DHA supplementation for early preterm birth prevention: An application of Bayesian finite mixture models to adaptive clinical trial design optimization.

BACKGROUND: Early preterm birth (ePTB) - born before 34 weeks of gestation - poses a significant public health challenge. Two randomized trials indicated an ePTB reduction among pregnant women receiving high-dose docosahexaenoic acid (DHA) supplementation. One of them is Assessment of DHA on Reducing Early Preterm Birth (ADORE). A survey employed in its secondary analysis identified women with low DHA levels, revealing that they derived greater benefits from high-dose DHA supplementation. This survey's inclusion in future trials can provide critical insights for informing clinical practices. OBJECTIVE: To optimize a Phase III trial design, ADORE Precision, aiming at assessing DHA supplement (200 vs. 1000 mg/day) on reducing ePTB among pregnant women with a low baseline DHA. METHODS: We propose a Bayesian Hybrid Response Adaptive Randomization (RAR) Design utilizing a finite mixture model to characterize gestational age at birth. Subsequently, a dichotomized ePTB outcome is used to inform trial design using RAR. Simulation studies were conducted to compare a Fixed Design, an Adaptive Design with early stopping, an ADORE-like Adaptive RAR Design, and two new Hybrid Designs with different hyperpriors. DISCUSSION: Simulation reveals several advantages of the RAR designs, such as higher allocation to the more promising dose and a trial duration reduction. The proposed Hybrid RAR Designs addresses the statistical power drop observed in Adaptive RAR. The new design model shows robustness to hyperprior choices. We recommend Hybrid RAR Design 1 for ADORE Precision, anticipating that it will yield precise determinations, which is crucial for advancing our understanding in this field.

A Systematic Review of Adaptive Seamless Clinical Trials for Late-Phase Oncology Development.

INTRODUCTION: Although oncology has seen large scientific and clinical advances over the last decade, it also has one of the lowest success rates for novel agents across therapeutic areas. Adaptive clinical trial design has been a popular option for increasing clinical trial efficiency and the chances of trial success. Seamless clinical trial design are studies in which two or more clinical trial phases are combined into a single study with a pre-specified transition between stages. This integration of phases may enhance efficiency. METHODS: To understand the precedent for the use of seamless designs, this working group was formed to conduct a comprehensive literature search on seamless clinical trials conducted with confirmatory intent in oncology. Trial design features were extracted into a database and analyzed with descriptive statistics. RESULTS: A literature search identified 68 clinical trials meeting inclusion and exclusion criteria. The most common design feature was a gate on treatment efficacy, where the trial would only proceed to the second stage if sufficient efficacy was observed in the first. The next most common feature was a selection of a dose or treatment regimen. Inferentially and operationally seamless designs were approximately equally represented. DISCUSSION: Key statistical considerations for seamless phase II/III designs include optimizing design choices by evaluating and comparing operating characteristics across design alternatives as well as showing control of overall Type I error rates. Executing the transition between phases should be evaluated for issues related to accrual, drug production, and procedures to maintain trial integrity. CONCLUSIONS: While there are unique statistical, regulatory, and operational considerations for seamless designs they are also uniquely suited to many development settings. These include, for example, addressing dose selection under FDA's Project Optimus and addressing the growing use of biomarkers and personalized medicine approaches in cancer treatment.

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations.

BACKGROUND: Adaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference. METHODS: We simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design. RESULTS: Power generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size. CONCLUSIONS: We designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

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.

Adaptive designs in dermatology clinical trials: Current status and future perspectives.

Current drug development strategies present many challenges that can impede drug approval by regulatory agencies. Alternative study models, such as adaptive trial designs, have recently sparked interest, as they provide a flexible and more efficient approach in conducting clinical trials. Adaptive trial designs offer several potential benefits over traditional randomized controlled trials, which include decrease in costs, reduced clinical development time and limiting exposure of patients to potentially ineffective treatments allowing completion of studies with fewer patients. This article explores the current use of adaptive trial designs in non-oncologic skin diseases and highlights the most common types of adaptive designs used in the field. We also review the operational challenges and statistical considerations associated with such designs and propose clinical development strategies to successfully implement adaptive designs. The article also proposes instances where adaptive trial designs are particularly beneficial, and other situations where they may not be very useful.

Sample size adaptation designs and efficiency comparison with group sequential designs.

This study is to give a systematic account of sample size adaptation designs (SSADs) and to provide direct proof of the efficiency advantage of general SSADs over group sequential designs (GSDs) from a different perspective. For this purpose, a class of sample size mapping functions to define SSADs is introduced. Under the two-stage adaptive clinical trial setting, theorems are developed to describe the properties of SSADs. Sufficient conditions are derived and used to prove analytically that SSADs based on the weighted combination test can be uniformly more efficient than GSDs in a range of likely values of the true treatment difference δ $$ \delta $$ . As shown in various scenarios, given a GSD, a fully adaptive SSAD can be obtained that has sufficient statistical power similar to that of the GSD but has a smaller average sample size for all δ $$ \delta $$ in the range. The associated sample size savings can be substantial. A practical design example and suggestions on the steps to find efficient SSADs are also provided.

How Much More Efficient Are Adaptive Platform Trials Than Multiple Stand-Alone Trials? A Comprehensive Simulation Study for Streamlining Drug Development During a Pandemic.

With the coronavirus disease 2019 (COVID-19) pandemic, there is growing interest in utilizing adaptive platform clinical trials (APTs), in which multiple drugs are compared with a single common control group, such as a placebo or standard-of-care group. APTs evaluate several drugs for one disease and accept additions or exclusions of drugs as the trials progress; however, little is known about the efficiency of APTs over multiple stand-alone trials. In this study, we simulated the total development period, total sample size, and statistical operating characteristics of APTs and multiple stand-alone trials in drug development settings for hospitalized patients with COVID-19. Simulation studies using selected scenarios reconfirmed several findings regarding the efficiency of APTs. The APTs without staggered addition of drugs showed a shorter total development period than stand-alone trials, but the difference rapidly diminished if patient's enrollment was accelerated during the trials owing to the spread of infection. APTs with staggered addition of drugs still have the possibility of reducing the total development period compared with multiple stand-alone trials in some cases. Our study demonstrated that APTs could improve efficiency relative to multiple stand-alone trials regarding the total development period and total sample size without undermining statistical validity; however, this improvement varies depending on the speed of patient enrollment, sample size, presence/absence of family-wise error rate adjustment, allocation ratio between drug and placebo groups, and interval of staggered addition of drugs. Given the complexity of planning and implementing APT, the decision to implement APT during a pandemic must be made carefully.

Power Considerations in Designing and Interpreting Adaptive Clinical Trials.

Adaptive clinical trials allow researchers to make preplanned modifications based on accumulating data from an ongoing trial while preserving the trial's integrity and validity. These modifications may include early termination in cases of successes or lack of efficacy, refining the sample size, altering treatments or doses, or focusing recruitment efforts on individuals most likely to benefit. In this issue of NEJM Evidence, Geisler et al.1 report results from the Apixaban for Treatment of Embolic Stroke of Undetermined Source (ATTICUS) trial, a multicenter randomized trial of apixaban compared with aspirin in patients with cardioembolism risk factors.

A systematic survey of adaptive trials shows substantial improvement in methods is needed.

OBJECTIVES: To investigate the design, conduct, and analysis of adaptive trials through a systematic survey and provide recommendations for future adaptive trials. STUDY DESIGN AND SETTING: We systematically searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases up to January 2020. We included trials that were self-described as adaptive trials or applied adaptive designs. We identified three frequently used adaptive designs and summarized their methodological details in terms of design, conduct, and analysis. Lastly, we provided recommendations for future adaptive trials. RESULTS: We included a total of 128 trials in this study. The primary motivations for using adaptive design were to speed up the trials and facilitate decision-making (n = 29, 31.5%). The three most frequently used methods were group sequential design (GSD) (n = 71, 55.5%), adaptive dose-finding design (ADFD) (n = 35, 27.3%), and adaptive randomization design (ARD) (n = 26, 20.3%). The timing and frequency of interim analysis were detailed in three-fourths of the GSD trials (n = 55, 77.5%) and in half of the ADFD trials (n = 19, 54.3%); however, more than half of the ARD trials (n = 15, 57.7%) did not provide this information. Some trials selected a different outcome than the primary outcome for interim analysis (GSD: n = 7, 12.7%; ADFD: n = 8, 27.6%; ARD: n = 7, 50.0%), but the majority of these trials did not provide explicit reasons for this choice (GSD: n = 7, 100.0%; ADFD: n = 7, 87.5%; ARD: n = 5, 71.4%). More than half (n = 76, 59.4%) of trials did not mention the accessibility of supporting documents, and two-thirds (n = 86, 67.2%) did not state the establishment of independent data monitoring committees (IDMCs). Moreover, unplanned adjustments were observed during the conduct of one-sixth adaptive trials (n = 22, 17.2%). Based on our findings, we provide 14 recommendations for improving adaptive trials in the future. CONCLUSION: Substantial improvements were needed in methods of adaptive trials, particularly in the areas of interim analysis, the establishment of independent data monitoring committees, and unplanned adjustments. In this study, we offer recommendations from both general and specific aspects for researchers to carefully design, conduct, and analyze adaptive trials.

Adaptive phase I-II clinical trial designs identifying optimal biological doses for targeted agents and immunotherapies.

Targeted agents and immunotherapies have revolutionized cancer treatment, offering promising options for various cancer types. Unlike traditional therapies the principle of "more is better" is not always applicable to these new therapies due to their unique biomedical mechanisms. As a result, various phase I-II clinical trial designs have been proposed to identify the optimal biological dose that maximizes the therapeutic effect of targeted therapies and immunotherapies by jointly monitoring both efficacy and toxicity outcomes. This review article examines several innovative phase I-II clinical trial designs that utilize accumulated efficacy and toxicity outcomes to adaptively determine doses for subsequent patients and identify the optimal biological dose, maximizing the overall therapeutic effect. Specifically, we highlight three categories of phase I-II designs: efficacy-driven, utility-based, and designs incorporating multiple efficacy endpoints. For each design, we review the dose-outcome model, the definition of the optimal biological dose, the dose-finding algorithm, and the software for trial implementation. To illustrate the concepts, we also present two real phase I-II trial examples utilizing the EffTox and ISO designs. Finally, we provide a classification tree to summarize the designs discussed in this article.

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.

Considerations for identifying the "right" subgroup in adaptive enrichment trials.

Adaptive Enrichment Trials aim to make efficient use of data in a pivotal trial of a new targeted therapy to both (a) more precisely identify who benefits from that therapy and (b) improve the likelihood of successfully concluding that the drug is effective, while controlling the probability of false positives. There are a number of frameworks for conducting such a trial and decisions that must be made regarding how to identify that target subgroup. Among those decisions, one must choose how aggressively to restrict enrollment criteria based on the accumulating evidence in the trial. In this article, we empirically evaluate the impact of aggressive versus conservative enrollment restrictions on the power of the trial to detect an effect of treatment. We identify that, in some cases, a more aggressive strategy can substantially improve power. This additionally raises an important question regarding label indication: To what degree do we need a formal test of the hypothesis of no treatment effect in the exact population implied by the label indication? We discuss this question and evaluate how our answer for adaptive enrichment trials may relate to the answer implied by current practice for broad eligibility trials.

Borrowing Concurrent Information from Non-Concurrent Control to Enhance Statistical Efficiency in Platform Trials.

A platform trial is a trial involving an innovative adaptive design with a single master protocol to efficiently evaluate multiple interventions. It offers flexible features such as dropping interventions for futility and adding new interventions to be evaluated during the course of a trial. Although there is a consensus that platform trials can identify beneficial interventions with fewer patients, less time, and a higher probability of success than traditional trials, there remains debate on certain issues, one of which is whether (and how) the non-concurrent control (NCC) (i.e., patients in the control group recruited prior to the new interventions) can be combined with the current control (CC) in the analysis, especially if there is a change of standard of care during the trial. METHODS: In this paper, considering time-to-event endpoints under the proportional hazard model assumption, we introduce a new concept of NCC concurrent observation time (NCC COT), and propose to borrow NCC COT through left truncation. This assumes that the NCC COT and CC are comparable. If the protocol does not prohibit NCC patients to change the standard of care while on study, NCC COT and CC likely will share the same standard of care. A simulated example is provided to demonstrate the approach. RESULTS: Using exponential distributions, the simulated example assumes that NCC COT and CC have the same hazard, and the treatment group has a lower hazard. The estimated HR comparing treatment to the pooled control group is 0.744 (95% CI 0.575, 0.962), whereas the comparison to the CC group alone is 0.755 (95% CI 0.566, 1.008), with corresponding p-values of 0.024 versus 0.057, respectively. This suggests that borrowing NCC COT can improve statistical efficiency when the exchangeability assumption holds. CONCLUSION: This article proposes an innovative approach of borrowing NCC COT to enhance statistical inference in platform trials under appropriate scenarios.

Adaptive Clinical Trials in Pediatric Critical Care: A Systematic Review.

OBJECTIVES: This systematic review investigates the use of adaptive designs in randomized controlled trials (RCTs) in pediatric critical care. DATA SOURCES: PICU RCTs, published between 1986 and 2020, stored in the www.PICUtrials.net database and MEDLINE, EMBASE, CENTRAL, and LILACS databases were searched (March 9, 2022) to identify RCTs published in 2021. PICU RCTs using adaptive designs were identified through an automated full-text screening algorithm. STUDY SELECTION: All RCTs involving children (< 18 yr old) cared for in a PICU were included. There were no restrictions to disease cohort, intervention, or outcome. Interim monitoring by a Data and Safety Monitoring Board that was not prespecified to change the trial design or implementation of the study was not considered adaptive. DATA EXTRACTION: We extracted the type of adaptive design, the justification for the design, and the stopping rule used. Characteristics of the trial were also extracted, and the results summarized through narrative synthesis. Risk of bias was assessed using the Cochrane Risk of Bias Tool 2. DATA SYNTHESIS: Sixteen of 528 PICU RCTs (3%) used adaptive designs with two types of adaptations used; group sequential design and sample size reestimation. Of the 11 trials that used a group sequential adaptive design, seven stopped early due to futility and one stopped early due to efficacy. Of the seven trials that performed a sample size reestimation, the estimated sample size decreased in three trials and increased in one trial. CONCLUSIONS: Little evidence of the use of adaptive designs was found, with only 3% of PICU RCTs incorporating an adaptive design and only two types of adaptations used. Identifying the barriers to adoption of more complex adaptive trial designs is needed.

Protocol for a cohort study to evaluate the effectiveness and cost-effectiveness of general population screening for cardiovascular disease: the Viborg Screening Programme (VISP).

INTRODUCTION: The prevalence of cardiovascular disease (CVD) is increasing. Furthermore, asymptomatic individuals may not receive timely preventive initiatives to minimise the risk of further CVD events. Paradoxically, 80% of CVD events are preventable by early detection, followed by prophylactic initiatives. Consequently, we introduced the population-based Viborg Screening Programme (VISP) for subclinical and manifest CVD, focusing on commonly occurring, mainly asymptomatic conditions, followed by prophylactic initiatives.The aim of the VISP was to evaluate the health benefits, harms and cost-effectiveness of the VISP from a healthcare sector perspective. Furthermore, we explored the participants' perspectives. METHODS AND ANALYSIS: From August 2014 and currently ongoing, approximately 1100 men and women from the Viborg municipality, Denmark, are annually invited to screening for abdominal aortic aneurysm, peripheral arterial disease, carotid plaque, hypertension, diabetes mellitus and cardiac arrhythmia on their 67th birthday. A population from the surrounding municipalities without access to the VISP acts as a control. The VISP invitees and the controls are followed on the individual level by nationwide registries. The primary outcome is all-cause mortality, while costs, hospitalisations and deaths from CVD are the secondary endpoints.Interim evaluations of effectiveness and cost-effectiveness are planned every 5 years using propensity score matching followed by a Cox proportional hazards regression analysis by the 'intention-to-treat' principle. Furthermore, censoring-adjusted incremental costs, life-years and quality-adjusted life-years are estimated. Finally, the participants' perspectives are explored by semistructured face-to-face interviews, with participant selection representing participants with both negative and positive screening results. ETHICS AND DISSEMINATION: The VISP is not an interventional trial. Therefore, approval from a regional scientific ethical committee is not needed. Data collection from national registries was approved by the Regional Data Protection Agency (record no. 1-16-02-232-15). We ensure patient and public involvement in evaluating the acceptability of VISP by adopting an interviewing approach in the study. TRIAL REGISTRATION NUMBER: NCT03395509.

Adaptive clinical trials and master protocols.

Methodologies for randomized, double-blind, placebo-controlled clinical trials continue to develop in concert with evolving scientific and translational knowledge. Adaptive trial designs, in which data generated during the study are used to modify subsequent study activity (i.e., sample sizes, entry criteria, or outcomes), can optimize flexibility and expedite the safety and efficacy assessments for interventions of interest. This chapter will summarize general designs, advantages, and pitfalls associated with adaptive clinical trials and compare their features with those of conventional trial designs. It will also review novel ways for which seamless designs and master protocols may improve trial efficiency while offering interpretable data.

Ensuring the Scientific Value and Feasibility of Clinical Trials: A Qualitative Interview Study.

BACKGROUND: Ethical and scientific principles require that clinical trials address an important question and have the resources needed to complete the study. However, there are no clear standards for review that would ensure that these principles are upheld. METHODS: We conducted semi-structured interviews with a convenience sample of nineteen experts in clinical trial design, conduct, and/or oversight to elucidate current practice and identify areas of need with respect to ensuring the scientific value and feasibility of clinical trials prior to initiation and while ongoing. We used a priori and grounded theory to analyze the data and constant comparative method to induce higher order themes. RESULTS: Interviewees perceived determination of scientific value as the responsibility of the investigator and, secondarily, other parties who review or oversee research. Interviewees reported that ongoing trials are rarely reevaluated due to emerging evidence from external sources, evaluation is complex, and there would be value in the development of standards for monitoring and evaluating evidence systematically. Investigators, IRBs, and/or data monitoring committees (DMCs) could undertake these responsibilities. Feasibility assessments are performed but are typically inadequate; potential solutions are unclear. CONCLUSIONS: There are three domains where current approaches are suboptimal and in which further guidance is needed. First, who has the responsibility for conducting scientific review, whether it be the investigator, IRB, and/or DMC is often unclear. Second, the standards for scientific review (e.g., appropriate search terms, data sources, and analytic plan) should be defined. Third, guidance is needed on the evaluation of ongoing studies in light of potentially new and evolving evidence, with particular reference to evidence from outside the trial itself.

Inference in response-adaptive clinical trials when the enrolled population varies over time.

A common assumption of data analysis in clinical trials is that the patient population, as well as treatment effects, do not vary during the course of the study. However, when trials enroll patients over several years, this hypothesis may be violated. Ignoring variations of the outcome distributions over time, under the control and experimental treatments, can lead to biased treatment effect estimates and poor control of false positive results. We propose and compare two procedures that account for possible variations of the outcome distributions over time, to correct treatment effect estimates, and to control type-I error rates. The first procedure models trends of patient outcomes with splines. The second leverages conditional inference principles, which have been introduced to analyze randomized trials when patient prognostic profiles are unbalanced across arms. These two procedures are applicable in response-adaptive clinical trials. We illustrate the consequences of trends in the outcome distributions in response-adaptive designs and in platform trials, and investigate the proposed methods in the analysis of a glioblastoma study.

Mobile App Interventions to Improve Medication Adherence Among Type 2 Diabetes Mellitus Patients: A Systematic Review of Clinical Trials.

BACKGROUND: Medication adherence in type 2 diabetes mellitus (T2DM) patients is often suboptimal resulting in complications. There has been a growing interest in using mobile apps for improving medication adherence. OBJECTIVE: The objective of this work was to systematically review the clinical trials that have used mobile app-based interventions in T2DM patients for improving medication adherence. METHODOLOGY: A systematic search was performed to identify published clinical trials between January 2008 and December 2020 in databases-PubMed, Cochrane Library, and Google Scholar. All studies were assessed for risk of bias using quality rating tool from the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Seven clinical studies having 649 participants were studied. The median sample size was 58 (range = 41-247) and the median age of participants was 53.2 (range = 48-69.4) years. All studies showed improvements in adherence; however, only three studies reported statically significant improvements in adherence measures. Selected studies were deemed as unclear in their risk of bias and the most common source of risk of bias among the studies was the absence of objective outcome assessment. CONCLUSIONS: Mobile apps appear to be effective interventions to help improve medication adherence in T2DM patients compared with conventional care strategies. The features of the App to improvise medical adherence cannot be defined based on the meta-analysis because of heterogeneity of study designs and less number of sample size. Systematically planned studies would set up applicability of mobile apps in the clinical management of T2DM.

Platform trials.

For the past few years, platform trials have experienced a significant increase, recently amplified by the COVID-19 pandemic. The implementation of a platform trial is particularly useful in certain pathologies, particularly when there is a significant number of drug candidates to be assessed, a rapid evolution of the standard of care or in situations of urgent need for evaluation, during which the pooling of protocols and infrastructure optimizes the number of patients to be enrolled, the costs, and the deadlines for carrying out the investigation. However, the specificity of platform trials raises methodological, ethical, and regulatory issues, which have been the subject of the round table and which are presented in this article. The round table was also an opportunity to discuss the complexity of sponsorship and data management related to the multiplicity of partners, funding, and governance of these trials, and the level of acceptability of their findings by the competent authorities.