Is the future for clinical trials internet-based? A cluster randomized clinical trial

2005 ◽  
Vol 2 (1) ◽  
pp. 72-79 ◽  
Author(s):  
Jennifer Litchfield ◽  
Jenny Freeman ◽  
Henrik Schou ◽  
Mark Elsley ◽  
Robert Fuller ◽  
...  
2021 ◽  
pp. 106519
Author(s):  
Barbara C. Tilley ◽  
Arch G. Mainous ◽  
Rossybelle P. Amorrortu ◽  
M. Diane McKee ◽  
Daniel W. Smith ◽  
...  

2021 ◽  
Vol 4 (4) ◽  
pp. 613-616
Author(s):  
Dun-Xian Tan ◽  
Russel J Reiter

SARS-CoV-2 has ravaged the population of the world for two years. Scientists have not yet identified an effective therapy to reduce the mortality of severe COVID-19 patients. In a single-center, open-label, randomized clinical trial, it was observed that melatonin treatment lowered the mortality rate by 93% in severely-infected COVID-19 patients compared with the control group (see below). This is seemingly the first report to show such a huge mortality reduction in severe COVID-19 infected individuals with a simple treatment. If this observation is confirmed by more rigorous clinical trials, melatonin could become an important weapon to combat this pandemic.


2002 ◽  
Vol 57 (2) ◽  
pp. 83-88 ◽  
Author(s):  
Edson Duarte Moreira ◽  
Ezra Susser

In observational studies, identification of associations within particular subgroups is the usual method of investigation. As an exploratory method, it is the bread and butter of epidemiological research. Nearly everything that has been learned in epidemiology has been derived from the analysis of subgroups. In a randomized clinical trial, the entire purpose is the comparison of the test subjects and the controls, and when there is particular interest in the results of treatment in a certain section of trial participants, a subgroup analysis is performed. These subgroups are examined to see if they are liable to a greater benefit or risk from treatment. Thus, analyzing patient subsets is a natural part of the process of improving therapeutic knowledge through clinical trials. Nevertheless, the reliability of subgroup analysis can often be poor because of problems of multiplicity and limitations in the numbers of patients studied. The naive interpretation of the results of such examinations is a cause of great confusion in the therapeutic literature. We emphasize the need for readers to be aware that inferences based on comparisons between subgroups in randomized clinical trials should be approached more cautiously than those based on the main comparison. That is, subgroup analysis results derived from a sound clinical trial are not necessarily valid; one must not jump to conclusions and accept the validity of subgroup analysis results without an appropriate judgment.


PEDIATRICS ◽  
1985 ◽  
Vol 76 (4) ◽  
pp. 622-623
Author(s):  
NIGEL PANETH ◽  
SYLVAN WALLENSTEIN

The therapeutic trial comparing extracorporeal membrane oxygenation with conventional treatment in neonatal respiratory failure reported by Bartlett et al (Pediatrics 1985;76:479-487) uses a method of comparing treatments unlikely to be familiar to most pediatricians. Known as the "randomized play the winner" method, it has thus far been little used in clinical research. Most clinical investigators consider the conventional randomized clinical trial to be the last word in treatment comparisons. But randomized clinical trials are costly, cumbersome, and to some observers less than ideal ethically. The ethical problem arises from the fact that during a "successful" randomized clinical trial (ie, one that demonstrates a significant advantage to one treatment) about half of the trial subjects will receive a treatment which, at the end of the trial, will be known to be inferior.


2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1527-1527
Author(s):  
Waqas Haque ◽  
Ann M. Geiger ◽  
Celette Sugg Skinner ◽  
Rasmi Nair ◽  
Simon Craddock Lee ◽  
...  

1527 Background: Patient accrual for cancer clinical trials is suboptimal. The complexity of applying eligibility criteria and enrolling patients may deter oncologists from recommending patients for a trial. As such, there is a need to understand how experience, training, and clinical decision support impact physician practices and intentions related to trial accrual. Methods: From May to September 2017, we conducted a survey on clinical trial accrual in a national sample of medical, surgical, and radiation oncologists. The 20-minute survey assessed barriers and facilitators to clinical trial accrual, including experience (e.g., “In the past 5 years, have you been a study or site PI of a trial?”), training (e.g., “Did you receive training about trial design and recruitment as part of medical school, residency, or fellowship? After fellowship?”), and clinical decision support (e.g., “What kind of clinical decision support has your practice implemented?). We used logistic regression to identify factors associated with frequency of discussing trials (with ≥25% of patients) and likelihood of recommending a trial to a patient (likely or very likely) in the future. Results: Survey respondents (n = 1,030) were mostly medical oncologists (59%), age 35-54 years (67%), male (74%), and not in academic practice (58%). About 18% of respondents (n = 183) reported discussing trials with ≥25% of their patients, and 80% reported being likely or very likely to recommend a trial to a patient in the future. Prior experience as principal investigator of a trial was associated with both frequency of discussing trials (OR 3.27, 95% CI 2.25, 4.75) and likelihood of recommending a trial in the future (OR 5.22, 95% CI 3.71, 7.34), as was receiving additional training in clinical trials after fellowship (discussion with patients: OR 2.48, 95% CI 1.80, 3.42; recommend in future: OR 1.92, 95% CI 1.37, 2.69). Implementing clinical decision support was not associated with discussing trials with ≥25% of patients (OR 1.12, 95% CI 0.76, 1.67), but was associated with being likely to recommend a trial in the future (OR 1.73, 95% CI 1.11, 2.71). Conclusions: In a national survey of oncologists, we observed differences in physician practices and intention related to clinical trial accrual. Whereas the vast majority (80%) reported being likely or very likely to recommend trials in the future, far fewer (20%) reported discussing trials with their patients within the past 5 years. Implementation of clinical decision support – electronic tools intended to optimize patient care and identification of patient eligibility – was not associated with frequency of past discussion of clinical trials but was associated with recommending a trial in the future. Given the stronger association between experience as a site Principal Investigator and recommending a trial, future research should explore how improving opportunities to lead a clinical trial impact trial accrual.


2011 ◽  
pp. 1738-1758
Author(s):  
Tillal Eldabi ◽  
Robert D. Macredie ◽  
Ray J. Paul

This chapter reports on the use of simulation in supporting decision-making about what data to collect in a randomized clinical trial (RCT). We show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” Healthcare systems pose many of the challenges, including difficulty in understanding the system being studied, uncertainty over which data to collect, and problems of communication between problem owners. In this chapter we show how simulation also allows the identification of critical variables in the RCT by measuring their effects on the simulation model’s “behavior.” The experience of developing the simulation model leads us to suggest simple but extremely valuable lessons. The first relates to the inclusion of stakeholders in the modeling process and the accessibility of the resulting models. The ownership and confidence felt by stakeholders in our case is, we feel, extremely important and may provide an example to others developing models.


Sign in / Sign up

Export Citation Format

Share Document