Traditional Randomized Clinical Trials Research Articles

Adaptive Study Design Methodology vs. Standard Randomized Clinical Trial in Different Therapeutic Area: A Review

Adaptive Study Design Methodology vs. Standard Randomized Clinical Trial in Different Therapeutic Area: A Review

A multi-objective optimization framework for determining optimal chemotherapy dosing and treatment duration

Traditional randomized clinical trials are regarded as the gold standard for assessing the efficacy of chemotherapy. However, this procedure has drawbacks such as high cost, time consumption, and limited patient exploration of treatment regimens. We develop a multi-objective optimization-based framework to address these limitations and determine the best chemotherapy dosing and treatment duration. The proposed framework uses patient-specific biological parameters to create a mathematical model of cell population dynamics in the patient’s body. The framework employs evolutionary heuristic search methods (simulated annealing and genetic algorithms) and a prescriptive analytics approach to optimize therapy sessions that transition from treatment to relaxation. We carefully adjust the chemotherapy dose during treatment to reduce tumor cells while preserving host cells (such as effector-immune cells). We strategically time the relaxation sessions to aid recovery, considering the ability of tumors and healthy cells to regenerate. We use a combined optimization method to determine the length of the session and the amount of drug to be administered. We compare quadratic and linear optimal control solvers for drug administration while genetic algorithms and simulated annealing are used to optimize session length. This approach is especially important in limited healthcare resources, ensuring efficient allocation while accurately identifying high-risk patients to optimize resource allocation and utilization.

Harnessing the Potential of Real-World Evidence in the Treatment of Colorectal Cancer: Where Do We Stand?

Treatment guidelines for colorectal cancer (CRC) are primarily based on the results of randomized clinical trials (RCTs), the gold standard methodology to evaluate safety and efficacy of oncological treatments. However, generalizability of trial results is often limited due to stringent eligibility criteria, underrepresentation of specific populations, and more heterogeneity in clinical practice. This may result in an efficacy-effectiveness gap and uncertainty regarding meaningful benefit versus treatment harm. Meanwhile, conduct of traditional RCTs has become increasingly challenging due to identification of a growing number of (small) molecular subtypes. These challenges-combined with the digitalization of health records-have led to growing interest in use of real-world data (RWD) to complement evidence from RCTs. RWD is used to evaluate epidemiological trends, quality of care, treatment effectiveness, long-term (rare) safety, and quality of life (QoL) measures. In addition, RWD is increasingly considered in decision-making by clinicians, regulators, and payers. In this narrative review, we elaborate on these applications in CRC, and provide illustrative examples. As long as the quality of RWD is safeguarded, ongoing developments, such as common data models, federated learning, and predictive modelling, will further unfold its potential. First, whenever possible, we recommend conducting pragmatic trials, such as registry-based RCTs, to optimize generalizability and answer clinical questions that are not addressed in registrational trials. Second, we argue that marketing approval should be conditional for patients who would have been ineligible for the registrational trial, awaiting planned (non) randomized evaluation of outcomes in the real world. Third, high-quality effectiveness results should be incorporated in treatment guidelines to aid in patient counseling. We believe that a coordinated effort from all stakeholders is essential to improve the quality of RWD, create a learning healthcare system with optimal use of trials and real-world evidence (RWE), and ultimately ensure personalized care for every CRC patient.

Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects

BackgroundThe aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest.MethodsIn this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM).ResultsThe results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase.ConclusionsThis study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.

Tactical Considerations for Designing Real-World Studies: Fit-for-Purpose Designs That Bridge Research and Practice.

Real-world evidence (RWE) is being used to provide information on diverse groups of patients who may be highly impacted by disease but are not typically studied in traditional randomized clinical trials (RCT) and to obtain insights from everyday care settings and real-world adherence to inform clinical practice. RWE is derived from so-called real-world data (RWD), ie, information generated by clinicians in the course of everyday patient care, and is sometimes coupled with systematic input from patients in the form of patient-reported outcomes or from wearable biosensors. Studies using RWD are conducted to evaluate how well medical interventions, services, and diagnostics perform under conditions of real-world use, and may include long-term follow-up. Here, we describe the main types of studies used to generate RWE and offer pointers for clinicians interested in study design and execution. Our tactical guidance addresses (1) opportunistic study designs, (2) considerations about representativeness of study participants, (3) expectations for transparency about data provenance, handling and quality assessments, and (4) considerations for strengthening studies using record linkage and/or randomization in pragmatic clinical trials. We also discuss likely sources of bias and suggest mitigation strategies. We see a future where clinical records - patient-generated data and other RWD - are brought together and harnessed by robust study design with efficient data capture and strong data curation. Traditional RCT will remain the mainstay of drug development, but RWE will play a growing role in clinical, regulatory, and payer decision-making. The most meaningful RWE will come from collaboration with astute clinicians with deep practice experience and questioning minds working closely with patients and researchers experienced in the development of RWE.

Evaluating Digital Twins for Alzheimer’s Disease using Data from a Completed Phase 2 Clinical Trial

AbstractBackgroundComplex Alzheimer’s Disease (AD) clinical trials that require a large number of subjects leads to long trial timelines and significant costs. Our novel methods can accelerate randomized clinical trials (RCT) by reducing the required number of subjects using a combination of deep learning‐based predictive models and statistical methods while meeting regulatory requirements. Our method leverages historical control clinical data in a way that allows for faster trials without sacrificing the reliability of traditional RCT analyses. A Phase 2 study on crenezumab (the ABBY study, NCT01343966) in mild‐to‐moderate AD was used to retrospectively assess the validity of this approach for AD clinical trials and the potential impact on future studies.MethodA probabilistic deep learning model of AD progression was used to predict longitudinal outcomes using baseline data for 184 subjects from the placebo arm of the ABBY study. These clinical predictions are called Digital Twins, and are comprehensive outputs across several outcomes, including the 12‐component Alzheimer’s Disease Assessment Scale ‐ Cognitive Subscale (ADAS‐Cog12), the Clinical Dementia Rating Sum‐of‐Boxes (CDR‐SB), labs, and vitals. After Digital Twins were created blind to subject outcomes, they were compared to observed outcomes across visits and statistical metrics were computed to evaluate the utility of predictions in accelerating this Phase 2 clinical trial. The Pearson correlation coefficient defines the extent to which Digital Twins can provide a reduction in subjects in a clinical trial.ResultUsing the 18‐month outcomes for the change in ADAS‐Cog12 and CDR‐SB scores from baseline, the Digital Twin predictions had a correlation of 0.46 (ADAS‐Cog12) and 0.37 (CDR‐SB) with the observed values from subjects that took placebo. These values suggest that in similarly designed studies, this approach can yield 21% (ADAS‐Cog12) or 14% (CDR‐SB) smaller studies when maintaining randomization, or 35% (ADAS‐Cog12) and 24% (CDR‐SB) smaller control arms when maintaining treatment arm size.ConclusionWhen used with appropriate statistical analysis methods, Digital Twins offer the ability to accelerate RCTs by requiring fewer subjects to achieve a desired power. Because these methods follow regulatory guidance, they can be applied in large pivotal studies to reduce trial timelines, costs, and potentially bring effective therapies to market faster.

Digital Health-Enabled Clinical Trials in Stroke: Ready for Prime Time?

As stroke continues to represent a major global health care problem, advancing our knowledge of new effective and safe stroke interventions represents a public health priority. The identification of these therapies requires the conduct of high-quality and well-powered randomized clinical trials. Despite its potential to inform clinical practice, traditional randomized clinical trial models have their drawbacks, including elevated costs, long completion times, failure to recruit the target sample sizes, lack of diversity, and complex operational procedures. Therefore, improving the participants' experience and trials' overall efficiency constitutes an important unmet need. Innovative models such as virtual and decentralized patient-centric trials have been proposed as a valuable strategy in this pursuit. In this narrative review, we discuss the limitations of traditional randomized clinical trial models and present the concept, advantages, and challenges of decentralized digitally enabled approaches to the conduct of stroke clinical trials.

Methodological Issues in Analyzing Real-World Longitudinal Occupational Health Data: A Useful Guide to Approaching the Topic.

Ever greater technological advances and democratization of digital tools such as computers and smartphones offer researchers new possibilities to collect large amounts of health data in order to conduct clinical research. Such data, called real-world data, appears to be a perfect complement to traditional randomized clinical trials and has become more important in health decisions. Due to its longitudinal nature, real-world data is subject to specific and well-known methodological issues, namely issues with the analysis of cluster-correlated data, missing data and longitudinal data itself. These concepts have been widely discussed in the literature and many methods and solutions have been proposed to cope with these issues. As examples, mixed and trajectory models have been developed to explore longitudinal data sets, imputation methods can resolve missing data issues, and multilevel models facilitate the treatment of cluster-correlated data. Nevertheless, the analysis of real-world longitudinal occupational health data remains difficult, especially when the methodological challenges overlap. The purpose of this article is to present various solutions developed in the literature to deal with cluster-correlated data, missing data and longitudinal data, sometimes overlapped, in an occupational health context. The novelty and usefulness of our approach is supported by a step-by-step search strategy and an example from the Wittyfit database, which is an epidemiological database of occupational health data. Therefore, we hope that this article will facilitate the work of researchers in the field and improve the accuracy of future studies.

A Bayesian group sequential design for randomized biosimilar clinical trials with adaptive information borrowing from historical data

ABSTRACT At the time of developing a biosimilar, the reference product has been on market for years and thus ample data are available on its efficacy and characteristics. We develop a Bayesian adaptive design for randomized biosimilar clinical trials to leverage the rich historical data on the reference product. This design takes a group sequential approach. At each interim, we employ the elastic meta-analytic-predictive (EMAP) prior methodology to adaptively borrow information from the historical data of the reference product to make go/no-go decision based on Bayesian posterior probabilities. In addition, the randomization ratio between the test and reference arms is adaptively adjusted at the interim with the goal to balance the sample size of the two arms at the end of trials. Simulation study shows that the proposed Bayesian adaptive design can substantially reduce the sample size of the reference arm, while achieving comparable power as the traditional randomized clinical trials that ignore the historical data. We apply our design to a biosimilar trial for treating breast cancer patients.

Improving Clinician Engagement in Patient‐Centered Outcomes Research

Research ObjectiveThe past decade has seen a proliferation of pragmatic clinical trials (PCTs) conducted in health care delivery settings. PCTs can be more informative to patients and other decision makers than traditional randomized clinical trials, but also require some level of involvement by provider organizations and health care professionals. However, PCTs have encountered challenges to engaging practicing clinicians. Empirical evidence regarding the views of health care providers on their potential role in such research is lacking, and there is limited information on the best strategies for involving practicing clinicians in PCTs. We conducted a qualitative study to better understand clinician views on their potential role in research conducted in the context of health care delivery and to develop guidance for researchers seeking to partner with clinicians.Study DesignWe conducted focus groups with “front‐line” clinicians within five health care delivery organizations. In addition, we conducted key informant interviews with research team members and clinicians participating in the ADAPTABLE aspirin‐dosing trial.Population StudiedThirty‐four health care providers participated in 6 focus groups and 2 phone interviews. Participants included 14 physicians, 8 nurses, 3 mental health providers, 2 dentists, and 7 medical assistants.We conducted 18 phone interviews with ADAPTABLE team members and clinicians, including 5 principal investigators, 10 project managers, and 3 participating clinicians.Principal FindingsFocus group participants strongly emphasized the importance of engaging practicing clinicians early in research planning in order to 1) identify study questions relevant to clinical practice, 2) improve study design with clinical insight, 3) better understand the specific patient population of interest, 4) adapt study protocols to the realities of clinic workflows, and 5) garner provider support prior to study implementation. They were generally supportive of efforts to design PCTs that limit the burden placed on clinicians (“low‐touch” PCTs), but expressed caution regarding the following: reliance on purely electronic approaches to patient engagement, patient hesitation to agree to study participation without checking with their provider, and clinician resistance to anything that might interfere with patient‐provider trust.ADAPTABLE study team members were sometimes surprised by the proportion of patients who wanted to speak with their physician before making a decision about study participation. They described multiple attempts at developing strategies to maximize the likelihood that patients and providers would discuss the study during clinic visits. Factors leading to success included multi‐faceted approaches to reminding both eligible patients and their providers prior to the patient‐clinician interaction and carefully selected and trained research staff.ConclusionsEven for “low‐touch” PCTs, researchers should not underestimate the importance of engaging with front‐line clinicians and clinic staff. We propose a framework for clinician engagement in “medium‐touch” PCTs that recognizes the importance of the clinician‐patient relationship and optimizes the role clinicians play in reassuring patients regarding trial participation.Implications for Policy or PracticePCTs hold the promise of dramatically improving the relevance of clinical trial evidence for decision‐making by patients, clinicians, and policymakers. To fully realize this potential, it is critical that researchers and clinicians find effective and mutually beneficial ways of partnering. Our findings will help to support this important and growing field.Primary Funding SourcePatient‐Centered Outcomes Research Institute.

Commentary on Point-of-Care Clinical Trials in Sports Medicine Research: Identifying Effective Treatment Interventions Through Real-World Evidence.

Commentary on Point-of-Care Clinical Trials in Sports Medicine Research: Identifying Effective Treatment Interventions Through Real-World Evidence.

Use of real-world evidence in regulatory decisions for rare diseases in the United States-Current status and future directions.

Following the release of the framework for the Real-World Evidence (RWE) Program, the US Food and Drug Administration (FDA) is actively evaluating and exploring ways to optimize the utility of real-world data (RWD) and RWE to support regulatory decision making. For rare conditions, conducting traditional randomized clinical trials may not always be feasible, and RWD and RWE have played and will continue to play an important role. We use three case examples-cerliponase alfa, asfotase alfa, and uridine triacetate-to illustrate how RWD from disease registries, medical records with chart review, and literature, respectively, have been used to generate RWE to support regulatory decisions for selected rare diseases. These examples highlight the need for improving data reliability and quality in existing data to expand use of RWD and RWE beyond "hard endpoints" and standardizing data collection for outcome measures in patient registries to expand its utility. We also discuss a recent FDA guidance for using RWE in supporting rare disease drug development, including its recommendations about using natural history studies as external control groups for single-arm interventional trials. The external control group needs to be comparable with the treated group. Selection bias and confounding are major concerns because of lack of randomization and unrecognized baseline differences. Use of valid epidemiological approaches can reduce these biases. Lastly, we discuss future directions to expand the use of RWD and RWE to support orphan drug approvals, including the need for including patient experience data as an important source of RWD.

Non-inferiority clinical trials: importance and applications in health sciences.

Non-inferiority randomized clinical trials are indicated when it is intended to prove that an experimental group is not inferior to a control group by more than a margin of non-inferiority. However, this type of study differs from traditional randomized clinical trials (superiority studies) because they have particularities that impact on the formulation of hypothesis to be tested, experimental design (non-inferiority margin determination, adapted sample size calculation, sensitivity of the study and data final analysis) and also on the presentation of data when writing the manuscript. Therefore, this article aims to present and discuss the particularities of non-inferiority clinical studies, since these requirements are fundamental to guarantee the validity of the conclusions of this type of study.

Новые технологии в области анализа Данных реального мира (RWD): вопросы и перспективы

Real World Data (RWD) offer the possibility to derive a more complete understanding of the use of medicines in everyday clinical practice, complementing rather than competing with evidence from traditional randomized clinical trials. Government regulators of the world’s leading economies and pharmaceutical manufacturers recognize the growing contribution of DRM to understanding of the lifecycle of medicines and patient needs. This overview of DRM development trends provides a thorough introduction to a variety of technological and methodological innovations, as well as potential ways of their application in the pharmaceutical industry.

Pragmatic Clinical Trials in Osteoporosis.

In this review, we present the application of pragmatic clinical trials for evaluating interventions in osteoporosis, and we discuss methodological considerations for designing and conducting a pragmatic clinical trial compared with a classical randomized clinical trial. Pragmatic clinical trials are a popular study design testing effectiveness of health interventions and are intended to address the limitations associated with traditional explanatory randomized clinical trials testing efficacy of interventions. To date, only few pragmatic clinical trials have been conducted in osteoporosis. Pragmatic clinical trials are conducted under routine clinical practice setting and are intended to inform policy makers and clinical decisions. Osteoporosis is a chronic disease well-suited to this particular study design given the existence of a clear and specific natural endpoint, namely fracture occurrence, and the availability of several treatments to prevent fractures.

Digital Health Interventions for Diabetes: Everything to Gain and Nothing to Lose.

IN BRIEF The traditional approach to integrating new therapies involves long, expensive roadmaps with evidence generation required for multiple stakeholders, most notably regulators and clinicians. More recently, new technologies such as insulin delivery systems and continuous glucose monitoring devices have become mainstream without complete clinical evidence being available when they were first introduced. There is tremendous enthusiasm from investors, industry, and people with diabetes regarding the potential of digital health to add value to diabetes care, and this enthusiasm exists despite a paucity of high-quality clinical evidence from traditional randomized clinical trials. Moreover, the potential of diabetes digital health technologies has been recognized by the U.S. Food and Drug Administration and other regulators, who are changing their approaches to allow easier, earlier access to diabetes software and devices. This wager that digital health will add value makes sense.

The ethical justification for inclusion of neonates in pragmatic randomized clinical trials for emergency newborn care

BackgroundResearch guidelines generally recognize vulnerable populations to include neonates with the aim of enhancing protections from harm. In practice, such guidance results in limiting participation in randomized clinical trials (RCTs). Yet while medical care of neonates should be based on best research evidence to ensure that safe, efficacious treatment or procedures are used, this seldom happens in contemporary practice.DiscussionThe compelling need to generate information on effectiveness and safety of procedures and medications that are already in use during neonatal care has led to increase in calls for pragmatic randomized clinical trials (PCTs). This raises ethical concerns as to whether exclusion of the vulnerable populations from research participations constitutes harm. First, neonates are denied access to both potentially beneficial research outputs and an opportunity to generate data on how interventions or medications perform in diverse clinical settings and inform clinical decision-making. Secondly, risks and harms in PCTs may differ from traditional RCTs, and can be reduced by modifications in study designs. The latter may involve assessment of effectiveness of comparable medication, devices or practices (whose safety data is available), randomization at the group level rather than at the individual level, avoidance of invasive and innovative study procedures, reliance on locally available data on relevant patient outcomes, and employment of procedures that tend to meet the criteria of minimal risk for human subject research. Thirdly, informed consent procedures should be modified from those of traditional RCTs, as neonates in traditional RCTs may be vulnerable to different extents in PCTs. Lastly, regulatory and oversight procedures designed for traditional RCT settings need modification, as they may not be translatable, feasible, appropriate or even ethical to apply in PCTs.ConclusionThe principle of justice, commonly interpreted as preventing an inequitable burden of research, should also allow fair access to potential benefits from PCTs for neonates and other vulnerable populations. Under certain conditions, prospective randomized trials involving neonates should be ethically permissible to allow inclusion of neonates in research. This may require modification of the research design, consent procedures or regulations for research oversight.

Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy

Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception—participants’ tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations—and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.

Ethical Acceptability of Postrandomization Consent in Pragmatic Clinical Trials

Pragmatic clinical trials that seek informed consent after randomization (ie, postrandomization consent) are increasingly used, but debate on ethics persists because control arm patients are not specifically informed about the trials and randomization occurs before consent for the trials. The public's attitude toward postrandomization consent trials is unknown, but the way the trials are described could bias people's views. To assess the attitudes of the US general public toward postrandomization informed consent for pragmatic trials and to measure potential framing and other factors associated with those attitudes. An online, 2 × 2 experimental survey (fielded between February 23 and April 3, 2018) portraying 4 scenarios of postrandomization informed consent (with prior broad consent for medical record use) was conducted. These scenarios included traditional randomized clinical trial language framing vs alternative framing in a high-stakes trial (ie, survival in leukemia) or low-stakes trial (ie, blood glucose level in diabetes). A total of 3793 individuals invited to participate were part of an existing panel representative of the US general public (GfK KnowledgePanel). The proportion of participants who would recommend that an ethics review board approve a postrandomization consent pragmatic trial. A total of 2042 of 3739 invitees (54.6%) responded; after exclusion of 38 incomplete surveys, 2004 participants were included in the analysis. Of these, 997 (49.8%) were women, 1440 (71.9%) were white non-Hispanic, 199 (9.9%) were black non-Hispanic, and 233 (11.6%) were Hispanic. Mean (SD) age was 47.5 (17.4) years. Across scenarios, weighted data showed that 75.4% of the participants would recommend approval of the postrandomization consent pragmatic trial, 20.4% would probably not recommend approval, and 4.2% would definitely not recommend approval. Approval was not sensitive to framing language (traditional vs new framing in high-stakes scenario, 74.3% vs 76.8%, P = .40; in low-stakes scenario, 77.7% vs 72.9%, P = .10) or to the stakes (low vs high stakes in traditional framing, 77.7% vs 74.3%, P = .25; in new framing, 72.9% vs 76.8%, P = .18). Better understanding of the postrandomization consent design was associated with higher rate of approval (78.1% vs 65.0%, P = .002 for high-stakes scenario; 77.2% vs 64.9%, P = .004 for low-stakes scenario), especially among those with less education. However, opinions about personal involvement in the control arm were more cautious (range depending on scenario, 45.6%-59.7%) and sensitive to stakes but not to framing. The public's generally high rate of approval of the ethics of postrandomization informed consent for pragmatic trial designs does not appear to be affected by whether postrandomization consent design is framed using traditional randomized clinical trial terminology, regardless of the stakes of the trial. Promoting better understanding of the design may increase its acceptance by the public.

Introduction: Personalized medicine: what is it and what are the challenges?

Personalized medicine and precision medicine are presenting new opportunities for better patient care, but also present unique challenges. The traditional randomized clinical trial is not necessarily the most appropriate approach to evaluating what may be the best treatment for an individual. Alternative study designs pose unique regulatory challenges for the U.S. Food and Drug Administration but offer the opportunity for higher response rates. Ovarian stimulation for assisted reproductive technology offers opportunities for personalized and more precise approaches to individual patient care.