Unified exact design with early stopping rules for single arm clinical trials with multiple endpoints

2021 ◽  
pp. 096228022110130
Author(s):  
Wei Wei ◽  
Denise Esserman ◽  
Michael Kane ◽  
Daniel Zelterman
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Decision Rules ◽  
Stopping Rules ◽  
Multiple Endpoints ◽  
Shiny App ◽  
R Shiny ◽  
Early Phase Clinical Trials ◽  
User Friendly ◽  
Exact Design

Adaptive designs are gaining popularity in early phase clinical trials because they enable investigators to change the course of a study in response to accumulating data. We propose a novel design to simultaneously monitor several endpoints. These include efficacy, futility, toxicity and other outcomes in early phase, single-arm studies. We construct a recursive relationship to compute the exact probabilities of stopping for any combination of endpoints without the need for simulation, given pre-specified decision rules. The proposed design is flexible in the number and timing of interim analyses. A R Shiny app with user-friendly web interface has been created to facilitate the implementation of the proposed design.

scholarly journals BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials

2021 ◽  
pp. 91-101
Author(s):  
Yanhong Zhou ◽  
Ruitao Lin ◽  
Ying-Wei Kuo ◽  
J. Jack Lee ◽  
Ying Yuan
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Single Agent ◽  
Adaptive Designs ◽  
Software Platform ◽  
Web Based ◽  
Major Barrier ◽  
R Shiny ◽  
Early Phase Clinical Trials ◽  
User Friendly

PURPOSE Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials. METHODS We used R Shiny to develop a web-based software platform to facilitate the use of recent novel adaptive designs. RESULTS We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software. CONCLUSION The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.

Monitoring event times in early phase clinical trials: some practical issues

2005 ◽  
Vol 2 (6) ◽  
pp. 467-478 ◽  
Author(s):  
Peter F Thall ◽  
Leiko H Wooten ◽  
Nizar M Tannir
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Event Times ◽  
Early Phase Clinical Trials

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

scholarly journals Simian adenovirus vector production for early-phase clinical trials: A simple method applicable to multiple serotypes and using entirely disposable product-contact components

Vaccine ◽  
2019 ◽  
Vol 37 (47) ◽  
pp. 6951-6961 ◽  
Author(s):  
Sofiya Fedosyuk ◽  
Thomas Merritt ◽  
Marco Polo Peralta-Alvarez ◽  
Susan J Morris ◽  
Ada Lam ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Early Phase ◽  
Adenovirus Vector ◽  
Simple Method ◽  
Simian Adenovirus ◽  
Early Phase Clinical Trials

scholarly journals Targeting for Success: Demonstrating Proof-of-Concept with Mechanistic Early Phase Clinical Pharmacology Studies for Disease-Modification in Neurodegenerative Disorders

2021 ◽  
Vol 22 (4) ◽  
pp. 1615
Author(s):  
Maurits F. J. M. Vissers ◽  
Jules A. A. C. Heuberger ◽  
Geert Jan Groeneveld
Keyword(s):  
Clinical Trials ◽  
Failure Rate ◽  
Neurodegenerative Disorders ◽  
Early Phase ◽  
Proof Of Concept ◽  
Mesh Terms ◽  
Disease Modifying ◽  
Early Phase Clinical Trials ◽  
Pharmacodynamic Biomarkers ◽  
Response Biomarkers

The clinical failure rate for disease-modifying treatments (DMTs) that slow or stop disease progression has been nearly 100% for the major neurodegenerative disorders (NDDs), with many compounds failing in expensive and time-consuming phase 2 and 3 trials for lack of efficacy. Here, we critically review the use of pharmacological and mechanistic biomarkers in early phase clinical trials of DMTs in NDDs, and propose a roadmap for providing early proof-of-concept to increase R&D productivity in this field of high unmet medical need. A literature search was performed on published early phase clinical trials aimed at the evaluation of NDD DMT compounds using MESH terms in PubMed. Publications were selected that reported an early phase clinical trial with NDD DMT compounds between 2010 and November 2020. Attention was given to the reported use of pharmacodynamic (mechanistic and physiological response) biomarkers. A total of 121 early phase clinical trials were identified, of which 89 trials (74%) incorporated one or multiple pharmacodynamic biomarkers. However, only 65 trials (54%) used mechanistic (target occupancy or activation) biomarkers to demonstrate target engagement in humans. The most important categories of early phase mechanistic and response biomarkers are discussed and a roadmap for incorporation of a robust biomarker strategy for early phase NDD DMT clinical trials is proposed. As our understanding of NDDs is improving, there is a rise in potentially disease-modifying treatments being brought to the clinic. Further increasing the rational use of mechanistic biomarkers in early phase trials for these (targeted) therapies can increase R&D productivity with a quick win/fast fail approach in an area that has seen a nearly 100% failure rate to date.

scholarly journals Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials

Cancers ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 2443
Author(s):  
Bethany Geary ◽  
Erin Peat ◽  
Sarah Dransfield ◽  
Natalie Cook ◽  
Fiona Thistlethwaite ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Trials ◽  
Early Phase ◽  
Characteristic Curve ◽  
Performance Status ◽  
Area Under The Curve ◽  
Protein Biomarkers ◽  
Late Stage Cancer ◽  
Early Phase Clinical Trials

TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient’s ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting.

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