Abstract MP23: Clinical and Demographic Factors Influence Clinical Trial Enrollment

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Daniel Suto ◽  
Emily Perrinez ◽  
Karen Rapp ◽  
Mohammed Nabulsi ◽  
Thomas M Hemmen
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Demographic Data ◽  
Clinical Trial Design ◽  
Stroke Severity ◽  
Chi Square ◽  
Stroke Risk Factor ◽  
Stroke Population ◽  
Hospital Administrative ◽  
Chi Square Analysis

Introduction: Inclusion of diverse patients in clinical trials is essential to represent patient populations and to ensure that results are generalizable. Analysis of this topic is motivated by prior reports of clinical trials underrepresenting historically disadvantaged groups. We aimed to understand if the clinical trial population is representative of the overall stroke population in our community. Methods: We obtained clinical and demographic data from hospital administrative records including all acute ischemic stroke patients between March 2019 and February 2020. Consent was counted when a signed consent for study participation was documented in the inpatient record. Enrollment in studies with a primary inpatient population at the time of consent (MOST, TIMELESS, ARCADIA, SleepSMART, and STRONG) were included. Patients with consent (CP) and without consent (NCP) were compared using chi-square analysis and t-test probability with SPSS. Results: During the study period, 504 patients met the above criteria; 55 were consented to participate in clinical trials. Overall CP did not differ from NCP in % women (45.5% vs 41.2%, not significant), Hispanic ethnicity, discharge medications, hypertension, diabetes, heart failure, drug and alcohol abuse, atrial fibrillation, and payer (Medicare versus non-Medicare). Median age was lower in CP compared to NCP (63 ± 12.8 vs. 67 ± 13.9, p=.046). The mean NIHSS was lower in CP (6.37 ± 5.95 vs. 8.21 ± 8.91, p=.048). The CP group had lower incidence of prior stroke (9.1% vs. 20.9%, p=.037), less dyslipidemia (12.7% vs. 30.1%, p=.007), fewer comorbidities on average (1.9 ± 1.1 vs. 2.2 ± 1.5, p=.044), was less likely to identify as Asian (0% vs. 8.4%, p=.015) and more likely to identify as white (59.3% vs. 45.7%, p=.045). The CP group was more likely to have migraine (12.7% vs. 1.8% p<.001) and were discharged on more stroke risk-factor modifying medications (2.2 ± .558 vs. 1.9 ± .902, p=.001). Conclusions: While the study population is largely representative, they are more likely to be White, with lower stroke severity, fewer medical comorbidities and less likely to be Asian. We plan to expand this analysis to include more study centers and to guide future clinical trial design.

Comparison of the demographics of patients enrolled (E) versus those not enrolled (NE) on therapeutic clinical trials at a comprehensive cancer center

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 17065-17065
Author(s):  
S. Goodin ◽  
D. C. Vamos ◽  
M. P. Kane ◽  
J. Nishioka ◽  
S. Lisi ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Demographic Data ◽  
Private Insurance ◽  
Univariate Analysis ◽  
The Elderly ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Chi Square ◽  
Enrollment Rates

17065 Background: In the U.S., representation of minorities and the elderly in clinical trials has been low yet few reports have evaluated this potential barrier to enrollment by comparing the demographics of patients E vs NE within an institution. Therefore, we compared these groups to determine if there were significant differences in demographics at our center. Methods: For all E patients, demographic data is collected in a clinical trial database. For evaluated NE patients, data was captured through a ‘non-protocol’ form. A univariate analysis was performed on the demographic data, including gender, age, race, and insurance status, for each year to determine if there were differences in patients E vs NE on a therapeutic clinical trial. Results: From June 2003 through December 2005, there were 912 E patients and data available on 474 NE patients. The results were consistent for each year from 2003 to 2005, and therefore combinable, with no statistical difference in any parameter for E patients versus NE patients during any year with the exception of gender (p=0.05; Chi-square). The distribution of patients E by gender is 52% (474/912) female vs 48% (438/912) male and NE is 69% (325/474) female vs 31% (149/474) male. The mean age of E patients was 55 vs 56 years for NE patients, with 32% vs 33% representing those >65years, respectively. For the E patients, 84% were white, 7.2% black, 4.6% Asian, 4.2% unknown, and 0.4% Hawaiian/Pacific Islander (H/PI). For the NE patients, where race was not consistently available, 65% were white, 9.3% black, 3.2% Asian, 20.5% unknown, and 2.1% H/PI. In both groups, most patients had private insurance (E 60%, NE 54%), followed by Medicare (E 27.5%, NE 29%), Medicaid (E 4%, NE 9%), self pay (E 7.5%, NE 7.4%), and unknown (E 1.3%, NE 0.4%). Conclusions: When comparing E vs NE patients, gender was the only factor that differed significantly. Although this result suggests that males were more likely to be E in a clinical trial, this finding should be interpreted with caution, since this difference might relate to differences in trial availability. While lower enrollment rates for the elderly and minority patients have been identified nationally, enrolling this group of patients does not appear to be a barrier at our center. No significant financial relationships to disclose.

Barriers to patient accrual in Ontario oncology clinical trials: Attitudes of oncologists and clinical research personnel.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 36-36
Author(s):  
Ammar Bookwala ◽  
Daisy Dastur ◽  
Audrey Wong ◽  
Christina Marchand ◽  
Jalal Ebrahim ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Research ◽  
Trial Design ◽  
Demographic Data ◽  
Clinical Trial Design ◽  
Standard Of Care ◽  
Medical Specialty ◽  
Research Personnel ◽  
Patient Accrual

36 Background: The medical specialty of oncology relies heavily on clinical trials to advance policies and practices related to cancer care. However, oncology clinical trial accrual in Ontario has dropped from 12.4% in 2007, to 8.5% in 2009. The objective of this study was to determine barriers experienced by Oncologists and Clinical Research Personnel (CRP) in recruiting patients to oncology trials in Ontario. Methods: In June 2012, an electronic survey was emailed to about 400 oncologists and CRP across Ontario. Variables of interest included demographic data, clinical trial involvement, and perceived barriers to participation in clinical trials amongst three previously identified barrier domains. Barriers were ranked, from 1 (least significant) to 5 (most significant). Statistics were compiled using Graphpad Prism software. Differences in responses were analyzed using the Kruskal – Wallis test and Dunn’s Multiple Comparison Test. Results: Of the 400 emails sent, there were 126 respondents (32%). Of the 126 respondents, 82 fully completed the survey (64.6% useable response rate). Amongst system related barriers, “time related” (Median Agreement (M): 4, Inter Quartile Range (IQR): 3-5), and “resource related” barriers (M: 4, IQR: 3-5) had the most negative effect on accrual (p<0.05). Amongst trial design barriers, “Relevance to patient population” (M: 3, IQR: 3-5), “Deviation from Standard of Care” (M: 3, IQR: 3-5) and “Complexity of Trial Protocol” (M: 4, IQR: 3-5) were the most significant barriers (p<0.05). Lastly, amongst personal barriers, “Commitment of the Principal Investigator/Research Staff” (M: 4, IQR: 3-5) and Drug Safety (M: 4, IQR: 2-4) were the most significant barriers to recruitment (p<0.05). Conclusions: Multiple barriers were identified as having a significant impact on patient accrual in clinical trials. Addressing these barriers prospectively in clinical trial design may benefit future studies to successfully accrue cancer patients. Also, creating clinical trial collaboration vehicles amongst sites in similar geographical areas may contribute to improving patient accrual to clinical trials.

Digital Technology in Clinical Trials for Multiple Sclerosis: a systematic review. (Preprint)

2020 ◽  
Author(s):  
Marcello De Angelis ◽  
Luigi Lavorgna ◽  
Antonio Carotenuto ◽  
Martina Petruzzo ◽  
Roberta Lanzillo ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trial ◽  
Clinical Trials ◽  
Outcome Measures ◽  
Digital Technology ◽  
Clinical Trial Design ◽  
Motor Rehabilitation ◽  
Robot Assisted ◽  
Future Directions ◽  
Treatment Side Effects

BACKGROUND Clinical trials in multiple sclerosis (MS) have leveraged the use of digital technology to overcome limitations in treatment and disease monitoring. OBJECTIVE To review the use of digital technology in concluded and ongoing MS clinical trials. METHODS In March 2020, we searched for “multiple sclerosis” and “trial” on pubmed.gov and clinicaltrials.gov using “app”, “digital”, “electronic”, “internet” and “mobile” as additional search words, separately. Overall, we included thirty-five studies. RESULTS Digital technology is part of clinical trial interventions to deliver psychotherapy and motor rehabilitation, with exergames, e-training, and robot-assisted exercises. Also, digital technology has become increasingly used to standardise previously existing outcome measures, with automatic acquisitions, reduced inconsistencies, and improved detection of symptoms. Some trials have been developing new patient-centred outcome measures for the detection of symptoms and of treatment side effects and adherence. CONCLUSIONS We will discuss how digital technology has been changing MS clinical trial design, and possible future directions for MS and neurology research.

scholarly journals “Include me if you can”—reasons for low enrollment of pediatric patients in a psychopharmacological trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Larissa Niemeyer ◽  
Konstantin Mechler ◽  
Jan Buitelaar ◽  
Sarah Durston ◽  
Bram Gooskens ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Demographic Data ◽  
Autism Spectrum ◽  
Current Therapy ◽  
Future Research ◽  
Controlled Clinical Trial ◽  
Double Blind ◽  
Clinical Patient ◽  
Compulsive Disorder

Abstract Background Low recruitment in clinical trials is a common and costly problem which undermines medical research. This study aimed to investigate the challenges faced in recruiting children and adolescents with obsessive-compulsive disorder and autism spectrum disorder for a randomized, double-blind, placebo-controlled clinical trial and to analyze reasons for non-participation. The trial was part of the EU FP7 project TACTICS (Translational Adolescent and Childhood Therapeutic Interventions in Compulsive Syndromes). Methods Demographic data on pre-screening patients were collected systematically, including documented reasons for non-participation. Findings were grouped according to content, and descriptive statistical analyses of the data were performed. Results In total, n = 173 patients were pre-screened for potential participation in the clinical trial. Of these, only five (2.9%) were eventually enrolled. The main reasons for non-inclusion were as follows: failure to meet all inclusion criteria/meeting one or more of the exclusion criteria (n = 73; 42.2%), no interest in the trial or trials in general (n = 40; 23.1%), and not wanting changes to current therapy/medication (n = 14; 8.1%). Conclusions The findings from this study add valuable information to the existing knowledge on reasons for low clinical trial recruitment rates in pediatric psychiatric populations. Low enrollment and high exclusion rates raise the question of whether such selective study populations are representative of clinical patient cohorts. Consequently, the generalizability of the results of such trials may be limited. The present findings will be useful in the development of improved recruitment strategies and may guide future research in establishing the measurement of representativeness to ensure enhanced external validity in psychopharmacological clinical trials in pediatric populations. Trial registration EudraCT 2014-003080-38. Registered on 14 July 2014.

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.

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