Pub Date : 2024-05-21DOI: 10.1177/17407745241251812
Nam-Anh Tran, Abigail McGrory, Naveen Poonai, Anna Heath
Background/aims: Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care.
Methods: We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (Pbest) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either Pbest or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either Pbest or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial.
Results: As the number of interventions increases, the SUCRA results in a higher predictive power compared with Pbest. Using Pbest results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using Pbest and the SUCRA, respectively. Thus, we chose Pbest as the ranking method for the ALICE trial.
Conclusion: Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than Pbest. Future work should consider whether other ranking methods may also be relevant for clinical trial design.
{"title":"A comparison of alternative ranking methods in two-stage clinical trials with multiple interventions: An application to the anxiolysis for laceration repair in children trial.","authors":"Nam-Anh Tran, Abigail McGrory, Naveen Poonai, Anna Heath","doi":"10.1177/17407745241251812","DOIUrl":"10.1177/17407745241251812","url":null,"abstract":"<p><strong>Background/aims: </strong>Multi-arm, multi-stage trials frequently include a standard care to which all interventions are compared. This may increase costs and hinders comparisons among the experimental arms. Furthermore, the standard care may not be evident, particularly when there is a large variation in standard practice. Thus, we aimed to develop an adaptive clinical trial that drops ineffective interventions following an interim analysis before selecting the best intervention at the final stage without requiring a standard care.</p><p><strong>Methods: </strong>We used Bayesian methods to develop a multi-arm, two-stage adaptive trial and evaluated two different methods for ranking interventions, the probability that each intervention was optimal (P<sub><i>best</i></sub>) and using the surface under the cumulative ranking curve (SUCRA), at both the interim and final analysis. The proposed trial design determines the maximum sample size for each intervention using the Average Length Criteria. The interim analysis takes place at approximately half the pre-specified maximum sample size and aims to drop interventions for futility if either P<sub><i>best</i></sub> or the SUCRA is below a pre-specified threshold. The final analysis compares all remaining interventions at the maximum sample size to conclude superiority based on either P<sub><i>best</i></sub> or the SUCRA. The two ranking methods were compared across 12 scenarios that vary the number of interventions and the assumed differences between the interventions. The thresholds for futility and superiority were chosen to control type 1 error, and then the predictive power and expected sample size were evaluated across scenarios. A trial comparing three interventions that aim to reduce anxiety for children undergoing a laceration repair in the emergency department was then designed, known as the Anxiolysis for Laceration Repair in Children Trial (ALICE) trial.</p><p><strong>Results: </strong>As the number of interventions increases, the SUCRA results in a higher predictive power compared with P<sub><i>best</i></sub>. Using P<sub><i>best</i></sub> results in a lower expected sample size when there is an effective intervention. Using the Average Length Criterion, the ALICE trial has a maximum sample size for each arm of 100 patients. This sample size results in a 86% and 85% predictive power using P<sub><i>best</i></sub> and the SUCRA, respectively. Thus, we chose P<sub><i>best</i></sub> as the ranking method for the ALICE trial.</p><p><strong>Conclusion: </strong>Bayesian ranking methods can be used in multi-arm, multi-stage trials with no clear control intervention. When more interventions are included, the SUCRA results in a higher power than P<sub><i>best</i></sub>. Future work should consider whether other ranking methods may also be relevant for clinical trial design.</p>","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":" ","pages":"17407745241251812"},"PeriodicalIF":2.2,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141070934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1177/17407745241243308
Michael P Fay, Fan Li
Background:Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect.Methods:We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination.Results:We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual’s hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption.Conclusion:We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.
{"title":"Causal interpretation of the hazard ratio in randomized clinical trials","authors":"Michael P Fay, Fan Li","doi":"10.1177/17407745241243308","DOIUrl":"https://doi.org/10.1177/17407745241243308","url":null,"abstract":"Background:Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect.Methods:We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination.Results:We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual’s hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption.Conclusion:We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"2012 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140841816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1177/17407745241243311
Michael P Fay, Fan Li
{"title":"Reply to Heitjan’s commentary","authors":"Michael P Fay, Fan Li","doi":"10.1177/17407745241243311","DOIUrl":"https://doi.org/10.1177/17407745241243311","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"37 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-29DOI: 10.1177/17407745241243307
Daniel F Heitjan
{"title":"Comment on “Causal interpretation of the hazard ratio in randomized clinical trials” by Fay and Li","authors":"Daniel F Heitjan","doi":"10.1177/17407745241243307","DOIUrl":"https://doi.org/10.1177/17407745241243307","url":null,"abstract":"","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"24 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140830296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-27DOI: 10.1177/17407745241243045
Tom Gugel, Karen Adams, Madelon Baranoski, N David Yanez, Michael Kampp, Tesheia Johnson, Ani Aydin, Elaine C Fajardo, Emily Sharp, Aartee Potnis, Chanel Johnson, Miriam M Treggiari
Introduction:Emergency clinical research has played an important role in improving outcomes for acutely ill patients. This is due in part to regulatory measures that allow Exception From Informed Consent (EFIC) trials. The Food and Drug Administration (FDA) requires sponsor-investigators to engage in community consultation and public disclosure activities prior to initiating an Exception From Informed Consent trial. Various approaches to community consultation and public disclosure have been described and adapted to local contexts and Institutional Review Board (IRB) interpretations. The COVID-19 pandemic has precluded the ability to engage local communities through direct, in-person public venues, requiring research teams to find alternative ways to inform communities about emergency research.Methods:The PreVent and PreVent 2 studies were two Exception From Informed Consent trials of emergency endotracheal intubation, conducted in one geographic location for the PreVent Study and in two geographic locations for the PreVent 2 Study. During the period of the two studies, there was a substantial shift in the methodological approach spanning across the periods before and after the pandemic from telephone, to in-person, to virtual settings.Results:During the 10 years of implementation of Exception From Informed Consent activities for the two PreVent trials, there was overall favorable public support for the concept of Exception From Informed Consent trials and for the importance of emergency clinical research. Community concerns were few and also did not differ much by method of contact. Attendance was higher with the implementation of virtual technology to reach members of the community, and overall feedback was more positive compared with telephone contacts or in-person events. However, the proportion of survey responses received after completion of the remote, live event was substantially lower, with a greater proportion of respondents having higher education levels. This suggests less active engagement after completion of the synchronous activity and potentially higher selection bias among respondents. Importantly, we found that engagement with local community leaders was a key component to develop appropriate plans to connect with the public.Conclusion:The PreVent experience illustrated operational advantages and disadvantages to community consultation conducted primarily by telephone, in-person events, or online activities. Approaches to enhance community acceptance included partnering with community leaders to optimize the communication strategies and trust building with the involvement of Institutional Review Board representatives during community meetings. Researchers might need to pivot from in-person planning to virtual techniques while maintaining the ability to engage with the public with two-way communication approaches. Due to less active engagement, and potential for selection bias in the responders, further research is needed to addr
{"title":"Design and implementation of community consultation for research conducted under exception from informed consent regulations for the PreVent and the PreVent 2 trials: Changes over time and during the COVID-19 pandemic","authors":"Tom Gugel, Karen Adams, Madelon Baranoski, N David Yanez, Michael Kampp, Tesheia Johnson, Ani Aydin, Elaine C Fajardo, Emily Sharp, Aartee Potnis, Chanel Johnson, Miriam M Treggiari","doi":"10.1177/17407745241243045","DOIUrl":"https://doi.org/10.1177/17407745241243045","url":null,"abstract":"Introduction:Emergency clinical research has played an important role in improving outcomes for acutely ill patients. This is due in part to regulatory measures that allow Exception From Informed Consent (EFIC) trials. The Food and Drug Administration (FDA) requires sponsor-investigators to engage in community consultation and public disclosure activities prior to initiating an Exception From Informed Consent trial. Various approaches to community consultation and public disclosure have been described and adapted to local contexts and Institutional Review Board (IRB) interpretations. The COVID-19 pandemic has precluded the ability to engage local communities through direct, in-person public venues, requiring research teams to find alternative ways to inform communities about emergency research.Methods:The PreVent and PreVent 2 studies were two Exception From Informed Consent trials of emergency endotracheal intubation, conducted in one geographic location for the PreVent Study and in two geographic locations for the PreVent 2 Study. During the period of the two studies, there was a substantial shift in the methodological approach spanning across the periods before and after the pandemic from telephone, to in-person, to virtual settings.Results:During the 10 years of implementation of Exception From Informed Consent activities for the two PreVent trials, there was overall favorable public support for the concept of Exception From Informed Consent trials and for the importance of emergency clinical research. Community concerns were few and also did not differ much by method of contact. Attendance was higher with the implementation of virtual technology to reach members of the community, and overall feedback was more positive compared with telephone contacts or in-person events. However, the proportion of survey responses received after completion of the remote, live event was substantially lower, with a greater proportion of respondents having higher education levels. This suggests less active engagement after completion of the synchronous activity and potentially higher selection bias among respondents. Importantly, we found that engagement with local community leaders was a key component to develop appropriate plans to connect with the public.Conclusion:The PreVent experience illustrated operational advantages and disadvantages to community consultation conducted primarily by telephone, in-person events, or online activities. Approaches to enhance community acceptance included partnering with community leaders to optimize the communication strategies and trust building with the involvement of Institutional Review Board representatives during community meetings. Researchers might need to pivot from in-person planning to virtual techniques while maintaining the ability to engage with the public with two-way communication approaches. Due to less active engagement, and potential for selection bias in the responders, further research is needed to addr","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"133 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140812301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1177/17407745241244788
Karen M Higgins, Gregory Levin, Robert Busch
Randomization and blinding are regarded as the most important tools to help reduce bias in clinical trial designs. Randomization is used to help guarantee that treatment arms differ systematically only by treatment assignment at baseline, and blinding is used to ensure that differences in endpoint evaluation and clinical decision-making during the trial arise only from the treatment received and not, for example, the expectation or desires of the people involved. However, given that there are times when it is not feasible or ethical to conduct fully blinded trials, we discuss what can be done to improve a trial, including conducting the trial as if it were a fully blinded trial and maintaining confidentiality of ongoing study results. In this article, we review how best to design, conduct, and analyze open-label trials to ensure the highest level of study integrity and the reliability of the study conclusions.
{"title":"Considerations for open-label randomized clinical trials: Design, conduct, and analysis","authors":"Karen M Higgins, Gregory Levin, Robert Busch","doi":"10.1177/17407745241244788","DOIUrl":"https://doi.org/10.1177/17407745241244788","url":null,"abstract":"Randomization and blinding are regarded as the most important tools to help reduce bias in clinical trial designs. Randomization is used to help guarantee that treatment arms differ systematically only by treatment assignment at baseline, and blinding is used to ensure that differences in endpoint evaluation and clinical decision-making during the trial arise only from the treatment received and not, for example, the expectation or desires of the people involved. However, given that there are times when it is not feasible or ethical to conduct fully blinded trials, we discuss what can be done to improve a trial, including conducting the trial as if it were a fully blinded trial and maintaining confidentiality of ongoing study results. In this article, we review how best to design, conduct, and analyze open-label trials to ensure the highest level of study integrity and the reliability of the study conclusions.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"64 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1177/17407745241238443
Dan-Yu Lin, Jianqiao Wang, Yu Gu, Donglin Zeng
BackgroundThe current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness.MethodsWe specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials.ResultsFor remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23–1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09–1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10–1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13–1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79–1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85–1.12; p = 0.74).ConclusionsThe proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.
背景目前针对住院 COVID-19 患者的治疗试验终点仅能捕捉到患者临床过程的一部分，其统计能力和稳健性有限。我们还为临床状况的每一级改善或恶化指定了比例危险模型，并为总体治疗获益设定了共同危险比。我们将这些方法应用于自适应 COVID-19 治疗试验。结果对于雷米替韦与安慰剂相比，常见的几率比为 1.48（95% 置信区间 (CI) = 1.23-1.79；p < 0.001），常见的危险比为 1.27（95% CI = 1.09-1.47；p = 0.002）。巴利替尼加雷米替韦与单用雷米替韦相比，共同几率比为1.32（95% CI = 1.10-1.57；p = 0.002），共同危险比为1.30（95% CI = 1.13-1.49；p <；0.001）。对于β-1a干扰素加雷米替韦与单用雷米替韦，共同几率比为0.95 (95% CI = 0.79-1.14; p = 0.56)，共同危险比为0.98 (95% CI = 0.85-1.12; p = 0.74)。
{"title":"Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials","authors":"Dan-Yu Lin, Jianqiao Wang, Yu Gu, Donglin Zeng","doi":"10.1177/17407745241238443","DOIUrl":"https://doi.org/10.1177/17407745241238443","url":null,"abstract":"BackgroundThe current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness.MethodsWe specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials.ResultsFor remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23–1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09–1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10–1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13–1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79–1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85–1.12; p = 0.74).ConclusionsThe proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"6 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-15DOI: 10.1177/17407745241240401
Cody Chiuzan, Hakim-Moulay Dehbi
In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.
{"title":"The 3 + 3 design in dose-finding studies with small sample sizes: Pitfalls and possible remedies","authors":"Cody Chiuzan, Hakim-Moulay Dehbi","doi":"10.1177/17407745241240401","DOIUrl":"https://doi.org/10.1177/17407745241240401","url":null,"abstract":"In the last few years, numerous novel designs have been proposed to improve the efficiency and accuracy of phase I trials to identify the maximum-tolerated dose (MTD) or the optimal biological dose (OBD) for noncytotoxic agents. However, the conventional 3+3 approach, known for its and poor performance, continues to be an attractive choice for many trials despite these alternative suggestions. The article seeks to underscore the importance of moving beyond the 3+3 design by highlighting a different key element in trial design: the estimation of sample size and its crucial role in predicting toxicity and determining the MTD. We use simulation studies to compare the performance of the most used phase I approaches: 3+3, Continual Reassessment Method (CRM), Keyboard and Bayesian Optimal Interval (BOIN) designs regarding three key operating characteristics: the percentage of correct selection of the true MTD, the average number of patients allocated per dose level, and the average total sample size. The simulation results consistently show that the 3+3 algorithm underperforms in comparison to model-based and model-assisted designs across all scenarios and metrics. The 3+3 method yields significantly lower (up to three times) probabilities in identifying the correct MTD, often selecting doses one or even two levels below the actual MTD. The 3+3 design allocates significantly fewer patients at the true MTD, assigns higher numbers to lower dose levels, and rarely explores doses above the target dose-limiting toxicity (DLT) rate. The overall performance of the 3+3 method is suboptimal, with a high level of unexplained uncertainty and significant implications for accurately determining the MTD. While the primary focus of the article is to demonstrate the limitations of the 3+3 algorithm, the question remains about the preferred alternative approach. The intention is not to definitively recommend one model-based or model-assisted method over others, as their performance can vary based on parameters and model specifications. However, the presented results indicate that the CRM, Keyboard, and BOIN designs consistently outperform the 3+3 and offer improved efficiency and precision in determining the MTD, which is crucial in early-phase clinical trials.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"2019 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.1177/17407745231221152
Lucie Biard, Anaïs Andrillon, Rebecca B Silva, Shing M Lee
Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.
{"title":"Dose optimization for cancer treatments with considerations for late-onset toxicities","authors":"Lucie Biard, Anaïs Andrillon, Rebecca B Silva, Shing M Lee","doi":"10.1177/17407745231221152","DOIUrl":"https://doi.org/10.1177/17407745231221152","url":null,"abstract":"Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"22 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-09DOI: 10.1177/17407745241243027
Ellen Richmond, Goli Samimi, Margaret House, Leslie G Ford, Eva Szabo
BackgroundThe Early Phase Cancer Prevention Clinical Trials Program (Consortia), led by the Division of Cancer Prevention, National Cancer Institute, supports and conducts trials assessing safety, tolerability, and cancer preventive potential of a variety of interventions. Accrual to cancer prevention trials includes the recruitment of unaffected populations, posing unique challenges related to minimizing participant burden and risk, given the less evident or measurable benefits to individual participants. The Accrual Quality Improvement Program was developed to address these challenges and better understand the multiple determinants of accrual activity throughout the life of the trial. Through continuous monitoring of accrual data, Accrual Quality Improvement Program identifies positive and negative factors in real-time to optimize enrollment rates for ongoing and future trials.MethodsThe Accrual Quality Improvement Program provides a web-based centralized infrastructure for collecting, analyzing, visualizing, and storing qualitative and quantitative participant-, site-, and study-level data. The Accrual Quality Improvement Program approaches cancer prevention clinical trial accrual as multi-factorial, recognizing protocol design, potential participants’ characteristics, and individual site as well as study-wide implementation issues.ResultsThe Accrual Quality Improvement Program was used across 39 Consortia trials from 2014 to 2022 to collect comprehensive trial information. The Accrual Quality Improvement Program captures data at the participant level, including number of charts reviewed, potential participants contacted and reasons why participants were not eligible for contact or did not consent to the trial or start intervention. The Accrual Quality Improvement Program also captures site-level (e.g. staffing issues) and study-level (e.g. when protocol amendments are made) data at each step of the recruitment/enrollment process, from potential participant identification to contact, consent, intervention, and study completion using a Recruitment Journal. Accrual Quality Improvement Program’s functionality also includes tracking and visualization of a trial’s cumulative accrual rate compared to the projected accrual rate, including a zone-based performance rating with corresponding quality improvement intervention recommendations.ConclusionThe challenges associated with recruitment and timely completion of early phase cancer prevention clinical trials necessitate a data collection program capable of continuous collection and quality improvement. The Accrual Quality Improvement Program collects cumulative data across National Cancer Institute, Division of Cancer Prevention early phase clinical trials, providing the opportunity for real-time review of participant-, site-, and study-level data and thereby enables responsive recruitment strategy and protocol modifications for improved recruitment rates to ongoing trials. Of note, Accrual Quality I
{"title":"Accrual Quality Improvement Program for clinical trials","authors":"Ellen Richmond, Goli Samimi, Margaret House, Leslie G Ford, Eva Szabo","doi":"10.1177/17407745241243027","DOIUrl":"https://doi.org/10.1177/17407745241243027","url":null,"abstract":"BackgroundThe Early Phase Cancer Prevention Clinical Trials Program (Consortia), led by the Division of Cancer Prevention, National Cancer Institute, supports and conducts trials assessing safety, tolerability, and cancer preventive potential of a variety of interventions. Accrual to cancer prevention trials includes the recruitment of unaffected populations, posing unique challenges related to minimizing participant burden and risk, given the less evident or measurable benefits to individual participants. The Accrual Quality Improvement Program was developed to address these challenges and better understand the multiple determinants of accrual activity throughout the life of the trial. Through continuous monitoring of accrual data, Accrual Quality Improvement Program identifies positive and negative factors in real-time to optimize enrollment rates for ongoing and future trials.MethodsThe Accrual Quality Improvement Program provides a web-based centralized infrastructure for collecting, analyzing, visualizing, and storing qualitative and quantitative participant-, site-, and study-level data. The Accrual Quality Improvement Program approaches cancer prevention clinical trial accrual as multi-factorial, recognizing protocol design, potential participants’ characteristics, and individual site as well as study-wide implementation issues.ResultsThe Accrual Quality Improvement Program was used across 39 Consortia trials from 2014 to 2022 to collect comprehensive trial information. The Accrual Quality Improvement Program captures data at the participant level, including number of charts reviewed, potential participants contacted and reasons why participants were not eligible for contact or did not consent to the trial or start intervention. The Accrual Quality Improvement Program also captures site-level (e.g. staffing issues) and study-level (e.g. when protocol amendments are made) data at each step of the recruitment/enrollment process, from potential participant identification to contact, consent, intervention, and study completion using a Recruitment Journal. Accrual Quality Improvement Program’s functionality also includes tracking and visualization of a trial’s cumulative accrual rate compared to the projected accrual rate, including a zone-based performance rating with corresponding quality improvement intervention recommendations.ConclusionThe challenges associated with recruitment and timely completion of early phase cancer prevention clinical trials necessitate a data collection program capable of continuous collection and quality improvement. The Accrual Quality Improvement Program collects cumulative data across National Cancer Institute, Division of Cancer Prevention early phase clinical trials, providing the opportunity for real-time review of participant-, site-, and study-level data and thereby enables responsive recruitment strategy and protocol modifications for improved recruitment rates to ongoing trials. Of note, Accrual Quality I","PeriodicalId":10685,"journal":{"name":"Clinical Trials","volume":"28 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140593853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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