Adaptive designs in clinical trials: why use them, and how to run and report them

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.

Considerations for first field trials of low-threshold gene drive for malaria vector control

Sustainable reductions in African malaria transmission require innovative tools for mosquito control. One proposal involves the use of low-threshold gene drive in Anopheles vector species, where a 'causal pathway' would be initiated by (i) the release of a gene drive system in target mosquito vector species, leading to (ii) its transmission to subsequent generations, (iii) its increase in frequency and spread in target mosquito populations, (iv) its simultaneous propagation of a linked genetic trait aimed at reducing vectorial capacity for Plasmodium, and (v) reduced vectorial capacity for parasites in target mosquito populations as the gene drive system reaches fixation in target mosquito populations, causing (vi) decreased malaria incidence and prevalence. Here the scope, objectives, trial design elements, and approaches to monitoring for initial field releases of such gene dive systems are considered, informed by the successful implementation of field trials of biological control agents, as well as other vector control tools, including insecticides, Wolbachia, larvicides, and attractive-toxic sugar bait systems. Specific research questions to be addressed in initial gene drive field trials are identified, and adaptive trial design is explored as a potentially constructive and flexible approach to facilitate testing of the causal pathway. A fundamental question for decision-makers for the first field trials will be whether there should be a selective focus on earlier points of the pathway, such as genetic efficacy via measurement of the increase in frequency and spread of the gene drive system in target populations, or on wider interrogation of the entire pathway including entomological and epidemiological efficacy. How and when epidemiological efficacy will eventually be assessed will be an essential consideration before decisions on any field trial protocols are finalized and implemented, regardless of whether initial field trials focus exclusively on the measurement of genetic efficacy, or on broader aspects of the causal pathway. Statistical and modelling tools are currently under active development and will inform such decisions on initial trial design, locations, and endpoints. Collectively, the considerations here advance the realization of developer ambitions for the first field trials of low-threshold gene drive for malaria vector control within the next 5 years.

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.

Adaptive Designs in Implementation Science and Practice: Their Promise and the Need for Greater Understanding and Improved Communication

The promise of adaptation and adaptive designs in implementation science has been hindered by the lack of clarity and precision in defining what it means to adapt, especially regarding the distinction between adaptive study designs and adaptive implementation strategies. To ensure a common language for science and practice, authors reviewed the implementation science literature and found that the term adaptive was used to describe interventions, implementation strategies, and trial designs. To provide clarity and offer recommendations for reporting and strengthening study design, we propose a taxonomy that describes fixed versus adaptive implementation strategies and implementation trial designs. To improve impact, (a) futureimplementation studies should prespecify implementation strategy core functions that in turn can be taught to and replicated by health system/community partners, (b) funders should support exploratory studies that refine and specify implementation strategies, and (c) investigators should systematically address design requirements and ethical considerations (e.g., randomization, blinding/masking) with health system/community partners.

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Drug development for major chronic health conditions-aligning with growing public health needs: Proceedings from a multistakeholder think tank

The global pharmaceutical industry portfolio is skewed towards cancer and rare diseases due to more predictable development pathways and financial incentives. In contrast, drug development for major chronic health conditions that are responsible for a large part of mortality and disability worldwide is stalled. To examine the processes of novel drug development for common chronic health conditions, a multistakeholder Think Tank meeting, including thought leaders from academia, clinical practice, non-profit healthcare organizations, the pharmaceutical industry, the Food and Drug Administration (FDA), payors as well as investors, was convened in July 2022. Herein, we summarize the proceedings of this meeting, including an overview of the current state of drug development for chronic health conditions and key barriers that were identified. Six major action items were formulated to accelerate drug development for chronic diseases, with a focus on improving the efficiency of clinical trials and rapid implementation of evidence into clinical practice.

Current status of immunological therapies for rheumatoid arthritis with a focus on antigen-specific therapeutic vaccines

Rheumatoid arthritis (RA) is recognized as an autoimmune joint disease driven by T cell responses to self (or modified self or microbial mimic) antigens that trigger and aggravate the inflammatory condition. Newer treatments of RA employ monoclonal antibodies or recombinant receptors against cytokines or immune cell receptors as well as small-molecule Janus kinase (JAK) inhibitors to systemically ablate the cytokine or cellular responses that fuel inflammation. Unlike these treatments, a therapeutic vaccine, such as CEL-4000, helps balance adaptive immune homeostasis by promoting antigen-specific regulatory rather than inflammatory responses, and hence modulates the immunopathological course of RA. In this review, we discuss the current and proposed therapeutic products for RA, with an emphasis on antigen-specific therapeutic vaccine approaches to the treatment of the disease. As an example, we describe published results of the beneficial effects of CEL-4000 vaccine on animal models of RA. We also make a recommendation for the design of appropriate clinical studies for these newest therapeutic approaches, using the CEL-4000 vaccine as an example. Unlike vaccines that create or boost a new immune response, the clinical success of an immunomodulatory therapeutic vaccine for RA lies in its ability to redirect autoreactive pro-inflammatory memory T cells towards rebalancing the "runaway" immune/inflammatory responses that characterize the disease. Human trials of such a therapy will require alternative approaches in clinical trial design and implementation for determining safety, toxicity, and efficacy. These approaches include adaptive design (such as the Bayesian optimal design (BOIN), currently employed in oncological clinical studies), and the use of disease-related biomarkers as indicators of treatment success.

Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials

Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.

Standardization and Implementation of a Surgical Coaching Model for Pediatric Urology

To bridge gaps in proficiency and encourage life-long learning following training, coaching models have been utilized in multiple surgical fields; however, not within pediatric urology. In this review of our methodology, we describe the development of a coaching model at a single institution. In our initial experience, the perceived most beneficial aspect of the program was the goal setting process with logistics around debriefs being the most challenging. With our proposed coaching study, we aim to develop a model based upon prior coaching frameworks,1,2 that is feasible and universally adaptable to allow for further advancement of surgical coaching, particularly within the field of pediatric urology.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data

BACKGROUND: Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials. METHODS: We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial. RESULTS: Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention. CONCLUSIONS: Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

Mixed Methods Evaluation of Satisfaction with Two Culturally Tailored Substance use Prevention Programs for American Indian/Alaska Native Emerging Adults

American Indian/Alaska Native (AI/AN) communities are disproportionately affected by the opioid epidemic. AI/AN emerging adults (ages 18-25) in urban areas are at particularly high risk, with the overdose death rate among urban-dwelling AI/AN people 1.4 times higher than rural-dwelling AI/AN people. Despite these challenges, there are no evidence-based culturally tailored prevention or intervention programs to address opioid, alcohol and other drug use among urban AI/AN emerging adults. This study focused on understanding AI/AN emerging adults' experiences with two culturally tailored programs addressing opioid, cannabis, and alcohol use as part of the randomized controlled trial for Traditions and Connections for Urban Native Americans (TACUNA) in order to enhance feasibility of this intervention. Using a convergent mixed methods design at 3-month follow-up, we collected satisfaction and experience ratings and written narratives (total n = 162; intervention n = 77; control n = 85) from a sample of urban-dwelling AI/AN emerging adults who participated in both programs. We analyzed data through simultaneous examination of qualitative and quantitative data. The quantitative ratings show that both programs were rated highly. The qualitative data contextualized these ratings, illustrating pathways through which specific components were perceived to cause desired or observed behavioral change in participants. Among the elements that mattered most to these participants were the convenience of the virtual format, having a comfortable and safe space to share personal stories, and learning new information about their social networks. Negative comments focused on workshop length and inconvenient scheduling. This is one of the first studies to explore participant satisfaction and experience with culturally tailored substance use programming among a historically marginalized and understudied population. It is important to consider the voices of urban-dwelling AI/AN people in program development because hidden factors, such as limited financial resources, limited time, and misalignment with cultural values may prevent existing programs from being feasible.

A translational roadmap for transcranial magnetic and direct current stimulation in stroke rehabilitation: Consensus-based core recommendations from the third stroke recovery and rehabilitation roundtable

BACKGROUND AND AIMS

The purpose of this Third Stroke Recovery and Rehabilitation Roundtable (SRRR3) was to develop consensus recommendations to address outstanding barriers for the translation of preclinical and clinical research using the non-invasive brain stimulation (NIBS) techniques Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) and provide a roadmap for the integration of these techniques into clinical practice.

METHODS

International NIBS and stroke recovery experts (N = 18) contributed to the consensus process. Using a nominal group technique, recommendations were reached via a five-stage process, involving a thematic survey, two priority ranking surveys, a literature review and an in-person meeting.

RESULTS AND CONCLUSIONS

Results of our consensus process yielded five key evidence-based and feasibility barriers for the translation of preclinical and clinical NIBS research, which were formulated into five core consensus recommendations. Recommendations highlight an urgent need for (1) increased understanding of NIBS mechanisms, (2) improved methodological rigor in both preclinical and clinical NIBS studies, (3) standardization of outcome measures, (4) increased clinical relevance in preclinical animal models, and (5) greater optimization and individualization of NIBS protocols. To facilitate the implementation of these recommendations, the expert panel developed a new SRRR3 Unified NIBS Research Checklist. These recommendations represent a translational pathway for the use of NIBS in stroke rehabilitation research and practice.

Machine Learning and Health Science Research: Tutorial

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Preventing Respiratory Viral Illness Invisibly (PRiVII): protocol for a pragmatic cluster randomized trial evaluating far-UVC light devices in long-term care facilities to reduce infections

BACKGROUND

Respiratory viral illness (RVI)-e.g., influenza, COVID-19-is a serious threat in long-term care (LTC) facilities. Standard infection control measures are suboptimal in LTC facilities because of residents' cognitive impairments, care needs, and susceptibility to loneliness and mental illness. Further, LTC residents living with high degrees of frailty who contract RVIs often develop the so-called atypical symptoms (e.g., delirium, worse mobility) instead of typical cough and fever, delaying infection diagnosis and treatment. Although far-UVC (222 nm) light devices have shown potent antiviral activity in vitro, clinical efficacy remains unproven.

METHODS

Following a study to assay acceptability at each site, this multicenter, double-blinded, cluster-randomized, placebo-controlled trial aims to assess whether far-UVC light devices impact the incidence of RVIs in LTC facilities. Neighborhoods within LTC facilities are randomized to receive far-UVC light devices (222 nm) or identical placebo light devices that emit only visible spectrum light (400-700 nm) in common areas. All residents are monitored for RVIs using both a standard screening protocol and a novel screening protocol that target atypical symptoms. The 3-year incidence of RVIs will be compared using intention-to-treat analysis. A cost-consequence analysis will follow.

DISCUSSION

This trial aims to inform decisions about whether to implement far-UVC light in LTC facilities for RVI prevention. The trial design features align with this pragmatic intent. Appropriate additional ethical protections have been implemented to mitigate participant vulnerabilities that arise from conducting this study. Knowledge dissemination will be supported through media engagement, peer-reviewed presentations, and publications.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05084898. October 20, 2021.

Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion

Randomized controlled trials (RCTs) have traditionally been considered the gold standard for medical evidence. However, in light of emerging methodologies in data science, many experts question the role of RCTs. Within this context, experts in the USA and Canada came together to debate whether the primacy of RCTs as the gold standard for medical evidence, still holds in light of recent methodological advances in data science and in the era of big data. The purpose of this manuscript, aims to raise awareness of the pros and cons of RCTs and observational studies in order to help guide clinicians, researchers, students, and decision-makers in making informed decisions on the quality of medical evidence to support their work. In particular, new and underappreciated advantages and disadvantages of both designs are contrasted. Innovations taking place in both of these research methodologies, which can blur the lines between the two, are also discussed. Finally, practical guidance for clinicians and future directions in assessing the quality of evidence is offered.

Trials Investigating Exercise for Low Back Pain Lack Pragmatic Application: A Systematic Review

OBJECTIVE

To systematically review the orientation of trials analyzing exercise for low back pain (LBP) on the efficacy-effectiveness spectrum.

DATA SOURCES

PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Ovid MEDLINE were searched for trials published between January 1, 2010, and August 13, 2022.

STUDY SELECTION

Randomized controlled trials investigating exercise for adults with LBP.

DATA EXTRACTION

Two reviewers independently used the Rating of Included Trials on the Efficacy-Effectiveness Spectrum (RITES) tool to determine the efficacy-effectiveness orientation. The same 2 reviewers assessed the risk of bias for all studies using the Cochrane Collaboration risk of bias 2.0 tool.

DATA SYNTHESIS

The search identified 2975 records. Full-text review was conducted on 674 studies, and 159 studies were included. The proportion of trials with a strong or rather strong efficacy orientation (RITES rating=1 or 2), balanced emphasis (RITES rating=3), or strong or rather strong effectiveness (RITES rating=4 or 5) orientation on the efficacy-effectiveness spectrum within each RITES domain were reported. A greater proportion of trials had an efficacy orientation when compared with effectiveness or a balanced emphasis within 4 domains: participant characteristics: efficacy 43.9%, 41.9% effectiveness, balanced 14.5%; trial setting: 69.0% efficacy, effectiveness 15.8% balanced 15.2%; flexibility of interventions: 74.2% efficacy, effectiveness 8.8%, balanced 17.0%; clinical relevance of experimental and comparison interventions: 50.3% efficacy, 33.3% effectiveness 33.3%, balanced 16.4%. A high risk of bias was found in 42.1% (n=67) of trials.

CONCLUSION

Trials investigating the effect of exercise for LBP have a greater orientation toward efficacy across multiple trial design domains. Researchers should consider pragmatic designs in future trials to improve generalizability. Clinicians should consider the efficacy-effectiveness orientation when translating evidence into clinical practice.

A translational roadmap for transcranial magnetic and direct current stimulation in stroke rehabilitation: Consensus-based core recommendations from the third stroke recovery and rehabilitation roundtable

BACKGROUND AND AIMS

The purpose of this Third Stroke Recovery and Rehabilitation Roundtable (SRRR3) was to develop consensus recommendations to address outstanding barriers for the translation of preclinical and clinical research using the non-invasive brain stimulation (NIBS) techniques Transcranial Magnetic Stimulation (TMS) and Transcranial Direct Current Stimulation (tDCS) and provide a roadmap for the integration of these techniques into clinical practice.

METHODS

International NIBS and stroke recovery experts (N = 18) contributed to the consensus process. Using a nominal group technique, recommendations were reached via a five-stage process, involving a thematic survey, two priority ranking surveys, a literature review and an in-person meeting.

RESULTS AND CONCLUSIONS

Results of our consensus process yielded five key evidence-based and feasibility barriers for the translation of preclinical and clinical NIBS research, which were formulated into five core consensus recommendations. Recommendations highlight an urgent need for (1) increased understanding of NIBS mechanisms, (2) improved methodological rigor in both preclinical and clinical NIBS studies, (3) standardization of outcome measures, (4) increased clinical relevance in preclinical animal models, and (5) greater optimization and individualization of NIBS protocols. To facilitate the implementation of these recommendations, the expert panel developed a new SRRR3 Unified NIBS Research Checklist. These recommendations represent a translational pathway for the use of NIBS in stroke rehabilitation research and practice.

Bayesian phase II adaptive randomization by jointly modeling efficacy and toxicity as time-to-event outcomes

The main goals of Phase II trials are to identify the therapeutic efficacy of new treatments and continue monitoring all the possible adverse effects. In Phase II trials, it is important to develop an adaptive randomization (AR) procedure that takes into account both the efficacy and toxicity. In most existing articles, toxicity is modeled as a binary endpoint through an unobservable random effect (frailty) to link the efficacy and toxicity. However, this approach does not capture toxicity profiles that evolve over time. In this article, we propose a new Bayesian adaptive randomization (BAR) procedure using the covariate-adjusted efficacy-toxicity ratio (ETR) index, where efficacy and toxicity are jointly modelled as time-to-event (TTE) outcomes. Furthermore, we also propose early stopping rules for toxicity and futility such that inferior treatments can be dropped at earlier time of trial. Simulation results show that compared to the BAR procedures based solely on the efficacy and that based on TTE efficacy and binary toxicity outcomes, the proposed BAR procedure can better identify the difference in treatment toxicity such that it can assign more patients to the superior treatment arm under some scenarios.

Challenges, unmet needs and future directions - a critical evaluation of the clinical trial landscape in schizophrenia research

INTRODUCTION

Developing novel antipsychotic mechanisms of action and repurposing established compounds for the treatment of schizophrenia is of utmost importance to improve relevant symptom domains and to improve the risk/benefit ratio of antipsychotic compounds. Novel trial design concepts, pathophysiology-based targeted treatment approaches, or even the return to old values may improve schizophrenia outcomes in the future.

AREAS COVERED

In this review of the clinical trial landscape in schizophrenia, we present an overview of the challenges and gaps in current clinical trials and elaborate on potential solutions to improve the outcomes of people with schizophrenia.

EXPERT OPINION

The classic parallel group design may limit substantial advantages in drug approval or repurposing. Collaborative approaches between regulatory authorities, industry, academia, and funding agencies are needed to overcome barriers in clinical schizophrenia research to allow for meaningful outcome improvements for the patients.

The critical need to accelerate cerebral palsy research with consumer engagement, global networks, and adaptive designs

The prevalence of cerebral palsy (CP) varies globally, with higher rates and burden of disease in low- and middle-income countries. CP is a lifelong condition with no cure, presenting diverse challenges such as motor impairment, epilepsy, and mental health disorders. Research progress has been made but more is needed, especially given consumer demands for faster advancements and improvements in the scientific evidence base for interventions. This paper explores three strategies to accelerate CP research: consumer engagement, global clinical trial networks, and adaptive designs. Consumer engagement involving individuals with lived experience enhances research outcomes. Global clinical trial networks provide efficiency through larger and more diverse participant pools. Adaptive designs, unlike traditional randomized controlled trials, allow real-time modifications based on interim analyses, potentially answering complex questions more efficiently. The establishment of a CP Global Clinical Trials Network, integrating consumer engagement, global collaboration, and adaptive designs, marks a paradigm shift. The Network aims to address consumer-set research priorities. While challenges like ethical considerations and capacity building exist, the potential benefits for consumers, clinicians, researchers, and funding bodies are substantial. This paper underscores the urgency of transforming CP research methodologies for quicker translation of novel treatments into clinical practice to improve quality of life for those with CP.

Concordance of shoulder symptoms and imaging findings: a protocol for the Finnish Imaging of Shoulder (FIMAGE) study

INTRODUCTION

Shoulder pain is a substantial medical and socioeconomic problem in most societies, affecting the ability to work or carry out leisure time activities as well as subsequently influencing physical and psychological well-being. According to a nationwide survey in Finland, 27% of the population reported shoulder pain within the last 30 days. In clinical practice, imaging findings of structural abnormalities are typically thought to explain symptoms, even though such findings are also prevalent in asymptomatic individuals, particularly with increasing age. Overall, there is a paucity of high-quality evidence on the prevalence, clinical relevance and prognosis of 'abnormal' imaging findings of the shoulder.The aim of the Finnish Imaging of Shoulder (FIMAGE) study is fourfold: to assess (1) the prevalence of shoulder symptoms and the most common anatomical variants and imaging abnormalities of the shoulder; (2) the concordance between shoulder symptoms, function and imaging abnormalities; (3) the most important determinants of symptoms, function and imaging abnormalities; and (4) the course of shoulder complaints over 5 years.

METHODS

The FIMAGE target population of 600 participants, aged 40-75 years, will be randomly selected from a nationally representative general population sample of 9922 individuals originally recruited for the Finnish Health 2000 Survey. On giving informed consent, the participants will be invited to a clinical visit that includes assessment of general health, shoulder symptoms, bilateral shoulder examination and imaging of both shoulders with plain radiography and MRI.

ETHICS AND DISSEMINATION

The study has been approved by the Institutional Review Board of the Helsinki and Uusimaa Hospital District. The findings will be published according to the Strengthening the Reporting of Observational Studies in Epidemiology criteria.

TRIAL REGISTRATION NUMBER

NCT05641415.

Network and covariate adjusted response-adaptive design for binary response

Randomization is a distinguishing feature of clinical trials for unbiased assessment of treatment efficacy. With a growing demand for more flexible and efficient randomization schemes and motivated by the idea of adaptive design, in this article we propose the network and covariate adjusted response-adaptive (NCARA) design that can concurrently manage three challenges: (1) maximizing benefits of a trial by assigning more patients to the superior treatment group randomly; (2) balancing social network ties across treatment arms to eliminate potential network interference; and (3) ensuring balance of important covariates, such as age, gender, and other potential confounders. We conduct simulation with different network structures and a variety of parameter settings. It is observed that the NCARA design outperforms four alternative randomization designs in solving the above-mentioned problems and has comparable power and type I error for detecting true difference between treatment groups. In addition, we conduct real data analysis to implement the new design in two clinical trials. Compared to equal randomization (the original design utilized in the trials), the NCARA design slightly increases power, largely increases the percentage of patients assigned to the better-performing group, and significantly improves network and covariate balances. It is also noted that the advantages of the NCARA design are augmented when the sample size is small and the level of network interference is high. In summary, the proposed NCARA design assists researchers in conducting clinical trials with high-quality and high-efficiency.

Evaluating ethnically diverse patients' perspectives of considering participation in renal clinical research

BACKGROUND

Clinical trial cohorts do not often reflect target patient populations because minority ethnic groups are underrepresented in clinical trials.

AIM

To increase minority ethnic groups' opportunities to participate in clinical trials, by evaluating ethnically diverse patients' perspectives of considering participation in renal clinical research.

DISCUSSION

The authors gave patients participating in at least one research study the opportunity to take part in a structured survey. The survey explored preferences, barriers and opportunities that patients considered when deciding whether to take part in a clinical trial. The authors included participants from multiple ethnic groups so they could compare data for different ethnicities.

CONCLUSION

Participation was a positive experience for most patients, mostly because of the research team's flexibility and professionalism. Researchers' gender and ethnicity did not affect the participants' decision to participate. Cultural preferences were not obvious from the data as 80% of the participants were white.

IMPLICATIONS FOR PRACTICE

Patients preferred a face-to-face approach and the expertise of the research team affected participation more than any other characteristics did. However, respondents were already research-engaged and conducting a similar study with those who have declined to participate in research may show different results.

Conducting a bayesian multi-armed trial with response adaptive randomization for comparative effectiveness of medications for CSPN

Background

Response adaptive randomization is popular in adaptive trial designs, but the literature detailing its execution is lacking. These designs are desirable for patients/stakeholders, particularly in comparative effectiveness research, due to the potential benefits including improving participant buy-in by providing more participants with better treatment during the trial. Frequentist approaches have often been used, but adaptive designs naturally fit the Bayesian methodology; it was developed to deal with data as they come in by updating prior information.

Methods

PAIN-CONTRoLS was a comparative-effectiveness trial utilizing Bayesian response adaptive randomization to four drugs, nortriptyline, duloxetine, pregabalin, or mexiline, for cryptogenic sensory polyneuropathy (CSPN) patients. The aim was to determine which treatment was most tolerable and effective in reducing pain. Quit and efficacy rates were combined into a utility function to develop a single outcome, which with treatment sample size, drove the adaptive randomization. Prespecified interim analyses allowed the study to stop for early success or update the randomization probabilities to the better-performing treatments.

Results

Seven adaptations to the randomization occurred before the trial ended due to reaching the maximum sample size, with more participants receiving nortriptyline and duloxetine. At the end of the follow-up, nortriptyline and duloxetine had lower probabilities of participants that had stopped taking the study medication and higher probabilities were efficacious. Mexiletine had the highest quit rate, but had an efficacy rate higher than pregabalin.

Conclusions

Response adaptive randomization has become a popular trial tool, especially for those utilizing Bayesian methods for analyses. By illustrating the execution of a Bayesian adaptive design, using the PAIN-CONTRoLS trial data, this paper continues the work to provide literature for conducting Bayesian response adaptive randomized trials.

Optimizing clinical nutrition research: the role of adaptive and pragmatic trials

Evidence-based nutritional recommendations address the health impact of suboptimal nutritional status. Efficacy randomized controlled trials (RCTs) have traditionally been the preferred method for determining the effects of nutritional interventions on health outcomes. Nevertheless, obtaining a holistic understanding of intervention efficacy and effectiveness in real-world settings is stymied by inherent constraints of efficacy RCTs. These limitations are further compounded by the complexity of nutritional interventions and the intricacies of the clinical context. Herein, we explore the advantages and limitations of alternative study designs (e.g., adaptive and pragmatic trials), which can be incorporated into RCTs to optimize the efficacy or effectiveness of interventions in clinical nutrition research. Efficacy RCTs often lack external validity due to their fixed design and restrictive eligibility criteria, leading to efficacy-effectiveness and evidence-practice gaps. Adaptive trials improve the evaluation of nutritional intervention efficacy through planned study modifications, such as recalculating sample sizes or discontinuing a study arm. Pragmatic trials are embedded within clinical practice or conducted in settings that resemble standard of care, enabling a more comprehensive assessment of intervention effectiveness. Pragmatic trials often rely on patient-oriented primary outcomes, acquire outcome data from electronic health records, and employ broader eligibility criteria. Consequently, adaptive and pragmatic trials facilitate the prompt implementation of evidence-based nutritional recommendations into clinical practice. Recognizing the limitations of efficacy RCTs and the potential advantages of alternative trial designs is essential for bridging efficacy-effectiveness and evidence-practice gaps. Ultimately, this awareness will lead to a greater number of patients benefiting from evidence-based nutritional recommendations.

The Design of Nested Adaptive Clinical Trials of Multiple Organ Dysfunction Syndrome Children in a Single Study

OBJECTIVES

Describe the statistical design of the Personalized Immunomodulation in Sepsis-induced Multiple Organ Dysfunction Syndrome (MODS) (PRECISE) study.

DESIGN

Children with sepsis-induced MODS undergo real-time immune testing followed by assignment to an immunophenotype-specific study cohort. Interventional cohorts include the granulocyte macrophage-colony stimulating factor (GM-CSF) for the Reversal of Immunoparalysis in Pediatric Sepsis-induced MODS (GRACE)-2 trial, which uses the drug GM-CSF (or placebo) to reverse immunoparalysis; and the Targeted Reversal of Inflammation in Pediatric Sepsis-induced MODS (TRIPS) trial, which uses the drug anakinra (or placebo) to reverse systemic inflammation. Both trials have adaptive components and use a statistical framework in which frequent data monitoring assesses futility and efficacy, allowing potentially earlier stopping than traditional approaches. Prespecified simulation-based stopping boundaries are customized to each trial to preserve an overall one-sided type I error rate. The TRIPS trial also uses response-adaptive randomization, updating randomization allocation proportions to favor active arms that appear more efficacious based on accumulating data.

SETTING

Twenty-four U.S. academic PICUs.

PATIENTS

Septic children with specific immunologic derangements during ongoing dysfunction of at least two organs.

INTERVENTIONS

The GRACE-2 trial compares GM-CSF and placebo in children with immunoparalysis. The TRIPS trial compares four different doses of anakinra to placebo in children with moderate to severe systemic inflammation.

MEASUREMENTS AND MAIN RESULTS

Both trials assess primary efficacy using the sum of the daily pediatric logistic organ dysfunction-2 score over 28 days. Ranked summed scores, with mortality assigned the worst possible value, are compared between arms using the Wilcoxon Rank Sum test (GRACE-2) and a dose-response curve (TRIPS). We present simulation-based operating characteristics under several scenarios to demonstrate the behavior of the adaptive design.

CONCLUSIONS

The adaptive design incorporates innovative statistical features that allow for multiple active arms to be compared with placebo based on a child's personal immunophenotype. The design increases power and provides optimal operating characteristics compared with traditional conservative methods.

Tailoring cognitive behavioural therapy for insomnia across contexts, conditions, and individuals: What do we know, where do we go?

Cognitive behavioural therapy for insomnia (CBT-I) is considered the front-line treatment for insomnia. Despite the demonstrated effectiveness of CBT-I, it is necessary to consider how CBT-I may be tailored to different individuals. The purpose of the present review is to provide a summary of literature on tailoring CBT-I to different individuals and provide directions for future research. This review focused on the following domains of adaptation: (i) tailoring CBT-I components to individuals with comorbid mental or physical health conditions such as comorbid depression and pain; (ii) adapting CBT-I delivery for different contexts in which individuals exist, such as inpatient, educational, and different social/cultural settings, (iii) adapting CBT-I to specific individuals via case-formulation in clinical settings. We highlight current gaps in the exploration of tailored CBT-I, including a lack of research methodology to evaluate tailored interventions, a need for the integration of ongoing individualised assessment to inform treatment, and the necessary involvement of consumers and stakeholders throughout the research and treatment development process. Together, this review showed abundant adaptations in CBT-I already exist in the literature. Future research is needed in understanding when and how to apply adaptations in CBT-I and evaluate the benefits of these adaptations.

Adaptive design clinical trials: current status by disease and trial phase in various perspectives

An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages.

Ethics Along the Continuum of Research Involving Persons with Disorders of Consciousness

Interest in disorders of consciousness (DoC) has grown substantially over the past decade and has illuminated the importance of improving understanding of DoC biology; care needs (use of monitoring, performance of interventions, and provision of emotional support); treatment options to promote recovery; and outcome prediction. Exploration of these topics requires awareness of numerous ethics considerations related to rights and resources. The Curing Coma Campaign Ethics Working Group used its expertise in neurocritical care, neuropalliative care, neuroethics, neuroscience, philosophy, and research to formulate an informal review of ethics considerations along the continuum of research involving persons with DoC related to the following: (1) study design; (2) comparison of risks versus benefits; (3) selection of inclusion and exclusion criteria; (4) screening, recruitment, and enrollment; (5) consent; (6) data protection; (7) disclosure of results to surrogates and/or legally authorized representatives; (8) translation of research into practice; (9) identification and management of conflicts of interest; (10) equity and resource availability; and (11) inclusion of minors with DoC in research. Awareness of these ethics considerations when planning and performing research involving persons with DoC will ensure that the participant rights are respected while maximizing the impact and meaningfulness of the research, interpretation of outcomes, and communication of results.

Navigating the Complex Landscape of Fibrodysplasia Ossificans Progressiva: From Current Paradigms to Therapeutic Frontiers

Fibrodysplasia ossificans progressiva (FOP) is an enigmatic, ultra-rare genetic disorder characterized by progressive heterotopic ossification, wherein soft connective tissues undergo pathological transformation into bone structures. This incapacitating process severely limits patient mobility and poses formidable challenges for therapeutic intervention. Predominantly caused by missense mutations in the ACVR1 gene, this disorder has hitherto defied comprehensive mechanistic understanding and effective treatment paradigms. This write-up offers a comprehensive overview of the contemporary understanding of FOP's complex pathobiology, underscored by advances in molecular genetics and proteomic studies. We delve into targeted therapy, spanning genetic therapeutics, enzymatic and transcriptional modulation, stem cell therapies, and innovative immunotherapies. We also highlight the intricate complexities surrounding clinical trial design for ultra-rare disorders like FOP, addressing fundamental statistical limitations, ethical conundrums, and methodological advancements essential for the success of interventional studies. We advocate for the adoption of a multi-disciplinary approach that converges bench-to-bedside research, clinical expertise, and ethical considerations to tackle the challenges of ultra-rare diseases like FOP and comparable ultra-rare diseases. In essence, this manuscript serves a dual purpose: as a definitive scientific resource for ongoing and future FOP research and a call to action for innovative solutions to address methodological and ethical challenges that impede progress in the broader field of medical research into ultra-rare conditions.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials

Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test = 0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test < 0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all simulations with Cox regression-derived p value of  < 0.01 for the effect of the intervention on the respective primary outcome). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Innovative approaches to COVID-19 medical countermeasure development

BACKGROUND

The COVID-19 pandemic, while unfortunately notable for immense strain and death throughout the world, has also shown great promise in the development of medical countermeasures. As the global scientific community shifted almost entirely towards vaccines, diagnostics and therapeutics, new trial designs most significantly adaptive platform trials, began to be used with greater speed and broader reach. These designs allowed for deploying and investigating new therapeutics, repurposing currently existing therapeutics and flexibly removing or adding additional medications as data appeared in real-time. Moreover, public-private sector partnering occurred at a level not seen before, contributing greatly to the rapid development and deployment of vaccines.

OBJECTIVES

To provide a brief overview of the advances in preventative and therapeutic medical countermeasure development for COVID-19.

METHODS

A narrative review of relevant major medical countermeasure trials was conducted using the date range February 2020-December 2022, representing the period of greatest productivity in research to investigate COVID-19.

RESULTS

Among the most influential trial designs are the adaptive platform designs, which have been applied to the development of initial COVID-19 antivirals, monoclonal antibodies, repurposing of existing immunomodulatory therapy and assisted in the disproof of ineffective medical therapies. Some of the most prominent examples include the REMAP-CAP, RECOVERY and TOGETHER trials.

CONCLUSIONS

Adaptive platform trial designs hold great promise for utility in future pandemics and mass casualty events. Additionally, public-private sectoring is essential for rapid medical countermeasure development and should be further enhanced for future biopreparedness.

A flexible dose-response modeling framework based on continuous toxicity outcomes in phase I cancer clinical trials

BACKGROUND

The past few decades have seen remarkable developments in dose-finding designs for phase I cancer clinical trials. While many of these designs rely on a binary toxicity response, there is an increasing focus on leveraging continuous toxicity responses. A continuous toxicity response pertains to a quantitative measure represented by real numbers. A higher value corresponds not only to an elevated likelihood of side effects for patients but also to an increased probability of treatment efficacy. This relationship between toxicity and dose is often nonlinear, necessitating flexibility in the quest to find an optimal dose.

METHODS

A flexible, fully Bayesian dose-finding design is proposed to capitalize on continuous toxicity information, operating under the assumption that the true shape of the dose-toxicity curve is nonlinear.

RESULTS

We conduct simulations of clinical trials across varying scenarios of non-linearity to evaluate the operational characteristics of the proposed design. Additionally, we apply the proposed design to a real-world problem to determine an optimal dose for a molecularly targeted agent.

CONCLUSIONS

Phase I cancer clinical trials, designed within a fully Bayesian framework with the utilization of continuous toxicity outcomes, offer an alternative approach to finding an optimal dose, providing unique benefits compared to trials designed based on binary toxicity outcomes.

Translational Challenges and Prospective Solutions in the Implementation of Biomimetic Delivery Systems

Biomimetic delivery systems (BDSs), inspired by the intricate designs of biological systems, have emerged as a groundbreaking paradigm in nanomedicine, offering unparalleled advantages in therapeutic delivery. These systems, encompassing platforms such as liposomes, protein-based nanoparticles, extracellular vesicles, and polysaccharides, are lauded for their targeted delivery, minimized side effects, and enhanced therapeutic outcomes. However, the translation of BDSs from research settings to clinical applications is fraught with challenges, including reproducibility concerns, physiological stability, and rigorous efficacy and safety evaluations. Furthermore, the innovative nature of BDSs demands the reevaluation and evolution of existing regulatory and ethical frameworks. This review provides an overview of BDSs and delves into the multifaceted translational challenges and present emerging solutions, underscored by real-world case studies. Emphasizing the potential of BDSs to redefine healthcare, we advocate for sustained interdisciplinary collaboration and research. As our understanding of biological systems deepens, the future of BDSs in clinical translation appears promising, with a focus on personalized medicine and refined patient-specific delivery systems.

Subcutaneous ketamine infusion in palliative patients for major depressive disorder (SKIPMDD)-Phase II single-arm open-label feasibility study

BACKGROUND

Ketamine at subanaesthetic dosages (≤0.5mg/kg) exhibits rapid onset (over hours to days) antidepressant effects against major depressive disorder in people who are otherwise well. However, its safety, tolerability and efficacy are not known for major depressive disorder in people with advanced life-limiting illnesses.

OBJECTIVE

To determine the feasibility, safety, tolerability, acceptability and any antidepressant signal/activity to justify and inform a fully powered study of subcutaneous ketamine infusions for major depressive disorder in the palliative setting.

METHODS

This was a single arm, open-label, phase II feasibility study (Australian New Zealand Clinical Trial Registry Number-ACTRN12618001586202). We recruited adults (≥ 18-years-old) with advanced life-limiting illnesses referred to four palliative care services in Sydney, Australia, diagnosed with major depressive disorder from any care setting. Participants received weekly subcutaneous ketamine infusion (0.1-0.4mg/kg) over two hours using individual dose-titration design. Outcomes assessed were feasibility, safety, tolerability and antidepressant activity.

RESULTS

Out of ninety-nine referrals, ten participants received ketamine and were analysed for responses. Accrual rate was 0.54 participants/month across sites with 50% of treated participants achieving ≥ 50% reduction in baseline Montgomery-Åsberg Depression Rating Scale, meeting feasibility criteria set a priori. There were no clinically relevant harms encountered.

CONCLUSIONS

A future definitive trial exploring the effectiveness of subcutaneous infusion of ketamine for major depressive disorder in the palliative care setting may be feasible by addressing identified study barriers. Individual dose-titration of subcutaneous ketamine infusions over two hours from 0.1mg/kg can be well-tolerated and appears to produce transient antidepressant signals over hours to days.

Optimal trial design selection: a comparative analysis between two-arm and three-arm trials incorporating network meta-analysis for evaluating a new treatment

BACKGROUND

Planning the design of a new trial comparing two treatments already in a network of trials with an a-priori plan to estimate the effect size using a network meta-analysis increases power or reduces the sample size requirements. However, when the comparison of interest is between a treatment already in the existing network (old treatment) and a treatment that hasn't been studied previously (new treatment), the impact of leveraging information from the existing network to inform trial design has not been extensively investigated. We aim to identify the most powerful trial design for a comparison of interest between an old treatment A and a new treatment Z, given a fixed total sample size. We consider three possible designs: a two-arm trial between A and Z ('direct two-arm'), a two-arm trial between another old treatment B and Z ('indirect two-arm'), and a three-arm trial among A, B, and Z.

METHODS

We compare the standard error of the estimated effect size between treatments A and Z for each of the three trial designs using formulas. For continuous outcomes, the direct two-arm trial always has the largest power, while for a binary outcome, the minimum variances among the three trial designs are conclusive only when [Formula: see text]. Simulation studies are conducted to demonstrate the potential for the indirect two-arm and three-arm trials to outperform the direct two-arm trial in terms of power under the condition of [Formula: see text].

RESULTS

Based on the simulation results, we observe that the indirect two-arm and three-arm trials have the potential to be more powerful than a direct two-arm trial only when [Formula: see text]. This power advantage is influenced by various factors, including the risk of the three treatments, the total sample size, and the standard error of the estimated effect size from the existing network meta-analysis.

CONCLUSIONS

The standard two-arm trial design between two treatments in the comparison of interest may not always be the most powerful design. Utilizing information from the existing network meta-analysis, incorporating an additional old treatment into the trial design through an indirect two-arm trial or a three-arm trial can increase power.

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.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test=0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test<0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all with pone-sample t-test<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Drawing causal inferences from randomized controlled trials in psychotherapy research

OBJECTIVE

Randomized control trials (RCTs) have played a critical role in psychotherapy research. This article discusses RCTs in the context of the criteria for drawing causal inferences in psychotherapy and intervention research more generally. The article also highlights underused variations of RCTs and how they not only establish causal relations but also address critical questions that can improve our intervention portfolio and patient care.

CONCLUSION

Random assignment is discussed in terms of what it can and cannot do in relation to drawing conclusions about the effects of interventions. Finally, RCTs are examined in the context of multiple questions that can guide therapy research, improve patient care, and develop treatments that reach people in need of psychological services.

Association of preoperative anaesthesia consultation prior to elective noncardiac surgery with patient and health system outcomes: a population-based study

BACKGROUND

Surgical volumes and use of preoperative anaesthesia consultations are increasing. However, contemporary data estimating the association between preoperative anaesthesia consultation and patient (days alive and at home [DAH30], mortality) and system (costs, length of stay, and readmissions) outcomes are not available.

METHODS

We conducted a population-based comparative effectiveness study using linked health administrative data among patients aged ≥40 yr who underwent intermediate-risk to high-risk elective, inpatient, noncardiac surgery in Ontario, Canada (2009-17). Our primary outcome was DAH30. Secondary outcomes included DAH90, 30-day and 1-yr mortality, 30-day health system costs, length of index admission, and 30-day readmissions. Propensity score overlap weights were used to adjust for confounders. Prespecified effect modifier analyses focused on high-risk subgroups.

RESULTS

Among 364 149 patients, 274 365 (75.3%) received a preoperative anaesthesia consultation. No adjusted association was found (22.5 days vs 22.5 days; adjusted ratio of means 1.00, 95% CI 1.00-1.00) between consultation and DAH30 in the full population. We identified significant effect modification (significantly more DAH30) among patients with ischaemic heart disease, ASA physical status ≥4, frailty index score ≥0.21, and who underwent vascular surgery. Secondary outcomes were associated with preoperative consultation, including greater DAH90, decreased length of stay, lower 30-day and 1-yr mortality, and reduced 30-day costs.

CONCLUSIONS

Preoperative anaesthesia consultation was not associated with greater DAH30 across the overall study population. However, important potential benefits were observed among high-risk subgroups. Research is needed to identify optimal patient populations and consultation processes.

Adaptive designs in critical care trials: a simulation study

BACKGROUND

Adaptive clinical trials are growing in popularity as they are more flexible, efficient and ethical than traditional fixed designs. However, notwithstanding their increased use in assessing treatments for COVID-19, their use in critical care trials remains limited. A better understanding of the relative benefits of various adaptive designs may increase their use and interpretation.

METHODS

Using two large critical care trials (ADRENAL.

CLINICALTRIALS

gov number, NCT01448109. Updated 12-12-2017; NICE-SUGAR.

CLINICALTRIALS

gov number, NCT00220987. Updated 01-29-2009), we assessed the performance of three frequentist and two bayesian adaptive approaches. We retrospectively re-analysed the trials with one, two, four, and nine equally spaced interims. Using the original hypotheses, we conducted 10,000 simulations to derive error rates, probabilities of making an early correct and incorrect decision, expected sample size and treatment effect estimates under the null scenario (no treatment effect) and alternative scenario (a positive treatment effect). We used a logistic regression model with 90-day mortality as the outcome and the treatment arm as the covariate. The null hypothesis was tested using a two-sided significance level (α) at 0.05.

RESULTS

Across all approaches, increasing the number of interims led to a decreased expected sample size. Under the null scenario, group sequential approaches provided good control of the type-I error rate; however, the type I error rate inflation was an issue for the Bayesian approaches. The Bayesian Predictive Probability and O'Brien-Fleming approaches showed the highest probability of correctly stopping the trials (around 95%). Under the alternative scenario, the Bayesian approaches showed the highest overall probability of correctly stopping the ADRENAL trial for efficacy (around 91%), whereas the Haybittle-Peto approach achieved the greatest power for the NICE-SUGAR trial. Treatment effect estimates became increasingly underestimated as the number of interims increased.

CONCLUSIONS

This study confirms the right adaptive design can reach the same conclusion as a fixed design with a much-reduced sample size. The efficiency gain associated with an increased number of interims is highly relevant to late-phase critical care trials with large sample sizes and short follow-up times. Systematically exploring adaptive methods at the trial design stage will aid the choice of the most appropriate method.

Medicines and Healthcare products Regulatory Agency's "Consultation on proposals for legislative changes for clinical trials": a response from the Trials Methodology Research Partnership Adaptive Designs Working Group, with a focus on data sharing

In the UK, the Medicines and Healthcare products Regulatory Agency consulted on proposals "to improve and strengthen the UK clinical trials legislation to help us make the UK the best place to research and develop safe and innovative medicines". The purpose of the consultation was to help finalise the proposals and contribute to the drafting of secondary legislation. We discussed these proposals as members of the Trials Methodology Research Partnership Adaptive Designs Working Group, which is jointly funded by the Medical Research Council and the National Institute for Health and Care Research. Two topics arose frequently in the discussion: the emphasis on legislation, and the absence of questions on data sharing. It is our opinion that the proposals rely heavily on legislation to change practice. However, clinical trials are heterogeneous, and as a result some trials will struggle to comply with all of the proposed legislation. Furthermore, adaptive design clinical trials are even more heterogeneous than their non-adaptive counterparts, and face more challenges. Consequently, it is possible that increased legislation could have a greater negative impact on adaptive designs than non-adaptive designs. Overall, we are sceptical that the introduction of legislation will achieve the desired outcomes, with some exceptions. Meanwhile the topic of data sharing - making anonymised individual-level clinical trial data available to other investigators for further use - is entirely absent from the proposals and the consultation in general. However, as an aspect of the wider concept of open science and reproducible research, data sharing is an increasingly important aspect of clinical trials. The benefits of data sharing include faster innovation, improved surveillance of drug safety and effectiveness and decreasing participant exposure to unnecessary risk. There are already a number of UK-focused documents that discuss and encourage data sharing, for example, the Concordat on Open Research Data and the Medical Research Council's Data Sharing Policy. We strongly suggest that data sharing should be the norm rather than the exception, and hope that the forthcoming proposals on clinical trials invite discussion on this important topic.

How Electronic Medical Record Integration Can Support More Efficient Critical Care Clinical Trials

Large volumes of data are collected on critically ill patients, and using data science to extract information from the electronic medical record (EMR) and to inform the design of clinical trials represents a new opportunity in critical care research. Using improved methods of phenotyping critical illnesses, subject identification and enrollment, and targeted treatment group assignment alongside newer trial designs such as adaptive platform trials can increase efficiency while lowering costs. Some tools such as the EMR to automate data collection are already in use. Refinement of data science approaches in critical illness research will allow for better clinical trials and, ultimately, improved patient outcomes.

tVNS in Stroke: A Narrative Review on the Current State and the Future

Ischemic stroke is a leading cause of disability and there is a paucity of therapeutic strategies that promote functional recovery after stroke. Transcutaneous vagus nerve stimulation (tVNS) has shown promising evidence as a tool to reduce infarct size in animal models of hyperacute stroke. In chronic stroke, tVNS paired with limb movements has been shown to enhance neurological recovery. In this review, we summarize the current evidence for tVNS in preclinical models and clinical trials in humans. We highlight the mechanistic pathways involved in the beneficial effects of tVNS. We critically evaluate the current gaps in knowledge and recommend the key areas of research required to translate tVNS into clinical practice in acute and chronic stroke.