Traditional Randomized Controlled Trials Research Articles

Nested Randomized Controlled Trials in Large Databases: An Opportunity for Inflammatory Bowel Disease?

Although randomized controlled trials (RCTs) are the gold standard for investigating the efficacy and safety of interventions, they present major operational challenges due to their complexity, time-consuming nature, and high costs. To address some of these difficulties, RCTs nested in cohorts (RCTsNC) have been developed to enable patient enrolment and randomization from existing databases. RCTsNC is an emerging trial design, which has been successfully utilized across several medical disciplines but not inflammatory bowel disease (IBD). This narrative review outlines the principles of RCTsNC and discusses the numerous advantages it affords for IBD, including harnessing longer-term longitudinal data for safety and efficacy assessment, and enhanced recruitment and follow up processes. Leveraging pre-existing cohorts and their organizational structures improves patient acceptance and is more economical compared to traditional randomized trials. Observational data for IBD, derived from research (cohort and case-control studies) and non-research sources (electronic health records and registries), provides access to comprehensive records for a large number of IBD patients. It permits researchers to address knowledge gaps in IBD where traditional RCTs have had a limited role, such as specific sub-populations typically excluded from pivotal trials, or assessing the effect of environmental exposures on disease course. This review also details caveats of this study design that include the risk of selection bias and constraints related to comparisons with placebo. In conclusion, RCTsNC offers a promising opportunity IBD research given the challenges of the current IBD RCT landscape.

Virtual Clinical Trials - Implications of Computer Simulations and Artificial Intelligence for Musculoskeletal Research.

In silico clinical trials, particularly when augmented with artificial intelligence methods, represent an innovative approach with much to offer, particularly in the musculoskeletal field. They are a cost-effective, efficient, and ethical means of evaluating treatments and interventions by supplementing and/or augmenting traditional randomized controlled trials (RCTs). While they are not a panacea and should not replace traditional RCTs, their integration into the research process promises to accelerate medical advancements and improve patient outcomes. To accomplish this, a multidisciplinary approach is needed, and collaboration is instrumental. With advances in computing and analytical prowess, and by adhering to the tenets of team science, realization of such a novel integrative approach toward clinical trials may not be far from providing far-reaching contributions to medical research. As such, by harnessing the power of in silico clinical trials, investigators can potentially unlock new possibilities in treatment and intervention for ultimately improving patient care and outcomes.

Progress in methodological research on bridging the efficacy-effectiveness gap of clinical interventions(2): to improve the extrapolation of efficacy

Objective: Randomized controlled trials (RCT) usually have strict implementation criteria. The included subjects' characteristics of the conditions for the intervention implementation are quite different from the actual clinical environment, resulting in discrepancies between the risk-benefit of interventions in actual clinical use and the risk-benefit shown in RCT. Therefore, some methods are needed to enhance the extrapolation of RCT results to evaluate the real effects of drugs in real people and clinical practice settings. Methods: Six databases (PubMed, Embase, Web of Science, CNKI, Wanfang Data, and VIP) were searched up to 31st December 2022 with detailed search strategies. A scoping review method was used to integrate and qualitatively describe the included literature inductively. Results: A total of 12 articles were included. Three methods in the included literature focused on: ①improving the design of traditional RCT to increase population representation; ②combining RCT Data with real-world data (RWD) for analysis;③calibrating RCT results according to real-world patient characteristics. Conclusions: Improving the design of RCT to enhance the population representation can improve the extrapolation of the results of RCT. Combining RCT data with RWD can give full play to the advantages of data from different sources; the results of the RCT were calibrated against real-world population characteristics so that the effects of interventions in real-world patient populations can be predicted.

Pragmatic clinical trials to advance research in children and adolescents with eating disorders.

To discuss the utility of pragmatic clinical trials (PCTs) to help advance research in eating disorders (EDs). We describe challenges associated with traditional explanatory research trials and examine PCTs as an alternative, including a review of the PRECIS-2 tool. There are many challenges associated with the design and completion of traditional RCTs within the field of EDs. Pragmatic clinical trials are studies that closely align with conditions available in everyday practice and focus on outcomes that are relevant to patients and clinicians. Results of PCTS maximize applicability and generalizability to clinical settings. Available therapies established for the treatment of EDs provide remission rates that rarely exceed 50%, implying a need for additional research on new or adjunctive treatments. In addition to a general overview of PCTs, we draw upon published literature and our own experiences involving adjunctive olanzapine for the treatment of children and youth with anorexia nervosa to help highlight challenges associated with randomized controlled trial (RCT) design and implementation, and offer pragmatic suggestions that would allow patients greater choice in treatment trials, while at the same time capturing outcomes that are most likely to advance treatment efforts. Pragmatic clinical trials provide alternatives to RCT design that can help bolster research in EDs that aims to explore real-world effects of interventions. Available therapies established for the treatment of eating disorders (EDs) in children and adolescents provide remission rates that rarely exceed 50%, implying a need for additional research on new or adjunctive treatments. In this article, we discuss the utility of pragmatic trials to help promote research that can help advance knowledge that is relevant to clinical care settings.

P505 Accelerating Crohn's Disease clinical trials using generative artificial intelligence

Abstract Background At Unlearn.AI, we expedite decision making and address challenges of lengthy and costly Crohn’s Disease (CD) trial enrollment by introducing deep learning models that create digital twins of trial participants, which are comprehensive forecasts of health outcomes. These generative predictions are incorporated into standard linear and logistic regression analyses, in addition to more advanced methods, in clinical trials. Our TwinRCT technology, which aligns with regulatory guidance, augments standard RCTs for easy integration into clinical programs. We demonstrate the expected benefit of these solutions in a subset of IBD Plexus data that were not previously involved in the training of the generative model, showcasing its potential to transform future CD trials. Methods We developed a generative deep learning model using data from approximately 4,500 Inflammatory Bowel Disorder (IBD) participants in observational studies and randomized controlled trial (RCT) control arms from the IBD Plexus program of the Crohn’s & Colitis Foundation. The trained model was used to forecast, entirely from each participant’s baseline data, the control outcomes of participants in a held out set of 300 participants. The resultant forecasts, or digital twins, represent comprehensive longitudinal trajectories across a range of outcomes, laboratory measures, and vital signs. We evaluated the expected benefits of incorporating the digital twins in linear and logistic regression models, analyzing the change from baseline to follow-up (Weeks 12 through 52) on sCDAI total score, change in daily bowel movement count, change in abdominal pain score and proportion of patients achieving clinical remission (sCDAI < 150; sCDAI is very closely related to CDAI). Results Digital twins provided substantial attainable benefit across outcomes and time points, enabling up to 21% overall sample size reduction or 34% reduction in the control arm only. Conclusion TwinRCTs offer a strategic enhancement to traditional RCTs by minimizing the required sample size needed to identify significant treatment effects. Aligning with existing FDA and EMA guidelines, TwinRCTs can be effectively utilized in Phase 2 and Phase 3 CD studies for faster decision making, as well as reduced timelines and costs, without increasing regulatory risk over standard approaches.

Registry randomized controlled trials: the future of surgical trials?

Registry randomized controlled trials: the future of surgical trials?

Evaluation of clinical trial designs for novel anticancer drugs in China: A cohort study of drugs approved between 2015 and 2021

China has greatly facilitated the approval of many novel anticancer drugs since the drug regulatory reform in 2015. Here, we review the clinical trial designs used in pivotal clinical trials for approved anticancer agents in China from 2015 to 2021. Overall, 79 new molecular entities (NMEs) with 140 anticancer indications were identified. Of these, adaptive randomized controlled trial (RCT) designs were used most frequently in pivotal clinical trials (n=83, 49%), followed by single-arm design trials (n=52, 30%) and traditional RCT design trials (n=36, 21%). The single-arm trials and adaptive RCTs can significantly shorten clinical trial duration compared with traditional RCT designs. Our findings showed that novel clinical trial designs were widely used in China to accelerate the launch of anticancer drugs.

A case for conducting business-to-business experiments with multi-arm multi-stage adaptive designs

Many businesses conduct experiments to scientifically test, measure, and optimize decisions in areas like sales, marketing, and operations efficiency. While randomized controlled trials (RCTs) or A/B tests are the dominant method for conducting business experiments especially for business-to-consumer marketing, adaptive designs have yet to make extensive inroads outside of the pharmaceutical and medical industries. In this study, we aim to raise awareness of the applicability and advantages of multi-arm multi-stage adaptive designs outside of clinical settings and we use simulations to demonstrate the value of these designs to modern business experiments, with a focus on business-to-business experiments such as testing alternative sales techniques. Our simulation results show that, compared to RCT, multi-arm multi-stage adaptive designs (MAMS) can reduce the sample size requirements and expected time to experiment completion whilst maintaining a similar level of statistical power. We also demonstrate that these benefits can translate into actual cost savings in conjunction with shorter time to market, resulting in higher overall efficiency over the traditional RCTs. MAMS serves as a strong alternative methodology in experiments where not all customers can be contacted at once such as business-to-business campaigns and general live channel programs which typically take weeks to months to complete.

Challenges Encountered in Surgical Traumatic Brain Injury Research: A Need for Methodological Improvement of Future Studies

Investigating neurosurgical interventions for traumatic brain injury (TBI) involves complex methodological and practical challenges. In the present report, we have provided an overview of the current state of neurosurgical TBI research and discussed the key challenges and possible solutions. The content of our report was based on an extensive literature review and personal knowledge and expert opinions of senior neurosurgeon researchers and epidemiologists. Current best practice research strategies include randomized controlled trials (RCTs) and comparative effectiveness research. The performance of RCTs has been complicated by the heterogeneity of TBI patient populations with the associated sample size requirements, the traditional eminence-based neurosurgical culture, inadequate research budgets, and the often acutely life-threatening setting of severe TBI. Statistical corrections can mitigate the effects of heterogeneity, and increasing awareness of clinical equipoise and informed consent alternatives can improve trial efficiency. The substantial confounding by indication, which limits the interpretability of observational research, can be circumvented by using an instrumental variable analysis. Traditional TBI outcome measures remain relevant but do not adequately capture the subtleties of well-being, suggesting a need for multidimensional approaches to outcome assessments. In settings in which traditional RCTs are difficult to conduct and substantial confounding by indication can be present, observational studies using an instrumental variable analysis and "pragmatic" RCTs are promising alternatives. Embedding TBI research into standard clinical practice should be more frequently considered but will require fundamental modifications to the current health care system. Finally, multimodality outcome assessment will be key to improving future surgical and nonsurgical TBI research.

COVID-19 Research Comparative Outcome looking through Real-World Evidence Vs. Clinical Trials

Randomized controlled trials (RCTs) are considered the gold standard because of their conventional study design, accurately defined endpoints, and high-quality evidence to define any cause-effect relationships. On the other hand, Real-world Data (RWD) relates to the collection of data pertaining to health status of the patient and/or delivery of routine health care. As the sources of RWD are becoming vast, Real-World Evidence (RWE) studies are gaining increasing attention. RWE can emerge as the backbone for regulatory decision-making in clinical research by providing adequate scientific evidence. A lot of RWE studies have been conducted in COVID-19, where a variety of repurposed drugs have been tested in real-world settings for the treatment and management of COVID-19. The current need is to work on a Hybrid trial methodology combining the most important parts of traditional RCTs and observational study designs to infer the RWE.  

How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis.

How Large a Study Is Needed to Detect TKA Revision Rate Reductions Attributable to Robotic or Navigated Technologies? A Simulation-based Power Analysis.

How representative is a point-of-care randomized trial? Clinical outcomes of patients excluded from a point-of-care randomized controlled trial evaluating antibiotic duration for Gram-negative bacteraemia: a multicentre prospective observational cohort study

ObjectivesPatients included in randomized controlled trials (RCT) are poorly representative of the general population. We compared outcomes of patients excluded from the PIRATE trial, a point-of-care RCT evaluating antibiotic durations for Gram-negative bacteraemia, with those of enrolled patients. MethodsA prospective observational cohort study, ‘EPCO’ (Excluded Patients' Clinical Outcomes) included patients excluded from the PIRATE trial. As in PIRATE, whose patients were randomized to 7-day, 14-day, or C-reactive-protein (CRP)-guided antibiotic durations, EPCO's primary outcome was occurrence of clinical success at 30 days. We also compared baseline characteristics, outcome rates and treatment-effect estimates. ResultsIn all, 405 patients were included in EPCO and compared with the 503 PIRATE patients. Reasons for exclusion were mainly medical (317/405; 78%), the most frequent being complicated infection. Excluded patients had more co-morbidities (Charlson median 3 versus 1, p < 0.001). Bacteraemia was more often health-care-associated (26% versus 9%, p < 0.001). The 30-day success rate was significantly lower among EPCO patients (299/396; 76% versus 469/493; 95%, p < 0.001), but the success rate was not significantly different for those excluded for non-medical reasons (68/75; 91%, p 0.09). There was no significant difference in failure rates of EPCO patients according to their treatment duration (difference 7 days versus 14 days: p 0.75; 7 days versus CRP-correspondent: p 1.00; 14 days versus CRP-correspondent: p 1.00). ConclusionAlthough point-of-care-randomized trials are more inclusive and representative than traditional RCTs, they are still likely to select patients with lower failure risk. Shorter antibiotic durations were not associated with failure in either included or excluded patients, supporting the generalizability of the PIRATE trial's findings.

A novel preference-informed complementary trial (PICT) design for clinical trial research influenced by strong patient preferences

BackgroundPatients and their families often have preferences for medical care that relate to wider considerations beyond the clinical effectiveness of the proposed interventions. Traditionally, these preferences have not been adequately considered in research. Research questions where patients and families have strong preferences may not be appropriate for traditional randomized controlled trials (RCTs) due to threats to internal and external validity, as there may be high levels of drop-out and non-adherence or recruitment of a sample that is not representative of the treatment population. Several preference-informed designs have been developed to address problems with traditional RCTs, but these designs have their own limitations and may not be suitable for many research questions where strong preferences and opinions are present.MethodsIn this paper, we propose a novel and innovative preference-informed complementary trial (PICT) design which addresses key weaknesses with both traditional RCTs and available preference-informed designs. In the PICT design, complementary trials would be operated within a single study, and patients and/or families would be given the opportunity to choose between a trial with all treatment options available and a trial with treatment options that exclude the option which is subject to strong preferences. This approach would allow those with strong preferences to take part in research and would improve external validity through recruiting more representative populations and internal validity. Here we discuss the strengths and limitations of the PICT design and considerations for analysis and present a motivating example for the design based on the use of opioids for pain management for children with musculoskeletal injuries.ConclusionsPICTs provide a novel and innovative design for clinical trials with more than two arms, which can address problems with existing preference-informed trial designs and enhance the ability of researchers to reflect shared decision-making in research as well as improving the validity of trials of topics with strong preferences.

Assessment of cancer screening effectiveness in the era of screening programs.

Whilst the benefit of detecting and removing cancer precursors in the cervix and the large bowel is unequivocally documented, the balance between benefit and harm due to screening for early detection of breast and prostate cancer remains uncertain after 30-50years of randomized control trials (RCTs). Concomitantly, traditional RCTs become increasingly unfeasible due to low compliance, contamination of control groups, difficulties to enroll screening naïve individuals, and prohibitively high costs. Therefore, we have systematically reviewed pros and cons with alternative study designs to quantify screening effectiveness. Whilst traditional RCTs should remain gold standard whenever feasible, observational cohort and case-control studies are unlikely to provide the valid evidence needed for health policy decisions. Instead, we advocate two methodological approaches that are novel in assessment of cancer screening effectiveness. One-the difference-in-difference design, well established in health policy and public health research-has a sophisticated ecologic design which unfortunately is feasible only in few countries. Another implies that sequential RCTs become embedded whenever population-based screening programs are implemented.

PBI4 EVIDENCE REQUIREMENT TRENDS FOR SINGLE ARM STUDY SCENARIOS IN RARE DISEASE AND ONCOLOGY: WHAT ARE THE LESSONS FOR VALUE DEMONSTRATION?

Approximately fifty percent of all new drug launches in rare disease and oncology recently have involved single-arm trial product launch scenarios for products that are in fast-track regulatory programs. This is a significant departure from our traditional randomized-controlled trial (RCT) and conventional evidence hierarchy-based decision model and suggest that clinical trial models are changing, driven by technology and other advances. This analysis considers evidence requirement trends associated with single-arm development scenarios and lessons for improving future product value demonstration approaches.

Trial designs using real-world data: The changing landscape of the regulatory approval process.

PurposeThere is a need to develop hybrid trial methodology combining the best parts of traditional randomized controlled trials (RCTs) and observational study designs to produce real‐world evidence (RWE) that provides adequate scientific evidence for regulatory decision‐making.MethodsThis review explores how hybrid study designs that include features of RCTs and studies with real‐world data (RWD) can combine the advantages of both to generate RWE that is fit for regulatory purposes.ResultsSome hybrid designs include randomization and use pragmatic outcomes; other designs use single‐arm trial data supplemented with external comparators derived from RWD or leverage novel data collection approaches to capture long‐term outcomes in a real‐world setting. Some of these approaches have already been successfully used in regulatory decisions, raising the possibility that studies using RWD could increasingly be used to augment or replace traditional RCTs for the demonstration of drug effectiveness in certain contexts. These changes come against a background of long reliance on RCTs for regulatory decision‐making, which are labor‐intensive, costly, and produce data that can have limited applicability in real‐world clinical practice.ConclusionsWhile RWE from observational studies is well accepted for satisfying postapproval safety monitoring requirements, it has not commonly been used to demonstrate drug effectiveness for regulatory purposes. However, this position is changing as regulatory opinions, guidance frameworks, and RWD methodologies are evolving, with growing recognition of the value of using RWE that is acceptable for regulatory decision‐making.

Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies.

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with 3 cycles, which compare aggregated N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: Aggregated N-of-1 trials outperformed both traditional parallel RCT and crossover designs when these trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

Real world study and its application in gastrointestinal stromal tumor

Gastrointestinal stromal tumor (GIST) is a relatively rare type of gastrointestinal tract tumors. Thus, it is difficult to perform randomized controlled trials (RCTs) of GIST in a single center, which are often plagued by a small number of participants. Real world study (RWS) is a complement to the evidence derived from traditional RCT. Emerging sources of real world data offer enormous opportunity for deeper understanding of why treatments work (or not) and for whom. Evidence generated from RWD can help clarify best use of treatments for individuals and populations, and care value. Thus, RWS of GIST has attracted much attention. RWS helps us better understand the diagnosis, treatment, prognosis and outcome prediction of GIST in clinical practice. GIST is often misdiagnosed as other tumor. A diagnostic test provides evidence on how well a test correctly identifies or rules out GIST. Therapeutic research aims to evaluate the effectiveness and/or safety of a therapy for GIST. Prognostic research aims to forecast the likely outcome of GIST, explore the factors affecting the outcome, and analyze quality of life. Predictive research aims to quantify the probability of identification or health outcome of GIST based on a set of predictors. Pharmacoeconomic data in real world can evaluate the cost-effectiveness of medicinal products for GISTs and serve as a guidance tool for optimal healthcare resource allocation. Attaching importance to data sourse and data quality, strenthening communicate with a qualified statistician, and the implementation of a standardized process are necessary for performing a high-quality RWS.

Establishing Clinical Utility for Diagnostic Tests Using a Randomized Controlled, Virtual Patient Trial Design

Demonstrating clinical utility for diagnostic tests and securing coverage and reimbursement requires high quality and, ideally, randomized controlled trial (RCT) data. Traditional RCTs are often too costly, slow, and cumbersome for diagnostic firms. Alternative data options are needed. We evaluated four RCTs using virtual patients to demonstrate clinical utility. Each study used a similar pre-post intervention, two round design to facilitate comparison. Representative samples of physicians were recruited and randomized into control and intervention arms. All physicians were asked to care for their virtual patients during two assessment rounds, separated by a multi-week time interval. Between rounds, intervention physicians reviewed educational materials on the diagnostic test. All physician responses were scored against evidence-based care criteria. RCTs using virtual patients can demonstrate clinical utility for a variety of diagnostic test types, including: (1) an advanced multi-biomarker blood test, (2) a chromosomal microarray, (3) a proteomic assay analysis, and (4) a multiplex immunofluorescence imaging platform. In two studies, utility was demonstrated for all targeted patient populations, while in the other two studies, utility was only demonstrated for a select sub-segment of the intended patient population. Of these four tests, two received positive coverage decisions from Palmetto, one utilized the study results to support commercial payer adjudications, and the fourth company went out of business. RCTs using virtual patients are a cost-effective approach to demonstrate the presence or absence of clinical utility.

Estimating Human Immunodeficiency Virus (HIV) Prevention Effects in Low-incidence Settings.

Randomized controlled trials (RCTs) for determining efficacy of preexposure prophylaxis (PrEP) in preventing human immunodeficiency virus (HIV) infection have not been conducted among US women because their lower HIV incidence requires impractically large studies. Results from higher-incidence settings, like Sub-Saharan Africa, may not apply to US women owing to differences in age, sexual behavior, coinfections, and adherence. We propose a novel strategy for evaluating PrEP efficacy in the United States using data from both settings to obtain four parameters: (1) intention-to-treat (ITT) and (2) per-protocol effects in the higher-incidence setting, (3) per-protocol effect generalized to the lower-incidence setting, and (4) back-calculated ITT effect using adherence data from the lower-incidence setting. To illustrate, we simulated two RCTs comparing PrEP against placebo: one in 4000 African women and another in 500 US women. We estimated all parameters using g-computation and report risk ratios averaged over 2000 simulations, alongside the 2.5th and 97.5th percentiles of the simulation results. Twelve months after randomization, the African ITT and per-protocol risk ratios were 0.65 (0.47, 0.88) and 0.20 (0.08, 0.34), respectively. The US ITT and per-protocol risk ratios were 0.42 (0.20, 0.62) and 0.17 (0.03, 0.38), respectively. These results matched well the simulated true effects. Our simple demonstration informs the design of future studies seeking to estimate the effectiveness of a treatment (like PrEP) in lower-incidence settings where a traditional RCT would not be feasible. See video abstract at, http://links.lww.com/EDE/B506.