Adaptive design clinical trials: Methodology, challenges and prospect

MicroRNAs in extracellular vesicles: A potential role in cancer progression

Intercellular communication, an essential biological process in multicellular organisms, is mediated by direct cell-to-cell contact and cell secretary molecules. Emerging evidence identifies a third mechanism of intercellular communication- the release of extracellular vesicles (EVs). EVs are membrane-enclosed nanosized bodies, released from cells into the extracellular environment, often found in all biofluids. The growing body of research indicates that EVs carry bioactive molecules in the form of proteins, DNA, RNAs, microRNAs (miRNAs), lipids, metabolites, etc., and upon transferring them, alter the phenotypes of the target recipient cells. Interestingly, the abundance of EVs is found to be significantly higher in different diseased conditions, most importantly cancer. In the past few decades, numerous studies have identified EV miRNAs as an important contributor in the pathogenesis of different types of cancer. However, the underlying mechanism behind EV miRNA-associated cancer progression and how it could be used as a targeted therapy remain ill-defined. The present review highlights how EV miRNAs influence essential processes in cancer, such as growth, proliferation, metastasis, angiogenesis, apoptosis, stemness, immune evasion, resistance to therapy, etc. A special emphasis has been given to the potential role of EV miRNAs as cancer biomarkers. The final section of the review delineates the ongoing clinical trials on the role of miRNAs in the progression of different types of cancer. Targeting EV miRNAs could be a potential therapeutic means in the treatment of different forms of cancer alongside conventional therapeutic approaches.

Key Considerations for Designing Clinical Studies to Evaluate Digital Health Solutions

Evidence of clinical impact is critical to unlock the potential of digital health solutions (DHSs), yet many solutions are failing to deliver positive clinical results. We argue in this viewpoint that this failure is linked to current approaches to DHS evaluation design, which neglect numerous key characteristics (KCs) requiring specific scientific and design considerations. We first delineate the KCs of DHSs: (1) they are implemented at health care system and patient levels; (2) they are "complex" interventions; (3) they can drive multiple clinical outcomes indirectly through a multitude of smaller clinical benefits; (4) their mechanism of action can vary between individuals and change over time based on patient needs; and (5) they develop through short, iterative cycles-optimally within a real-world use context. Following our objective to drive better alignment between clinical evaluation design and the unique traits of DHSs, we then provide methodological suggestions that better address these KCs, including tips on mechanism-of-action mapping, alternative randomization methods, control-arm adaptations, and novel end-point selection, as well as innovative methods utilizing real-world data and platform research.

Adaptive clinical trials in surgery: A scoping review of methodological and reporting quality

IMPORTANCE

Adaptive surgical trials are scarce, but adopting these methods may help elevate the quality of surgical research when large-scale RCTs are impractical.

OBJECTIVE

Randomized-controlled trials (RCTs) are the gold standard for evidence-based healthcare. Despite an increase in the number of RCTs, the number of surgical trials remains unchanged. Adaptive clinical trials can streamline trial design and time to trial reporting. The advantages identified for ACTs may help to improve the quality of future surgical trials. We present a scoping review of the methodological and reporting quality of adaptive surgical trials.

EVIDENCE REVIEW

We performed a search of Ovid, Web of Science, and Cochrane Collaboration for all adaptive surgical RCTs performed from database inception to October 12, 2023. We included any published trials that had at least one surgical arm. All review and abstraction were performed in duplicate. Risk of bias (RoB) was assessed using the RoB 2.0 instrument and reporting quality was evaluated using CONSORT ACE 2020. All results were analyzed using descriptive methods.

FINDINGS

Of the 1338 studies identified, six trials met inclusion criteria. Trials were performed in cardiothoracic, oral, orthopedic, and urological surgery. The most common type of adaptive trial was group sequential design with pre-specified interim analyses planned for efficacy, futility, and/or sample size re-estimation. Two trials did use statistical simulations. Our risk of bias evaluation identified a high risk of bias in 50% of included trials. Reporting quality was heterogeneous regarding trial design and outcome assessment and details in relation to randomization and blinding concealment.

CONCLUSION AND RELEVANCE

Surgical trialists should consider implementing adaptive components to help improve patient recruitment and reduce trial duration. Reporting of future adaptive trials must adhere to existing CONSORT ACE 2020 guidelines. Future research is needed to optimize standardization of adaptive methods across medicine and surgery.

Challenges and Opportunities in Interdisciplinary Research and Real-World Data for Treatment Sequences in Health Technology Assessments

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Coagulation Protease-Driven Cancer Immune Evasion: Potential Targets for Cancer Immunotherapy

Blood coagulation and cancer are intrinsically connected, hypercoagulation-associated thrombotic complications are commonly observed in certain types of cancer, often leading to decreased survival in cancer patients. Apart from the common role in coagulation, coagulation proteases often trigger intracellular signaling in various cancers via the activation of a G protein-coupled receptor superfamily protease: protease-activated receptors (PARs). Although the role of PARs is well-established in the development and progression of certain types of cancer, their impact on cancer immune response is only just emerging. The present review highlights how coagulation protease-driven PAR signaling plays a key role in modulating innate and adaptive immune responses. This is followed by a detailed discussion on the contribution of coagulation protease-induced signaling in cancer immune evasion, thereby supporting the growth and development of certain tumors. A special section of the review demonstrates the role of coagulation proteases, thrombin, factor VIIa, and factor Xa in cancer immune evasion. Targeting coagulation protease-induced signaling might be a potential therapeutic strategy to boost the immune surveillance mechanism of a host fighting against cancer, thereby augmenting the clinical consequences of targeted immunotherapeutic regimens.

Clinical trial considerations for pediatric cancer drug development

Oncology has been one of the most active therapeutic areas in medicinal products development. Despite this fact, few drugs have been approved for use in pediatric cancer patients when compared to the number approved for adults with cancer. This disparity could be attributed to the fact that many oncology drugs have had orphan drug designation and were exempt from Pediatric Research Equity Act (PREA) requirements. On August 18, 2017, the RACE for Children Act, i.e. Research to Accelerate Cures and Equity Act, was signed into law as Title V of the 2017 FDA Reauthorization Act (FDARA) to amend the PREA. Pediatric investigation is now required if the drug or biological product is intended for the treatment of an adult cancer and directed at a molecular target that FDA determines to be "substantially relevant to the growth or progression of a pediatric cancer." This paper discusses the specific considerations in clinical trial designs and statistical methodologies to be implemented in oncology pediatric clinical programs.

Review of the Immune Checkpoint Inhibitors in the Context of Cancer Treatment

Checkpoint proteins are an integral part of the immune system and are used by the tumor cells to evade immune response, which helps them grow uncontrollably. By blocking these proteins, immune checkpoint inhibitors can restore the capability of the immune system to attack cancer cells and stop their growth. These findings are backed by adequate clinical trial data and presently, several FDA-approved immune checkpoint inhibitors exist in the market for treating various types of cancers, including melanoma, hepatocellular, endometrial, lung, kidney and others. Their mode of action is inhibition by targeting the checkpoint proteins CTLA-4, PD-1, PD-L1, etc. They can be used alone as well as in amalgamation with other cancer treatments, like surgery, radiation or chemotherapy. Since these drugs target only specific immune system proteins, their side effects are reduced in comparison with the traditional chemotherapy drugs, but may still cause a few affects like fatigue, skin rashes, and fever. In rare cases, these inhibitors are known to have caused more serious side effects, such as cardiotoxicity, and inflammation in the intestines or lungs. Herein, we provide an overview of these inhibitors and their role as biomarkers, immune-related adverse outcomes and clinical studies in the treatment of various cancers, as well as present some future perspectives.

Governance of adaptive platform trials

Adaptive Clinical Trials (ACT) differ from conventional clinical trials because they permit continual modifications to key components of trial design during the trial. ACTs have grown in prevalence in recent years, with Adaptive Platform Trials (APTs), in particular, having demonstrated their significant scientific, clinical, and public health utility in relation to the COVID-19 pandemic. There has been a steady increase in the number of regulations and guidelines aimed at guiding the conduct of clinical trials. However, despite the potential of APTs to expedite the testing of new interventions in emergency situations, there is a relative dearth of published literature on why and how such trials should be governed. This work attempts to address this knowledge gap.

Lung Cancer Clinical Trials with a Seamless Phase II/III Design: Systematic Review

Current lung cancer clinical research focuses on biomarkers and personalized treatment strategies. Adaptive clinical trial designs have gained significant ground due to their increased flexibility, compared to the conventional model of drug development from phase I to phase IV trials. One such adaptive approach is the seamless phase II/III design, which has been used to reduce the total sample size and drug development time. In this context, an algorithmic systematic search was conducted in MEDLINE (PUBMED), SCOPUS, EMBASE and Cochrane Central Register of Controlled Trials until 31 June 2022 in order to identify lung cancer trials of systematic treatments that have employed the seamless phase II/III methodology and to describe their characteristics. The search strategy yielded a total of 1420 records that were screened through their title and abstract; 28 eligible trials were included in the systematic review. Based on the study endpoints, the most common subtype included phase II/III trials with inefficacy/futility analyses (61%; 17/28), followed by dose escalation phase II/III trials (18%; 5/28), one multi-arm multi stage trial and 5 trials with other design (18%). Most eligible trials were open-label (71%; 20/27), included patients with non-small cell lung cancer (82%; 23/28), evaluated targeted therapies and/or immunotherapies (82%; 23/28) and recruited patients with advanced disease (89.3%; 25/28). In conclusion, the seamless phase II/III design is a feasible and suitable approach in lung cancer research, with distinct design subcategories according to study endpoints.

Applying concepts from "rapid" and "agile" implementation to advance implementation research

Background

The translation of research findings into practice can be improved to maximize benefits more quickly and with greater flexibility. To expedite translation, researchers have developed innovative approaches to implementation branded as “rapid” and “agile” implementation. Rapid implementation has roots in precision medicine and agile implementation has roots in systems engineering and software design. Research has shown that innovation often derives from learning and applying ideas that have impacted other fields.

Implications for implementation researchers

This commentary examines “rapid” and “agile” approaches to implementation and provides recommendations to implementation researchers stemming from these approaches. Four key ideas are synthesized that may be broadly applicable to implementation research, including (1) adopting a problem orientation, (2) applying lessons from behavioral economics, (3) using adaptive study designs and adaptive interventions, and (4) using multi-level models to guide implementation. Examples are highlighted from the field where researchers are applying these key ideas to illustrate their potential impact.

Conclusions

“Rapid” and “agile” implementation approaches to implementation stem from diverse fields. Elements of these approaches show potential for advancing implementation research, although adopting them may entail shifting scientific norms in the field.

Progress, development, and challenges in amyotrophic lateral sclerosis clinical trials

INTRODUCTION

Amyotrophic Lateral Sclerosis (ALS) brings unique challenges to a clinical trial setting, due in part to relatively low disease prevalence coupled with a poor prognosis, in addition to the complexities linked to disease heterogeneity. As critical understanding of the disease develops, particularly in relation to clinical phenotype and the mechanisms of disease progression, so too new concepts evolve in relation to clinical trials, including the advent of precision therapy, targeted to subgroups of ALS patients.

AREAS COVERED

Individualized, or precision medicine in ALS recognizes the heterogeneous nature of the disease and utilizes information such as the clinical phenotype of the disease, clinical biomarkers, and genotyping to promote a tailored approach to treatment. Separate to these considerations, the present review will discuss clinical trial design and how this can be improved to better match patient and investigator needs in ALS clinical trials.

EXPERT OPINION

Precision therapy will promote a more focused treatment approach, with the goal of improving clinical outcomes for ALS patients. An increased community awareness of ALS, coupled with significant industry and philanthropic funding for ALS research, is accelerating this process.

The benefits of covariate adjustment for adaptive multi-arm designs

Covariate adjustment via a regression approach is known to increase the precision of statistical inference when fixed trial designs are employed in randomized controlled studies. When an adaptive multi-arm design is employed with the ability to select treatments, it is unclear how covariate adjustment affects various aspects of the study. Consider the design framework that relies on pre-specified treatment selection rule(s) and a combination test approach for hypothesis testing. It is our primary goal to evaluate the impact of covariate adjustment on adaptive multi-arm designs with treatment selection. Our secondary goal is to show how the Uniformly Minimum Variance Conditionally Unbiased Estimator can be extended to account for covariate adjustment analytically. We find that adjustment with different sets of covariates can lead to different treatment selection outcomes and hence probabilities of rejecting hypotheses. Nevertheless, we do not see any negative impact on the control of the familywise error rate when covariates are included in the analysis model. When adjusting for covariates that are moderately or highly correlated with the outcome, we see various benefits to the analysis of the design. Conversely, there is negligible impact when including covariates that are uncorrelated with the outcome. Overall, pre-specification of covariate adjustment is recommended for the analysis of adaptive multi-arm design with treatment selection. Having the statistical analysis plan in place prior to the interim and final analyses is crucial, especially when a non-collapsible measure of treatment effect is considered in the trial.

Electronic Health Interventions for Patients With Breast Cancer: Systematic Review and Meta-Analyses

PURPOSE

Ongoing supportive care using electronic health (eHealth) interventions has the potential to provide remote support and improve health outcomes for patients with breast cancer. This study aimed to evaluate the effectiveness of eHealth interventions on patient-reported outcomes (quality of life [QOL], self-efficacy, and mental or physical health) for patients during and after breast cancer treatment and patient-reported experience measures (acceptability and engagement).

METHODS

Systematic review with meta-analyses (random-effects model) of randomized controlled trials was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Nine databases were searched using a prespecified search strategy. Patient-directed eHealth interventions for adult patients during or after active breast cancer treatment measuring QOL, self-efficacy, and mental (depressive, anxiety, and distress symptoms) or physical (physical activity, nutrition, and fatigue) health outcomes were included. Data from eligible full-text articles were independently extracted by six observers.

RESULTS

Thirty-two unique studies (4,790 patients) were included. All were health self-management interventions, and most were multicomponent (videos, forums, and electronic reminder systems) websites. Meta-analyses revealed a significant effect of eHealth interventions on QOL (standardized mean difference [SMD], 0.20 [95% CI, 0.03 to 0.36]), self-efficacy (SMD, 0.45 [95% CI, 0.24 to 0.65]), distress (SMD, -0.41 [95% CI,-0.63 to -0.20]), and fatigue (SMD, -0.37 [95% CI, -0.61 to -0.13]). Twenty-five studies (78.1%) measured patient-reported experience measures. Acceptability (n = 9) was high, with high ratings for satisfaction (range, 71%-100%), usefulness (range, 71%-95%), and ease-of-use (range, 73%-92%). Engagement (n = 25) decreased over time, but disease-focused information and interactive support were most engaging.

CONCLUSION

eHealth interventions may provide an acceptable and effective strategy for improving QOL, distress, self-efficacy, and fatigue among patients with breast cancer.

Applications of artificial intelligence and machine learning in heart failure

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective

Background

Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power.

Results

The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package ’barts’.

Conclusion

The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.

RCTs and other clinical trial designs in Ayurveda: A review of challenges and opportunities

Currently, there is a paucity of clinical trial designs that comprehensively evaluate the efficacy of most complementary and alternative systems of medicine (CAMs) like Ayurveda. Several factors such as complex interventions, individualized therapy, etc., make designing Ayurveda clinical trials challenging. The prevalent randomized control trial (RCT) designs largely involve symptomatology/pathology-based recruitment and standardized interventions in carefully monitored trial environments. The present paper critically reviews the suitability of the dominant RCT model to Ayurveda and argues for newer, more sensitive trial models including modified RCTs and other clinical trial designs. It also explores the merits of a non-hierarchical approach to clinical evidence generation.

Challenges in Evaluating Safety and Efficacy in Drug Development for Rare Diseases: A Review for Pharmacists

A rare disease, or orphan disease, in the United States is a condition with a national prevalence of fewer than 200,000 diagnoses. As therapies for rare diseases are developed and brought to market, pharmacists should understand the challenges of drug development for rare diseases and aid in educating patients about the approval process for rare disease therapies. Developing drugs for treating rare diseases presents unique challenges in proving the drug's safety and efficacy with adequate study design, power, and validity. Results of the clinical trials for rare diseases may be weakened by small patient populations, limited disease information, and difficulty defining end points and biomarkers. In addition to investigational barriers, pharmaceutical companies face financial barriers in justifying the investment of bringing a rare disease therapy to market. Federal programs, such as the Orphan Drug Act of 1983, expedited review, the Rare Pediatric Disease Priority Review Vouchers (RPD PRV) program, and the 21st Century Cures Act, give pharmaceutical companies motivation to develop therapies for rare diseases. The objective of this article is to provide pharmacists with an understanding of the challenges in designing clinical trials for drugs for rare diseases and discuss federal programs that address efforts to develop safe and efficacious drugs for rare diseases.

Blinding, sham, and treatment effects in randomized controlled trials for back pain in 2000-2019: A review and meta-analytic approach

BACKGROUND

Blinding aims to minimize biases from what participants and investigators know or believe. Randomized controlled trials, despite being the gold standard to evaluate treatment effect, do not generally assess the success of blinding. We investigated the extent of blinding in back pain trials and the associations between participant guesses and treatment effects.

METHODS

We did a review with PubMed/OvidMedline, 2000-2019. Eligibility criteria were back pain trials with data available on treatment effect and participants' guess of treatment. For blinding, blinding index was used as chance-corrected measure of excessive correct guess (0 for random guess). For treatment effects, within- or between-arm effect sizes were used. Analyses of investigators' guess/blinding or by treatment modality were performed exploratorily.

RESULTS

Forty trials (3899 participants) were included. Active and sham treatment groups had mean blinding index of 0.26 (95% confidence interval: 0.12, 0.41) and 0.01 (-0.11, 0.14), respectively, meaning 26% of participants in active treatment believed they received active treatment, whereas only 1% in sham believed they received sham treatment, beyond chance, that is, random guess. A greater belief of receiving active treatment was associated with a larger within-arm effect size in both arms, and ideal blinding (namely, "random guess," and "wishful thinking" that signifies both groups believing they received active treatment) showed smaller effect sizes, with correlation of effect size and summary blinding indexes of 0.35 (p = 0.028) for between-arm comparison. We observed uniformly large sham treatment effects for all modalities, and larger correlation for investigator's (un)blinding, 0.53 (p = 0.046).

CONCLUSION

Participants in active treatments in back pain trials guessed treatment identity more correctly, while those in sham treatments tended to display successful blinding. Excessive correct guesses (that could reflect weaker blinding and/or noticeable effects) by participants and investigators demonstrated larger effect sizes. Blinding and sham treatment effects on back pain need due consideration in individual trials and meta-analyses.

Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies

The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting.

Brain Organoids: Tiny Mirrors of Human Neurodevelopment and Neurological Disorders

Unravelling the complexity of the human brain is a challenging task. Nowadays, modern neurobiologists have developed 3D model systems called "brain organoids" to overcome the technical challenges in understanding human brain development and the limitations of animal models to study neurological diseases. Certainly like most model systems in neuroscience, brain organoids too have limitations, as these minuscule brains lack the complex neuronal circuitry required to begin the operational tasks of human brain. However, researchers are hopeful that future endeavors with these 3D brain tissues could provide mechanistic insights into the generation of circuit complexity as well as reproducible creation of different regions of the human brain. Herein, we have presented the contemporary state of brain organoids with special emphasis on their mode of generation and their utility in modelling neurological disorders, drug discovery, and clinical trials.

Including non-concurrent control patients in the analysis of platform trials: is it worth it?

Background

Platform trials allow adding new experimental treatments to an on-going trial. This feature is attractive to practitioners due to improved efficiency. Nevertheless, the operating characteristics of a trial that adds arms have not been well-studied. One controversy is whether just the concurrent control data (i.e. of patients who are recruited after a new arm is added) should be used in the analysis of the newly added treatment(s), or all control data (i.e. non-concurrent and concurrent).

Methods

We investigate the benefits and drawbacks of using non-concurrent control data within a two-stage setting. We perform simulation studies to explore the impact of a linear and a step trend on the inference of the trial. We compare several analysis approaches when one includes all the control data or only concurrent control data in the analysis of the newly added treatment.

Results

When there is a positive trend and all the control data are used, the marginal power of rejecting the corresponding hypothesis and the type one error rate can be higher than the nominal value. A model-based approach adjusting for a stage effect is equivalent to using concurrent control data; an adjustment with a linear term may not guarantee valid inference when there is a non-linear trend.

Conclusions

If strict error rate control is required then non-concurrent control data should not be used; otherwise it may be beneficial if the trend is sufficiently small. On the other hand, the root mean squared error of the estimated treatment effect can be improved through using non-concurrent control data.

Innovation in Regulatory Science Is Meeting Evolution of Clinical Evidence Generation

At the turn of the century, the pharmaceutical industry began a transition toward a focus on oncology, rare diseases, and other areas of high unmet need that required a new, more complex approach to drug development. For many of these disease states and novel approaches to therapy, traditional approaches to clinical trial design fall short, and a number of innovative trial designs have emerged. In light of these changes, regulators across the globe are implementing new programs to provide regular development program support, facilitate accelerated access, use real-world data, and use digital tools to improve patients' lives. Emerging market regulators are also focusing on simplifying their regulatory pathways via regional harmonization schemes with varying levels of ambition. These changes in the external environment imply that biopharma regulatory teams need to adapt and evolve, leveraging digital tools, data, and analytics, and positioning themselves as strategic advisors during development.

Cardiovascular Nursing Science Priorities: A Statement From the American Heart Association Council on Cardiovascular and Stroke Nursing

BACKGROUND

The American Heart Association's (AHA) Council on Cardiovascular and Stroke Nursing (CVSN) plays a critical role in advancing the mission of the AHA in the discovery of new scientific knowledge. The aim was to identify priority research topics that would promote and improve cardiovascular (CV) health, provide direction for the education of future nurse scientists, and serve as a resource and catalyst for federal and organizational funding priorities.

METHODS

A Qualtrics survey, which included 3 questions about priorities for CVSN nurse researchers, was sent to the CVSN Leadership Committee and all CVSN Fellows of the AHA (n = 208). Responses to the questions were reviewed for word repetitions, patterns, and concepts and were then organized into thematic areas. The thematic areas were reviewed within small groups at the November (2016) in-person CVSN leadership meeting.

RESULTS

Seventy-three surveys were completed. Five thematic areas were identified and included (1) developing and testing interventions, (2) assessment and monitoring, (3) precision CV nursing care, (4) translational and implementation science, and (5) big data. Topic areas noted were stroke, research methods, prevention of stroke and CV disease, self-management, and care and health disparities.

CONCLUSION

Five thematic areas and 24 topic areas were identified as priorities for CV nursing research. These findings can provide a guide for CV nurse scientists and for federal and foundational funders to use in developing funding initiatives. We believe additional research and discovery in these thematic areas will help reduce the rising global burden of CV disease.

Computational Drug Repurposing: Current Trends

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Biomedical discovery has been reshaped upon the exploding digitization of data which can be retrieved from a number of sources, ranging from clinical pharmacology to cheminformatics-driven databases. Now, supercomputing platforms and publicly available resources such as biological, physicochemical, and clinical data, can all be integrated to construct a detailed map of signaling pathways and drug mechanisms of action in relation to drug candidates. Recent advancements in computer-aided data mining have facilitated analyses of ‘big data’ approaches and the discovery of new indications for pre-existing drugs has been accelerated. Linking gene-phenotype associations to predict novel drug-disease signatures or incorporating molecular structure information of drugs and protein targets with other kinds of data derived from systems biology provide great potential to accelerate drug discovery and improve the success of drug repurposing attempts. In this review, we highlight commonly used computational drug repurposing strategies, including bioinformatics and cheminformatics tools, to integrate large-scale data emerging from the systems biology, and consider both the challenges and opportunities of using this approach. Moreover, we provide successful examples and case studies that combined various in silico drug-repurposing strategies to predict potential novel uses for known therapeutics.

Process evaluation of a randomised controlled trial of PBS-based staff training for challenging behaviour in adults with intellectual disability

BACKGROUND

Positive Behaviour Support (PBS) for challenging behaviour is a complex intervention. Process evaluation is pivotal in fully understanding the mechanisms and contextual factors that impact on participant outcomes.

AIMS

To conduct a process evaluation of a national clinical trial investigating the impact of PBS-based staff training on the level of challenging behaviour in adults with intellectual disability.

METHOD

The Medical Research Council guidance for process evaluation of complex interventions was followed. Semi-structured interviews with 62 stakeholders from the intervention arm (service users, family and paid carers, service managers, staff who delivered the intervention and PBS trainers), quantitative data from the study database and an external evaluation of the quality of the PBS plans were used.

RESULTS

Twenty-one health staff volunteered to be trained in delivering PBS. Available log data from 17 therapists revealed that they worked with 63 participants a median of 11.50 hours (IQR 8-32). Only 33 out of 108 reports had included all elements of the intervention. Another 47 reports had some elements of the intervention. All PBS plans were rated weak, indicating insufficient quality to impact challenging behaviour. Stakeholders reported an appreciation of PBS and its potential to impact quality of care and engagement with the participant. However, they also identified important challenges including managing PBS-related caseloads, paid carer turnover and service commitment to the delivery of PBS.

CONCLUSIONS

PBS-based staff training was well received, but therapists found it difficult to undertake all the elements of the intervention in routine care. Implementing a workforce training strategy is important to better define the active components of PBS, and resource implications if the intervention is no better than usual care.

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

BACKGROUND

Research 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.

DISCUSSION

The 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.

CONCLUSION

The 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.

[Assessing the benefits of digital health solutions in the societal reimbursement context]

For a number of reasons, achieving reimbursability for digital health products has so far proven difficult. Demonstrating the benefits of the technology is the main hurdle in this context. The generally accepted evaluation processes, especially parallel group comparisons in randomized controlled trials (RCTs) for (clinical) benefit assessment, are primarily intended to deal with questions of (added) medical benefit. In contrast to drugs or classical medical devices, users of digital health solutions often profit from gaining autonomy, increased awareness and mindfulness, better transparency in the provision of care, and improved comfort, although there are also digital solutions with an interventional character targeting clinical outcomes (e. g. for indications such as anorexia, depression). Commonly accepted methods for evaluating (clinical) benefits primarily rely on medical outcomes, such as morbidity and mortality, but do not adequately consider additional benefits unique to digital health. The challenge is therefore to develop evaluation designs that respect the particularities of digital health without reducing the validity of the evaluations (especially with respect to safety). There is an increasing need for concepts that include both continuous feedback loops for adapting and improving an application while at the same time generate sufficient evidence for complex benefit assessments. This approach may help improve risk benefit ratio assessments of digital health when it comes to implementing digital innovations in healthcare.

Examining Tensions That Affect the Evaluation of Technology in Health Care: Considerations for System Decision Makers From the Perspective of Industry and Evaluators

Virtual technologies have the potential to mitigate a range of challenges for health care systems. Despite the widespread use of mobile devices in everyday life, they currently have a limited role in health service delivery and clinical care. Efforts to integrate the fast-paced consumer technology market with health care delivery exposes tensions among patients, providers, vendors, evaluators, and system decision makers. This paper explores the key tensions between the high bar for evidence prior to market approval that guides health care regulatory decisions and the “fail fast” reality of the technology industry. We examine three core tensions: balancing user needs versus system needs, rigor versus responsiveness, and the role of pre- versus postmarket evidence generation. We use these to elaborate on the structure and appropriateness of evaluation mechanisms for virtual care solutions. Virtual technologies provide a foundation for personalized, patient-centered medicine on the user side, coupled with a broader understanding of impact on the system side. However, mechanisms for stakeholder discussion are needed to clarify the nature of the health technology marketplace and the drivers of evaluation priorities.

The Future Role of the United States in Global Health: Emphasis on Cardiovascular Disease

U.S. global health investment has focused on detection, treatment, and eradication of infectious diseases such as tuberculosis, malaria, and human immunodeficiency virus/acquired immunodeficiency syndrome, with significant results. Although efforts should be maintained and expanded to provide ongoing therapy for chronic infectious disease, there is a pressing need to meet the challenge of noncommunicable diseases, which constitute the highest burden of diseases globally. A Committee of the National Academies of Sciences, Engineering, and Medicine has made 14 recommendations that require ongoing commitments to eradication of infectious disease and increase the emphasis on chronic diseases such as cardiovascular disease. These include improving early detection and treatment, mitigating disease risk factors, shifting global health infrastructure to include management of cardiovascular disease, developing global partners and private-public ventures to meet infrastructure and funding challenges, streamlining medical product development and supply, increasing research and development capacity, and addressing gaps in global political and institutional leadership to meet the shifting challenge.

A framework for the design, conduct and interpretation of randomised controlled trials in the presence of treatment changes

BACKGROUND

When a randomised trial is subject to deviations from randomised treatment, analysis according to intention-to-treat does not estimate two important quantities: relative treatment efficacy and effectiveness in a setting different from that in the trial. Even in trials of a predominantly pragmatic nature, there may be numerous reasons to consider the extent, and impact on analysis, of such deviations from protocol. Simple methods such as per-protocol or as-treated analyses, which exclude or censor patients on the basis of their adherence, usually introduce selection and confounding biases. However, there exist appropriate causal estimation methods which seek to overcome these inherent biases, but these methods remain relatively unfamiliar and are rarely implemented in trials.

METHODS

This paper demonstrates when it may be of interest to look beyond intention-to-treat analysis for answers to alternative causal research questions through illustrative case studies. We seek to guide trialists on how to handle treatment changes in the design, conduct and planning the analysis of a trial; these changes may be planned or unplanned, and may or may not be permitted in the protocol. We highlight issues that must be considered at the trial planning stage relating to: the definition of nonadherence and the causal research question of interest, trial design, data collection, monitoring, statistical analysis and sample size.

RESULTS AND CONCLUSIONS

During trial planning, trialists should define their causal research questions of interest, anticipate the likely extent of treatment changes and use these to inform trial design, including the extent of data collection and data monitoring. A series of concise recommendations is presented to guide trialists when considering undertaking causal analyses.

Drug development challenges and strategies to address emerging and resistant fungal pathogens

INTRODUCTION

Invasive fungal infections represent an expanding threat to public health. The recent emergence of Candida auris, which is often resistant to existing antifungal agents and is associated with a high mortality rate, underscores the urgent need for novel drug development strategies. Areas covered: In this paper, we examine both challenges and opportunities associated with antifungal drug development and explore potential avenues to accelerate the development pipeline, including data sharing, surrogate endpoints, and the role of historical controls in clinical trials. Expert commentary: We review important lessons learned from the study of other rare diseases, including mitochondrial storage diseases and certain forms of cancer that may inform strategies to develop new antifungal agents while highlighting promising new compounds such as SCY-078 for the treatment of invasive fungal infections.

Unique Study Designs in Nephrology: N-of-1 Trials and Other Designs

Alternatives to the traditional parallel-group trial design may be required to answer clinical questions in special populations, rare conditions, or with limited resources. N-of-1 trials are a unique trial design which can inform personalized evidence-based decisions for the patient when data from traditional clinical trials are lacking or not generalizable. A concise overview of factorial design, cluster randomization, adaptive designs, crossover studies, and n-of-1 trials will be provided along with pertinent examples in nephrology. The indication for analysis strategies such as equivalence and noninferiority trials will be discussed, as well as analytic pitfalls.

Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review

BACKGROUND

Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible.

METHODS AND FINDINGS

We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design.

CONCLUSIONS

Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.

An investigation of the impact of futility analysis in publicly funded trials

Background

Publicly funded trials regularly fail to recruit their target sample size or find a significant positive result. Adaptive clinical trials which may partly mediate against the problems are not often applied. In this paper we investigate the potential of a form of adaption in a clinical trial - a futility analysis - to see if it has potential to improve publicly funded trials.

Methods

Outcome data from trials funded by two UK bodies, the Health Technology Assessment (HTA) programme and the UK Medical Research Council (MRC), were collected. These data were then used to simulate each trial with a single futility analysis using conditional power, undertaken after 50% to 90% of the patients had been recruited. Thirty-three trials recruiting between 2002 and 2008 met the inclusion criteria. Stopping boundaries of conditional powers of 20%, 30% and 40% were considered and outcomes included the number of trials successfully stopped and number of patients saved.

Results

Inclusion of a futility analysis after 75% of the patients had been recruited would have potentially resulted in 10 trials, which went on to have negative results, correctly stopping for futility using a stopping boundary of 30%. A total of 807 patients across all the trials would potentially have been saved using these futility parameters. The proportion of studies successfully recruiting would also have increased from 45% to 64%.

Conclusions

A futility assessment has the potential to increase efficiency, save patients and decrease costs in publicly funded trials. While there are logistical issues in undertaking futility assessments we recommend that investigators should aim to include a futility analysis in their trial design wherever possible.

Use of Bayesian statistics in drug development: Advantages and challenges

Mainly, two statistical methodologies are applicable to the design and analysis of clinical trials: frequentist and Bayesian. Most traditional clinical trial designs are based on frequentist statistics. In frequentist statistics prior information is utilized formally only in the design of a clinical trial but not in the analysis of the data. On the other hand, Bayesian statistics provide a formal mathematical method for combining prior information with current information at the design stage, during the conduct of the trial, and at the analysis stage. It is easier to implement adaptive trial designs using Bayesian methods than frequentist methods. The Bayesian approach can also be applied for post-marketing surveillance purposes and in meta-analysis. The basic tenets of good trial design are same for both Bayesian and frequentist trials. It has been recommended that the type of analysis to be used (Bayesian or frequentist) should be chosen beforehand. Switching to an analysis method that produces a more favorable outcome after observing the data is not recommended.

How can attrition rates be reduced in cancer drug discovery?

Attrition is a major issue in anticancer drug development with up to 95% of drugs tested in Phase I trials not reaching a marketing authorisation making the drug development process enormously costly and inefficient. It is essential that this problem is addressed throughout the whole drug development process to improve efficiency which will ultimately result in increased patient benefit with more profitable drugs. The approach to reduce cancer drug attrition rates must be based on three pillars. The first of these is that there is a need for new pre-clinical models which can act as better predictors of success in clinical trials. Furthermore, clinical trials driven by tumour biology with the incorporation of predictive and pharmacodynamic biomarkers would be beneficial in drug development. Finally, there is a need for increased collaboration to combine the unique strengths between industry, academia and regulators to ensure that the needs of all stakeholders are met.

Conceptual and Analytical Considerations toward the Use of Patient-Reported Outcomes in Personalized Medicine

BACKGROUND

Patient-reported outcomes (PROs) can play an important role in personalized medicine. PROs can be viewed as an important fundamental tool to measure the extent of disease and the effect of treatment at the individual level, because they reflect the self-reported health state of the patient directly. However, their effective integration in personalized medicine requires addressing certain conceptual and methodological challenges, including instrument development and analytical issues.

OBJECTIVES

To evaluate methodological issues, such as multiple comparisons, missing data, and modeling approaches, associated with the analysis of data related to PRO and personalized medicine to further our understanding on the role of PRO data in personalized medicine.

DISCUSSION

There is a growing recognition of the role of PROs in medical research, but their potential use in customizing healthcare is not widely appreciated. Emerging insights into the genetic basis of PROs could potentially lead to new pathways that may improve patient care. Knowledge of the biologic pathways through which the various genetic predispositions propel people toward negative or away from positive health experiences may ultimately transform healthcare. Understanding and addressing the conceptual and methodological issues in PROs and personalized medicine are expected to enhance the emerging area of personalized medicine and to improve patient care. This article addresses relevant concerns that need to be considered for effective integration of PROs in personalized medicine, with particular reference to conceptual and analytical issues that routinely arise with personalized medicine and PRO data. Some of these issues, including multiplicity problems, handling of missing values-and modeling approaches, are common to both areas. It is hoped that this article will help to stimulate further research to advance our understanding of the role of PRO data in personalized medicine.

CONCLUSION

A robust conceptual framework to incorporate PROs into personalized medicine can provide fertile opportunity to bring these two areas even closer and to enhance the way a specific treatment is attuned and delivered to address patient care and patient needs.

Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine

The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of clinical medicine and pharmacology. Principles of systems pharmacology can be applied to drug design and, ultimately, testing in human clinical trials. Rather than focusing exclusively on single drug targets, systems pharmacology examines the holistic response of a phenotype-dependent pathway or pathways to drug perturbation. Knowledge of individual pharmacogenetic profiles further modulates the responses to these drug perturbations, moving the field toward more individualized ('personalized') drug development. The speed with which the information required to assess these system responses and their genomic underpinnings is changing and the importance of identifying the optimal drug or drug combinations for maximal benefit and minimal risk require that clinical trial design strategies be adaptable. In this paper, we review the tenets of adaptive clinical trial design as they may apply to an era of expanding knowledge of systems pharmacology and pharmacogenomics, and clinical trail design in network medicine.

Adaptive trial design: its growing role in clinical research and implications for pharmacists

PURPOSE

Current trends and recent developments in use of adaptive trial design methodology for pharmaceutical research, as well as barriers to wider acceptance and implications for pharmacists, are discussed.

SUMMARY

Traditional clinical drug trials typically take many months or years to complete. In contrast, trials incorporating adaptive design elements allow researchers to make midstudy adjustments so that trial objectives are addressed more efficiently. Properly designed adaptive trials can enable researchers to conclude trials of unsuccessful treatments earlier or bring effective drugs to market sooner, improving patient safety and yielding substantial time and cost savings. Challenges and concerns with adaptive trial methodology include inadequate knowledge of adaptive design techniques among health care professionals and increased potential for bias or misinterpretation of trial results stemming from earlier access to data. Over the past decade, U.S. and European regulatory bodies have issued a number of documents to better define acceptable practices for designing, conducting, and reporting the results of adaptive trials, including updated Food and Drug Administration guidance released in 2010. Pharmacists need to stay current with developments in the field to properly assess and interpret trial results.

CONCLUSION

Adaptive trial design is an emerging study methodology that allows for design modifications during a study, with the objective of implementing trial data as early as possible for the benefit of patients and the drug development process.

Implications of comparative effectiveness research for radiation oncology

PURPOSE

The essence of comparative effectiveness research (CER) is to understand what health interventions work, for which patients, and under what conditions. The objective of this article is to introduce the relative strengths and weaknesses of several forms of evidence to illustrate the potential for CER evidence generation within radiation oncology.

METHODS

We introduce the underlying concepts of effectiveness and efficacy. We describe the design of traditional explanatory randomized trials (RCTs). We introduce the rationale, strengths, and weaknesses of several alternative study designs for comparative effectiveness, including pragmatic clinical trials, adaptive trials, and observational (nonrandomized) studies.

RESULTS

Explanatory RCTs are designed to assess the efficacy of an intervention while achieving a high degree of internal validity. Pragmatic clinical trials (PCTs) are prospective studies performed in typical, real-world clinical practice settings. The emphasis of PCTs is to maintain a degree of internal validity while also maximizing external validity. Adaptive trials can be modified at interim stages using existing or evolving evidence in the course of a trial, which may allow trials to maintain clinical relevance by studying current treatments. Observational data are becoming increasingly important, given substantial funding for clinical registries and greater availability of electronic medical records and claims databases, but need to address well-known limitations such as selection bias.

CONCLUSION

With the rapid proliferation of new and evolving radiotherapy technologies, it is incumbent upon our field to invest in building the evidence base for radiotherapy CER and to actively participate in current initiatives for generating comparative evidence.