressions.
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Affiliation(s)
- Pavlos Msaouel
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,David H. Koch Center for Applied Research of Genitourinary Cancers, The University of Texas, MD Anderson Cancer Center, Houston, Texas, USA
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