scholarly journals Number of Patients per Cohort and Sample Size Considerations Using Dose Escalation with Overdose Control

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Mourad Tighiouart ◽  
André Rogatko
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Phase I ◽  
Sample Size ◽  
Dose Escalation ◽  
Experimental Treatment ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trials ◽  
Number Of Patients ◽  
Therapeutic Doses

The main objective of cancer phase I clinical trials is to determine a maximum tolerated dose (MTD) of a new experimental treatment. In practice, most of these trials are designed so that three patients per cohort are treated at the same dose level. In this paper, we compare the safety and efficiency of trials using the escalation with overdose control (EWOC) scheme designed with three or only one patient per cohort. We show through simulations that the number of patients per cohort does not impact the proportion of patients given therapeutic doses, safety of the trial, and efficiency of the estimate of the MTD. Additionally, we present guidelines and tabulated values on the number of patients needed to design a phase I cancer clinical trial using EWOC to achieve a given accuracy of the estimate of the MTD.

scholarly journals A dynamic stopping rule for phase I clinical trials

2018 ◽  
Vol 55 (1) ◽  
pp. 17-30 ◽  
Author(s):  
M. Iftakhar Alam ◽  
Mohaimen Mansur
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Response ◽  
Parameter Estimate ◽  
Maximum Tolerated Dose ◽  
Stopping Rule ◽  
Bayesian Posterior Probability ◽  
Phase I Clinical Trials ◽  
Response Data ◽  
Number Of Patients

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

Stopping rules for phase I clinical trials with dose expansion cohorts

2021 ◽  
pp. 096228022110649
Author(s):  
Sean M Devlin ◽  
Alexia Iasonos ◽  
John O’Quigley
Keyword(s):  
Clinical Trials ◽  
Sample Size ◽  
Dose Escalation ◽  
Small Sample Size ◽  
Maximum Tolerated Dose ◽  
Small Sample ◽  
Stopping Rules ◽  
Phase I Clinical Trials ◽  
Study Protocols ◽  
Dose Levels

Many clinical trials incorporate stopping rules to terminate early if the clinical question under study can be answered with a high degree of confidence. While common in later-stage trials, these rules are rarely implemented in dose escalation studies, due in part to the relatively smaller sample size of these designs. However, even with a small sample size, this paper shows that easily implementable stopping rules can terminate dose-escalation early with minimal loss to the accuracy of maximum tolerated dose estimation. These stopping rules are developed when the goal is to identify one or two dose levels, as the maximum tolerated dose and co-maximum tolerated dose. In oncology, this latter goal is frequently considered when the study includes dose-expansion cohorts, which are used to further estimate and compare the safety and efficacy of one or two dose levels. As study protocols do not typically halt accrual between escalation and expansion, early termination is of clinical importance as it either allows for additional patients to be treated as part of the dose expansion cohort to obtain more precise estimates of the study endpoints or allows for an overall reduction in the total sample size.

scholarly journals Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

2014 ◽  
Vol 32 (23) ◽  
pp. 2505-2511 ◽  
Author(s):  
Alexia Iasonos ◽  
John O'Quigley
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Dose Escalation ◽  
Late Onset ◽  
Maximum Tolerated Dose ◽  
Dose Finding ◽  
Design Parameters ◽  
Phase I Trials ◽  
Phase I Clinical Trials ◽  
Trial Duration

Purpose We provide a comprehensive review of adaptive phase I clinical trials in oncology that used a statistical model to guide dose escalation to identify the maximum-tolerated dose (MTD). We describe the clinical setting, practical implications, and safety of such applications, with the aim of understanding how these designs work in practice. Methods We identified 53 phase I trials published between January 2003 and September 2013 that used the continual reassessment method (CRM), CRM using escalation with overdose control, or time-to-event CRM for late-onset toxicities. Study characteristics, design parameters, dose-limiting toxicity (DLT) definition, DLT rate, patient-dose allocation, overdose, underdose, sample size, and trial duration were abstracted from each study. In addition, we examined all studies in terms of safety, and we outlined the reasons why escalations occur and under what circumstances. Results On average, trials accrued 25 to 35 patients over a 2-year period and tested five dose levels. The average DLT rate was 18%, which is lower than in previous reports, whereas all levels above the MTD had an average DLT rate of 36%. On average, 39% of patients were treated at the MTD, and 74% were treated at either the MTD or an adjacent level (one level above or below). Conclusion This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.

Hierarchical models for sharing information across populations in phase I dose-escalation studies

2017 ◽  
Vol 27 (11) ◽  
pp. 3447-3459 ◽  
Author(s):  
Kristen M Cunanan ◽  
Joseph S Koopmeiners
Keyword(s):  
Clinical Trial ◽  
Phase I ◽  
Dose Escalation ◽  
Hierarchical Models ◽  
Hierarchical Modeling ◽  
Maximum Tolerated Dose ◽  
Phase I Clinical Trial ◽  
Dose Finding ◽  
Novel Treatment ◽  
Number Of Patients

The primary goal of a phase I clinical trial in oncology is to evaluate the safety of a novel treatment and identify the maximum tolerated dose, defined as the maximum dose with a toxicity rate below some pre-specified threshold. Researchers are often interested in evaluating the performance of a novel treatment in multiple patient populations, which may require multiple phase I trials if the treatment is to be used with background standard-of-care that varies by population. An alternate approach is to run parallel trials but combine the data through a hierarchical model that allows for a different maximum tolerated dose in each population but shares information across populations to achieve a more accurate estimate of the maximum tolerated dose. In this manuscript, we discuss hierarchical extensions of three commonly used models for the dose–toxicity relationship in phase I oncology trials. We then propose three dose-finding guidelines for phase I oncology trials using hierarchical modeling. The proposed guidelines allow us to fully realize the benefits of hierarchical modeling while achieving a similar toxicity profile to standard phase I designs. Finally, we evaluate the operating characteristics of a phase I clinical trial using the proposed hierarchical models and dose-finding guidelines by simulation. Our simulation results suggest that incorporating hierarchical modeling in phase I dose-escalation studies will increase the probability of correctly identifying the maximum tolerated dose and the number of patients treated at the maximum tolerated dose, while decreasing the rate of dose-limiting toxicities and number of patients treated above the maximum tolerated dose, in most cases.

scholarly journals Incorporating a Patient Dichotomous Characteristic in Cancer Phase I Clinical Trials Using Escalation with Overdose Control

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Mourad Tighiouart ◽  
Galen Cook-Wiens ◽  
André Rogatko
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Maximum Tolerated Dose ◽  
The Other ◽  
Phase I Clinical Trials ◽  
Other Hand ◽  
Number Of Patients

We describe a design for cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose (MTD) given patient’s specific dichotomous covariate value. The design is Bayesian adaptive and is an extension of escalation with overdose control (EWOC). We will assess the performance of this method by comparing the following designs via extensive simulations: (1) design using a covariate; patients are accrued to the trial sequentially and the dose given to a patient depends on his/her baseline covariate value, (2) design ignoring the covariate; patients are accrued to the trial sequentially and the dose given to a patient does not depend on his/her baseline covariate value, and (3) design using separate trials; in each group, patients are accrued to the trial sequentially and EWOC is implemented in each group. These designs are compared with respect to safety of the trial and efficiency of the estimates of the MTDs via extensive simulations. We found that ignoring a significant baseline binary covariate in the model results in a substantial number of patients being overdosed. On the other hand, accounting for a nonsignificant covariate in the model has practically no effect on the safety of the trial and efficiency of the estimates of the MTDs.

A Study on the Characteristics of Healthy Volunteers who Participate in Phase I Clinical Trials in Korea

2021 ◽  
pp. 155626462110342
Author(s):  
Ji-Hye Seo ◽  
Ock-Joo Kim ◽  
Sang-Ho Yoo ◽  
Eun Kyung Choi ◽  
Ji-Eun Park
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Phase I ◽  
Healthy Volunteers ◽  
Health Science ◽  
Trial Participation ◽  
Phase I Clinical Trials ◽  
Financial Reward ◽  
Therapeutic Purpose ◽  
The Republic

The phase I trial is the first step in administering a drug to humans, but it has no therapeutic purpose. Under the absence of therapeutic purpose, healthy volunteers demonstrated different motivations, unlike the actual patients participating in trials. There were many reported motivations, such as financial motivation, contributing to the health science, accessing ancillary health care benefits, scientific interest or interest in the goals of the study, meeting people, and general curiosity. The aim of this study was to identify the motivation and characteristics of healthy volunteers participating in phase I trials in the Republic of Korea. We gave surveys to 121 healthy volunteers to study their demographic characteristics and the reasons of participation. We identified whether the decision to participate in the research was influenced by demographic factors and whether the perception and attitudes toward the research were influenced by the characteristics of the healthy volunteers. After completion of the first survey, 12 healthy volunteers who had participated in a phase I clinical trial were selected to answer the second interview. According to our survey, most healthy volunteers were unmarried men and economically dependent. Most of them participated in the study because of financial reward. The most important factor to measure financial reward was the research period. Also, 43% of the volunteers were university students, 42% answered “university graduation” and 55% were residing in family-owned houses. Many healthy volunteers were found to be living in family homes and to have a student status or lack of economic independence. Results of the survey showed that 64% of respondents indicated having more than one clinical trial participation. In-depth interviews showed that healthy volunteers had diverse motivation to participate in research and that healthy volunteer perceive the clinical trial positively. The main motivation for healthy volunteers’ participation in research was “financial reward.” Healthy volunteers also considered research schedules, processes, and safety, and had a positive perception of clinical trials, but they thought that the public has a negative perception.

scholarly journals Provider Recommendations for Phase I Clinical Trials Within a Shared Decision-Making Model in Phase I Cancer Clinical Trial Discussions

2020 ◽  
Vol 16 (9) ◽  
pp. e859-e867
Author(s):  
Rachel S. Hianik ◽  
Gavin P. Campbell ◽  
Eli Abernethy ◽  
Colleen Lewis ◽  
Christina S. Wu ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Decision Making ◽  
Clinical Trials ◽  
Phase I ◽  
Shared Decision Making ◽  
Phase I Trial ◽  
Performance Status ◽  
Shared Decision ◽  
Phase I Clinical Trials ◽  
Decision Making Model

PURPOSE: Debate continues over whether explicit recommendations for a clinical trial should be included as an element of shared decision making within oncology. We aimed to determine if and how providers make explicit recommendations in the setting of phase I cancer clinical trials. METHODS: Twenty-three patient/provider conversations about phase I trials were analyzed to determine how recommendations are made and how the conversations align with a shared decision-making framework. In addition, 19 providers (9 of whose patient encounters were observed) were interviewed about the factors they consider when deciding whether to recommend a phase I trial. RESULTS: We found that providers are comprehensive in the factors they consider when recommending clinical trials. The two most frequently stated factors were performance status (89%) and patient preferences (84%). Providers made explicit recommendations in 19 conversations (83%), with 12 of those being for a phase I trial (12 [63%] of 19). They made these recommendations in a manner consistent with a shared decision-making model; 18 (95%) of the 19 conversations during which a recommendation was made included all steps, or all but 1 step, of shared decision making, as did 11 of the 12 conversations during which a phase I trial was recommended. In 7 (58%) of these later conversations, providers also emphasized the importance of the patient’s opinion. CONCLUSION: We suggest that providers not hesitate to make explicit recommendations for phase I clinical trials, because they are able to do so in a manner consistent with shared decision making. With further research, these results can be applied to other clinical trial settings.

Sequential or combined designs for Phase I/II clinical trials? A simulation study

2019 ◽  
Vol 16 (6) ◽  
pp. 635-644 ◽  
Author(s):  
Caroline Rossoni ◽  
Aurélie Bardet ◽  
Birgit Geoerger ◽  
Xavier Paoletti
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Phase Ii ◽  
Dose Escalation ◽  
Therapeutic Index ◽  
Maximum Tolerated Dose ◽  
Correct Decision ◽  
Operating Characteristics ◽  
In Series ◽  
Expansion Cohort

Background: Phase I and Phase II clinical trials aim at identifying a dose that is safe and active. Both phases are increasingly combined. For Phase I/II trials, two main types of designs are debated: a dose-escalation stage to select the maximum tolerated dose, followed by an expansion cohort to investigate its activity (dose-escalation followed by an expansion cohort), or a joint modelling to identify the best trade-off between toxicity and activity (efficacy–toxicity). We explore this question in the context of a paediatric Phase I/II platform trial. Methods: In series of simulations, we assessed the operating characteristics of dose-escalation followed by an expansion cohort (DE-EC) designs without and with reassessment of the maximum tolerated dose during the expansion cohort (DE-ECext) and of the efficacy–toxicity (EffTox) design. We investigated the probability to identify an active and tolerable agent, that is, the percentage of correct decision, for various dose-toxicity activity scenarios. Results: For a large therapeutic index, the percentage of correct decision reached 96.0% for efficacy–toxicity versus 76.1% for dose-escalation followed by an expansion cohort versus 79.6% for DE-ECext. Conversely, when all doses were deemed not active, the percentage of correct decision was 47% versus 55.9% versus 69.2%, respectively, for efficacy–toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Finally, in the case of a narrow therapeutic index, the percentage of correct decision was 48.0% versus 64.3% versus 67.2%, respectively, efficacy–toxicity, dose-escalation followed by an expansion cohort and DE-ECext. Conclusion: As narrow indexes are common in oncology, according to the present results, the sequential dose-escalation followed by an expansion cohort is recommended. The importance to re-estimate the maximum tolerated dose during the expansion cohort is confirmed. However, despite their theoretical advantages, Phase I/II designs are challenged by the variations in populations between the Phase I and the Phase II parts and by the lagtime in the evaluation of toxicity and activity.

Do Symptoms Matter When Considering Patients for Phase I Clinical Trials? A Pilot Study of Older Adults With Advanced Cancer

2011 ◽  
Vol 28 (7) ◽  
pp. 463-466 ◽  
Author(s):  
Jennifer M. Healy ◽  
Taral Patel ◽  
Shuko Lee ◽  
Sandra Sanchez-Reilly
Keyword(s):  
Older Adults ◽  
Clinical Trials ◽  
Phase I ◽  
Advanced Cancer ◽  
Poor Prognosis ◽  
Chart Review ◽  
Retrospective Chart Review ◽  
Experimental Treatment ◽  
Pain Scale ◽  
Phase I Clinical Trials

Background: Older adults (OA) with advanced cancer (AC) undergoing phase I clinical trials (PICT) have poor prognosis. There are no studies which describe symptoms experienced by OA. Methods: Retrospective chart review of PICT participants >60 years. OA were compared by age (>65 vs 60-65) and by number of symptoms (>3 vs ≤3). Results: N = 56. Mean age = 67.09; 48.21% female. Median life-expectancy = 5 months (interquartile range = 2-9 months); 80.36% had pain; of those 64% without pain scale. Most did not have interdisciplinary professionals or hospice referrals. Older adults with >3 symptoms had more admissions (37.5% vs 14.29%; P = .0335), complications (46.43% vs 16.07%; P = .0026), and greater decline in functional status (24 participants >3 symptoms vs 8; P = .0173). There were no significant differences comparing OA by age. Conclusions: Older adults enrolled in PICT with more symptoms may sacrifice QOL for experimental treatment.

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