Cancer

2021 ◽  
pp. 627-638
Author(s):  
Rashi Rai ◽  
Prudhvilal Bhukya ◽  
Muneesh Kumar Barman ◽  
Meenakshi Singh ◽  
Kailash Chand ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Success Rates ◽  
Design Strategies ◽  
Safety Level ◽  
Key Steps ◽  
The Impact

Clinical trials are essential to govern the impact of a new possible treatment. It is utilized to determine the safety level and efficacy of a certain treatment. Clinical trial studies in cancer have provided successful treatment leading to longer survival span in the patients. The design of clinical trials for cancer has been done to find new ways to prevent, diagnose, treat, and manage symptoms of the disease. This chapter will provide detailed information on different aspects of clinical trials in cancer research. Protocols outlining the design and method to conduct a clinical trial in each phase will be discussed. The process and the conditions applied in each phase (I, II, and III) will be described precisely. The design of trials done in every aspect such as prevention, immunochemotherapy, diagnosis, and treatment to combat cancer will be illustrated. Also, recent innovations in clinical design strategies and principles behind it as well as the use of recent advances in artificial intelligence in reshaping key steps of clinical trial design to increase trial success rates.

scholarly journals Interpretable Trials: Is Interpretability a Reason Why Clinical Trials Fail?

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi-Sheng Chao ◽  
Chao-Jung Wu ◽  
Hsing-Chien Wu ◽  
Danielle McGolrick ◽  
Wei-Chih Chen
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Research Method ◽  
Clinical Trial Design ◽  
Independent Variables ◽  
Composite Measures ◽  
Unknown Status ◽  
The Impact ◽  
Trial Failure

Background: There are clinical trials using composite measures, indices, or scales as proxy for independent variables or outcomes. Interpretability of derived measures may not be satisfying. Adopting indices of poor interpretability in clinical trials may lead to trial failure. This study aims to understand the impact of using indices of different interpretability in clinical trials.Methods: The interpretability of indices was categorized as: fair-to-poor, good, and unknown. In the literature, frailty indices were considered fair to poor interpretability. Body mass index (BMI) was highly interpretable. The other indices were of unknown interpretability. The trials were searched at clinicaltrials.gov on October 2, 2018. The use of indices as conditions/diseases or other terms was searched. The trials were grouped as completed, terminated, active, and other status. We tabulated the frequencies of frailty, BMI, and other indices.Results: There were 263,928 clinical trials found and 155,606 were completed or terminated. Among 2,115 trials adopting indices or composite measures as condition or disease, 244 adopted frailty and 487 used BMI without frailty indices. Significantly higher proportions of trials of unknown status used indices as conditions/diseases or other terms, compared to completed and terminated trials. The proportions of active trials using frailty indices were significantly higher than those of completed or terminated trials.Discussion: Clinical trial databases can be used to understand why trials may fail. Based on the findings, we suspect that using indices of poor interpretability may be associated with trial failure. Interpretability has not been conceived as an essential criterion for outcomes or proxy measures in trials. We will continue verifying the findings in other databases or data sources and apply this research method to improve clinical trial design. To prevent patients from experiencing trials likely to fail, we suggest further examining the interpretability of the indices in trials.

scholarly journals Conceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow

2019 ◽  
pp. 1-10 ◽  
Author(s):  
Neha M. Jain ◽  
Alison Culley ◽  
Teresa Knoop ◽  
Christine Micheel ◽  
Travis Osterman ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Knowledge Representation ◽  
Conceptual Framework ◽  
Trial Design ◽  
Clinical Trial Design ◽  
Prospective Clinical Trial ◽  
Future Trends ◽  
Current State ◽  
Evaluation Strategies

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

scholarly journals Edaravone in Amyotrophic Lateral Sclerosis’Lessons from the Clinical Development Program and the Importance of a Strategic Clinical Trial Design

US Neurology ◽  
2018 ◽  
Vol 14 (1) ◽  
pp. 47 ◽  
Author(s):  
Said R Beydoun ◽  
Jeffrey Rosenfeld
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Amyotrophic Lateral Sclerosis ◽  
Trial Design ◽  
Development Program ◽  
Clinical Trial Design ◽  
Clinical Development ◽  
Disease Heterogeneity ◽  
Clinical Development Program ◽  
Lateral Sclerosis

Edaravone significantly slows progression of amyotrophic lateral sclerosis (ALS), and is the first therapy to receive approval by the Food and Drug Administration (FDA) for the disease in 22 years. Approval of edaravone has marked a new chapter in pharmaceutical development since the key trial included a novel strategic clinical design involving cohort enrichment. In addition, approval was based on clinical trials that had a relatively small patient number and were performed outside of the US. Edaravone was developed through a series of clinical trials in Japan where it was determined that a well-defined subgroup of patients was required to reveal a treatment effect within the study period. Amyotrophic lateral sclerosis is associated with wide-ranging disease heterogeneity (both within the spectrum of ALS phenotypes as well as in the rate of progression). The patient cohort enrichment strategy aimed to address this heterogeneity and should now be considered as a viable, and perhaps preferred, trial design for future studies. Future research incorporating relevant biomarkers may help to better elucidate edaravone’s mechanism of action, pharmacodynamics, and subsequently ALS phenotypes that may preferentially benefit from treatment. In this review, we discuss the edaravone clinical development program, outline the strategic clinical trial design, and highlight important lessons for future trials.

Clinical Trial Design and Statistics

2018 ◽  
Author(s):  
Julie Ann Sosa
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Data Analysis ◽  
Trial Design ◽  
New Technologies ◽  
Drug Application ◽  
Clinical Trial Design ◽  
Double Blind Study ◽  
Type I ◽  
End Points

A clinical trial is a planned experiment designed to prospectively measure the efficacy or effectiveness of an intervention by comparing outcomes in a group of subjects treated with the test intervention with those observed in one or more comparable group(s) of subjects receiving another intervention.  Historically, the gold standard for a clinical trial has been a prospective, randomized, double-blind study, but it is sometimes impractical or unethical to conduct such in clinical medicine and surgery. Conventional outcomes have traditionally been clinical end points; with the rise of new technologies, however, they are increasingly being supplemented and/or replaced by surrogate end points, such as serum biomarkers. Because patients are involved, safety considerations and ethical principles must be incorporated into all phases of clinical trial design, conduct, data analysis, and presentation. This review covers the history of clinical trials, clinical trial phases, ethical issues, implementing the study, basic biostatistics for data analysis, and other resources. Figures show drug development and clinical trial process, and type I and II error. Tables list Food and Drug Administration new drug application types, and types of missing data in clinical trials. This review contains 2 highly rendered figures, 2 tables, and 38 references

A novel, hybrid, single- and multi-site clinical trial design for CLN3 disease, an ultra-rare lysosomal storage disorder

2019 ◽  
Vol 16 (5) ◽  
pp. 555-560 ◽  
Author(s):  
Heather R Adams ◽  
Sara Defendorf ◽  
Amy Vierhile ◽  
Jonathan W Mink ◽  
Frederick J Marshall ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Rare Diseases ◽  
Trial Design ◽  
Neurodegenerative Disorder ◽  
Clinical Trial Design ◽  
Trial Participation ◽  
Local Participation ◽  
Cln3 Disease ◽  
Study Participants

Background Travel burden often substantially limits the ability of individuals to participate in clinical trials. Wide geographic dispersion of individuals with rare diseases poses an additional key challenge in the conduct of clinical trials for rare diseases. Novel technologies and methods can improve access to research by connecting participants in their homes and local communities to a distant research site. For clinical trials, however, understanding of factors important for transition from traditional multi-center trial models to local participation models is limited. We sought to test a novel, hybrid, single- and multi-site clinical trial design in the context of a trial for Juvenile Neuronal Ceroid Lipofuscinosis (CLN3 disease), a very rare pediatric neurodegenerative disorder. Methods We created a “hub and spoke” model for implementing a 22-week crossover clinical trial of mycophenolate compared with placebo, with two 8-week study arms. A single central site, the “hub,” conducted screening, consent, drug dispensing, and tolerability and efficacy assessments. Each participant identified a clinician to serve as a collaborating “spoke” site to perform local safety monitoring. Study participants traveled to the hub at the beginning and end of each study arm, and to their individual spoke site in the intervening weeks. Results A total of 18 spoke sites were established for 19 enrolled study participants. One potential participant was unable to identify a collaborating local site and was thus unable to participate. Study start-up required a median 6.7 months (interquartile range = 4.6–9.2 months). Only 33.3% (n = 6 of 18) of spoke site investigators had prior clinical trial experience, thus close collaboration with respect to study startup, training, and oversight was an important requirement. All but one participant completed all study visits; no study visits were missed due to travel requirements. Conclusions This study represents a step toward local trial participation for patients with rare diseases. Even in the context of close oversight, local participation models may be best suited for studies of compounds with well-understood side-effect profiles, for those with straightforward modes of administration, or for studies requiring extended follow-up periods.

scholarly journals Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension

BMJ ◽  
2020 ◽  
pp. m3164 ◽  
Author(s):  
Xiaoxuan Liu ◽  
Samantha Cruz Rivera ◽  
David Moher ◽  
Melanie J Calvert ◽  
Alastair K Denniston
Keyword(s):  
Artificial Intelligence ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Trial Design ◽  
Trial Protocols ◽  
General Readership ◽  
Peer Reviewers ◽  
Multi Stakeholder ◽  
Analysis Of Error

Abstract The CONSORT 2010 (Consolidated Standards of Reporting Trials) statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency when evaluating new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI. Both guidelines were developed through a staged consensus process, involving a literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed on in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items, which were considered sufficiently important for AI interventions, that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and providing analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer-reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

R&D Spending & Success: Key Trends, Issues & Solutions

2017 ◽  
Vol 23 (1) ◽  
Author(s):  
Sanjay Kumar Rao
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Trial Design ◽  
Product Launch ◽  
Financial Resources ◽  
Success Rates ◽  
Trial Testing ◽  
Recent Trends ◽  
Pharmaceutical Prices ◽  
Design And Testing

Debates about rising bio/pharmaceutical prices are often predicated on the presumption that higher product prices are necessary for continuing funding of innovation. Recent trends only partially bear this out. While total spending on bio/pharmaceutical R&D has largely remained constant, costs for clinical trials have consumed a rising proportion of R&D budgets. More spending on clinical trials, however, has not yielded higher trial success rates. Prior to product launch, clinical trials take up most of the financial resources attributed to a product in development, costing on average $75-$100M. While completing a full cycle of pre-clinical and clinical trials can take an average of 7.5 years, the probability of successful filing for marketing a drug after clinical trial testing has never exceeded 13-20%, despite advances in trial design and testing processes.This article discusses key trends in R&D spending and productivity. It then lays out issues that prevent clinical trials from achieving higher success, and presents operational and strategic solutions that can be implemented to improve the effectiveness of increases in R&D spending.

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