scholarly journals Novel Clinical Trial Designs to Improve the Efficiency of Research

2020 ◽  
Vol 132 (1) ◽  
pp. 69-81 ◽  
Author(s):  
Daniel I. Sessler ◽  
Paul S. Myles
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Real Time ◽  
Randomized Trials ◽  
Clinical Evidence ◽  
Adaptive Designs ◽  
Large Numbers ◽  
Clinical Trial Designs ◽  
Study Sites ◽  
Trial Efficiency

Abstract SUMMARY Large randomized trials provide the highest level of clinical evidence. However, enrolling large numbers of randomized patients across numerous study sites is expensive and often takes years. There will never be enough conventional clinical trials to address the important questions in medicine. Efficient alternatives to conventional randomized trials that preserve protections against bias and confounding are thus of considerable interest. A common feature of novel trial designs is that they are pragmatic and facilitate enrollment of large numbers of patients at modest cost. This article presents trial designs including cluster designs, real-time automated enrollment, and practitioner-preference approaches. Then various adaptive designs that improve trial efficiency are presented. And finally, the article discusses the advantages of embedding randomized trials within registries.

scholarly journals Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Thomas Burnett ◽  
Pavel Mozgunov ◽  
Philip Pallmann ◽  
Sofia S. Villar ◽  
Graham M. Wheeler ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Adaptive Designs ◽  
Phase Iii ◽  
Scientific Interest ◽  
Clinical Community ◽  
Adaptive Trials ◽  
Clinical Trial Designs ◽  
Phase Iii Studies ◽  
Confirmatory Phase

AbstractAdaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.

Current Advances in Clinical Trials for Rare Disease Populations: Spotlight on the Patient

2021 ◽  
Vol 16 ◽  
Author(s):  
Erica Winter ◽  
Scott Schliebner
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Drug Development ◽  
Rare Disease ◽  
Rare Diseases ◽  
Statistical Analyses ◽  
Clinical Trial Designs ◽  
Novel Approaches

: Characterized by small, highly heterogeneous patient populations, rare disease trials magnify the challenges often encountered in traditional clinical trials. In recent years, there have been increased efforts by stakeholders to improve drug development in rare diseases through novel approaches to clinical trial designs and statistical analyses. We highlight and discuss some of the current and emerging approaches aimed at overcoming challenges in rare disease clinical trials, with a focus on the ultimate stakeholder, the patient.

Clinical trials

2015 ◽  
Author(s):  
Richard Haynes ◽  
Martin J. Landray ◽  
William G. Herrington ◽  
Colin Baigent
Keyword(s):  
Clinical Trials ◽  
Clinical Practice ◽  
Large Scale ◽  
Randomized Trials ◽  
Evidence Base ◽  
Relevant Study ◽  
Large Numbers ◽  
Trial Outcomes ◽  
Clinical Questions ◽  
The Many

Randomized trials are the best method for identifying and quantifying the benefits and risks of interventions in clinical practice. Nephrology lags behind most specialties in medicine in its evidence base. Many commonly used therapies are untested and may be ineffective or even cause harm. For trials to provide reliable answers to important clinical questions they must first avoid two sources of error. Firstly, systematic error (or bias) can only be removed by proper randomization. Secondly, random error (the play of chance) can only be removed by the randomization of large numbers of patients (and therefore the accrual of large numbers of trial outcomes). Following successful large-scale randomization, it is critical that patients’ compliance with their allocated treatment is maintained, relevant study outcomes are systematically ascertained, and appropriate statistical analyses are performed. There is an urgent need to conduct such trials to address the many important clinical questions in nephrology.

Highly efficient clinical trial designs for reliable screening of under-performing treatments: Application to the COVID-19 Pandemic

2020 ◽  
Vol 17 (5) ◽  
pp. 483-490
Author(s):  
Steven Piantadosi
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Sample Size ◽  
Trial Design ◽  
Ranking And Selection ◽  
Design Elements ◽  
Highly Efficient ◽  
Clinical Trial Designs ◽  
Under Performing ◽  
The Many

Background: The COVID-19 pandemic presents challenges for clinical trials including urgency, disrupted infrastructure, numerous therapeutic candidates, and the need for highly efficient trial and development designs. This paper presents design components and rationale for constructing highly efficient trials to screen potential COVID-19 treatments. Methods: Key trial design elements useful in this circumstance include futility hypotheses, treatment pooling, reciprocal controls, ranking and selection, and platform administration. Assuming most of the many candidates for COVID-19 treatment are likely to be ineffective, these components can be combined to facilitate very efficient comparisons of treatments. Results: Simulations indicate such designs can reliably discard underperforming treatments using sample size to treatment ratios under 30. Conclusions: Methods to create very efficient clinical trial comparisons of treatments for COVID-19 are available. Such designs might be helpful in the pandemic and should be considered for similar needs in the future.

scholarly journals Evolution of clinical trials in multiple sclerosis

2019 ◽  
Vol 12 ◽  
pp. 175628641982654 ◽  
Author(s):  
Yinan Zhang ◽  
Amber Salter ◽  
Erik Wallström ◽  
Gary Cutter ◽  
Olaf Stüve
Keyword(s):  
Multiple Sclerosis ◽  
Clinical Trials ◽  
Treatment Options ◽  
Adaptive Designs ◽  
Resonance Imaging ◽  
Disease Modifying Therapies ◽  
Relapsing Remitting ◽  
Trial Efficiency ◽  
The Cost ◽  
Larger Sample

Clinical trials have advanced the treatment of multiple sclerosis (MS) by demonstrating the safety and efficacy of disease-modifying therapies (DMTs). This review discusses major changes to MS clinical trials in the era of DMTs. As treatment options for MS continue to increase, patients in modern MS trials present earlier and with milder disease compared with historic MS populations. While placebo-controlled trials for some questions may still be relevant, DMT trials in relapsing–remitting MS (RRMS) are no longer ethical. The replacement of the placebo arm by an active comparator arm in trials have raised the cost of trials by requiring larger sample sizes to detect on-study changes in treatment effects. Efforts to improve trial efficiency in RRMS have focused on exploring adaptive designs and relying on sensitive magnetic resonance imaging measures of disease activity. In trials for progressive forms of MS (PMS), the lack of sensitive outcome measures that can be used in shorter-term trials have delayed the development of effective treatments. Recent shifting of the focus to advancing trials in PMS has identified paraclinical outcome measurements with improved potential, and the testing of agents for neuroprotection and remyelination is in progress.

Patients Driving the Clinical Trial Designs – Democracy in Clinical Research

2019 ◽  
Vol 14 (4) ◽  
pp. 237-246
Author(s):  
Payal Bhardwaj ◽  
Jeba Kumar ◽  
Raj Kumar Yadav
Keyword(s):  
Clinical Trial ◽  
Clinical Trials ◽  
Clinical Research ◽  
Retention Rate ◽  
Patient Centered ◽  
Regulatory Review ◽  
Patient Organizations ◽  
Patient Reported ◽  
Clinical Trial Designs ◽  
The Right

Background: Many of the clinical trials remain inefficient owing to the low retention rate, and an impact on the power of the study. In addition, regulatory bodies recommend including the patients’ experience, especially, patient-reported outcomes, while making clinical decisions, and approvals. Introduction: Patient centricity has reached the stage where patients are both willing and required to participate in clinical trial designs, regulatory review and experts on other panels. Efforts are being made in the right direction and there are multiple aspects that have been or are being addressed. Objective: The current article focuses on how to include patients in clinical trial designs, the benefits, challenges, and solutions. This means patients who were merely the participants until now, they will be the drivers of trials now, and hence the clinical trials will be more efficient and productive. Key Findings: There is a drive to enhance patients’ participation in clinical trial designs, especially, visits, efficacy outcomes and their expectations with the treatment. Patients want to remain informed, right from before participation to the completion of the trial. Patients are now an important part of regulatory review, as apparent from recent initiatives by the FDA and EMA. This will enhance patients’ awareness, and bring ownership and transparency. Various patient organizations, advocacy groups have made some great suggestions and taken initiatives in this direction. Clinical Trials Transformation Initiative, European Patient’s Academy on Therapeutic Innovation, and Patient- Centered Outcomes Research Institute are a few key initiatives. However, there is a set of challenges emanating from the complexity of trials, associated with unique mechanism of action of drugs, their efficacy and safety profiles, which has to be dealt with properly. Conclusion: Overall, the pharma domain is at the verge of putting the patient in the spotlight, to achieve a near-real democracy, where the clinical research is the by the patient, for the patient, and, of the patient.

A Bayesian Hybrid Adaptive Randomisation Design for Clinical Trials with Survival Outcomes

2016 ◽  
Vol 55 (01) ◽  
pp. 4-13
Author(s):  
M. Moatti ◽  
S. Zohar ◽  
W. F. Rosenberger ◽  
S. Chevret
Keyword(s):  
Clinical Trial ◽  
Multiple Myeloma ◽  
Clinical Trials ◽  
Adaptive Design ◽  
Hazard Ratio ◽  
Adaptive Designs ◽  
Stopping Rules ◽  
Phase Iii ◽  
Optimal Criterion ◽  
Fully Bayesian

SummaryBackground: Response-adaptive randomisation designs have been proposed to im -prove the efficiency of phase III randomised clinical trials and improve the outcomes of the clinical trial population. In the setting of failure time outcomes, Zhang and Rosen -berger (2007) developed a response-adaptive randomisation approach that targets an optimal allocation, based on a fixed sample size. Objectives: The aim of this research is to propose a response-adaptive randomisation procedure for survival trials with an interim monitoring plan, based on the following optimal criterion: for fixed variance of the esti -mated log hazard ratio, what allocation minimizes the expected hazard of failure? We demonstrate the utility of the design by re -designing a clinical trial on multiple myeloma. Methods: To handle continuous monitoring of data, we propose a Bayesian response-adap -tive randomisation procedure, where the log hazard ratio is the effect measure of interest. Combining the prior with the normal likelihood, the mean posterior estimate of the log hazard ratio allows derivation of the optimal target allocation. We perform a simu lationstudy to assess and compare the perform -ance of this proposed Bayesian hybrid adaptive design to those of fixed, sequential or adaptive – either frequentist or fully Bayesian – designs. Non informative normal priors of the log hazard ratio were used, as well as mixture of enthusiastic and skeptical priors. Stopping rules based on the posterior dis -tribution of the log hazard ratio were com -puted. The method is then illus trated by redesigning a phase III randomised clinical trial of chemotherapy in patients with multiple myeloma, with mixture of normal priors elicited from experts. Results: As expected, there was a reduction in the proportion of observed deaths in the adaptive vs. non-adaptive designs; this reduction was maximized using a Bayes mix -ture prior, with no clear-cut improvement by using a fully Bayesian procedure. The use of stopping rules allows a slight decrease in the observed proportion of deaths under the alternate hypothesis compared with the adaptive designs with no stopping rules. Conclusions: Such Bayesian hybrid adaptive survival trials may be promising alternatives to traditional designs, reducing the duration of survival trials, as well as optimizing the ethical concerns for patients enrolled in the trial.

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