Adaptive Designs for Clinical Trials: Application to Healthcare Epidemiology Research

Associations between obesity and hyperuricemia combing mendelian randomization with network pharmacology

Objective

Obesity has become a global health issue and a risk factor for hyperuricemia. However, the associations between obesity and hyperuricemia are sometimes confounding. In the present study, we performed mendelian randomization (MR) analysis to study their relationship and investigate the underlying mechanism by network pharmacology.

Method

Body mass index (BMI) and uric acid related to single nucleotide polymorphism were selected as instrumental variables for MR analysis. Three robust analytical methods are used for bidirectional MR analysis such as inverse-variance weighting, weighted median and MR-Egger regression. Then, we further performed sensitivity analysis to evaluate the horizontal pleiotropy, heterogeneities, and stability. The targets related to obesity and hyperuricemia were collected, screened and further conducted for Kyoto Encyclopedia of Genes and Genomes pathway enrichment to explore the mechanism of obesity and hyperuricemia using network pharmacology.

Results

The positive causality was indicated between BMI and hyperuricemia based on inverse variance-weighted analysis [odds ratio:1.23, 95% confidence interval: 1.11 to 1.30 for each standard deviation increase in BMI (4.6 kg/m2)]. Conversely, hyperuricemia did not influence BMI. 235 intersected targets from obesity and hyperuricemia were collected. Insulin resistance were the top 1 key target. The mechanism between obesity and hyperuricemia are associated with important pathways including adipocytokine signaling pathway, insulin resistance and cholesterol metabolism et al.

Conclusions

Our MR analysis supported the causal association between obesity and hyperuricemia based on availablegenome-wide association analysis summary statistics. Obesity leads to hyperuricemia via insulin resistance, which is a key link in the huge network pathways using network pharmacology.

The Future Ain't What It Used to Be…Out With the Old…In With the Better: Antibacterial Resistance Leadership Group Innovations

Clinical research networks conduct important studies that would not otherwise be performed by other entities. In the case of the Antibacterial Resistance Leadership Group (ARLG), such studies include diagnostic studies using master protocols, controlled phage intervention trials, and studies that evaluate treatment strategies or dynamic interventions, such as sequences of empiric and definitive therapies. However, the value of a clinical research network lies not only in the results from these important studies but in the creation of new approaches derived from collaborative thinking, carefully examining and defining the most important research questions for clinical practice, recognizing and addressing common but suboptimal approaches, and anticipating that the standard approaches of today may be insufficient for tomorrow. This results in the development and implementation of new methodologies and tools for the design, conduct, analyses, and reporting of research studies. These new methodologies directly impact the studies conducted within the network and have a broad and long-lasting impact on the field, enhancing the scientific value and efficiency of generations of research studies. This article describes innovations from the ARLG in diagnostic studies, observational studies, and clinical trials evaluating interventions for the prevention and treatment of antibiotic-resistant bacterial infections.

The Next Generation: Mentoring and Diversity in the Antibacterial Resistance Leadership Group

The Antibacterial Resistance Leadership Group (ARLG) Mentoring Program was established to develop and prepare the next generation of clinician-scientists for a career in antibacterial resistance research. The ARLG Diversity, Equity, and Inclusion Working Group partners with the Mentoring Committee to help ensure diversity and excellence in the clinician-scientist workforce of the future. To advance the field of antibacterial research while fostering inclusion and diversity, the Mentoring Program has developed a number of fellowships, awards, and programs, which are described in detail in this article.

Under the Hood: The Scientific Leadership, Clinical Operations, Statistical and Data Management, and Laboratory Centers of the Antibacterial Resistance Leadership Group

Developing and implementing the scientific agenda of the Antibacterial Resistance Leadership Group (ARLG) by soliciting input and proposals, transforming concepts into clinical trials, conducting those trials, and translating trial data analyses into actionable information for infectious disease clinical practice is the collective role of the Scientific Leadership Center, Clinical Operations Center, Statistical and Data Management Center, and Laboratory Center of the ARLG. These activities include shepherding concept proposal applications through peer review; identifying, qualifying, training, and overseeing clinical trials sites; recommending, developing, performing, and evaluating laboratory assays in support of clinical trials; and designing and performing data collection and statistical analyses. This article describes key components involved in realizing the ARLG scientific agenda through the activities of the ARLG centers.

Diagnostic Testing for Sepsis: A Systematic Review of Economic Evaluations

Introduction: Sepsis is a serious and expensive healthcare problem, when caused by a multidrug-resistant (MDR) bacteria mortality and costs increase. A reduction in the time until the start of treatment improves clinical results. The objective is to perform a systematic review of economic evaluations to analyze the cost-effectiveness of diagnostic methods in sepsis and to draw lessons on the methods used to incorporate antimicrobial resistance (AMR) in these studies. Material and Methods: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed, and the Consolidated Health Economic Evaluation Reporting standards (CHEERS) checklist was used to extract the information from the texts. Results: A total of 16 articles were found. A decision model was performed in 14. We found two ways to handle resistance while modelling: the test could identify infections caused by a resistant pathogen or resistance-related inputs, or outcomes were included (the incidence of AMR in sepsis patients, antibiotic use, and infection caused by resistant bacterial pathogens). Conclusion: Using a diagnostic technique to detect sepsis early on is more cost-effective than standard care. Setting a direct relationship between the implementation of a testing strategy and the reduction of AMR cases, we made several assumptions about the efficacy of antibiotics and the length-of-stay of patients.

Modern Clinician-initiated Clinical Trials to Determine Optimal Therapy for Multidrug-resistant Gram-negative Infections

Treatment options for multidrug-resistant (MDR) gram-negative infection are growing. However, postregistration, pragmatic, and clinician-led clinical trials in this field are few, recruit small sample sizes, and experience deficiencies in design and operations. MDR gram-negative therapeutic trials are often inefficient, only evaluating a single antibiotic or strategy at a time. Novel clinical trial designs offer potential solutions by attempting to obtain clinically meaningful conclusions at the end or during a trial, for many treatment strategies, simultaneously. An integrated, consensus approach to MDR gram-negative infection trial design is crucial.

The Bayesian Design of Adaptive Clinical Trials

This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.

Resist the Temptation of Response-Adaptive Randomization

Response-adaptive randomization (RAR) has recently gained popularity in clinical trials. The intent is noble: minimize the number of participants randomized to inferior treatments and increase the amount of information about better treatments. Unfortunately, RAR causes many problems, including (1) bias from temporal trends, (2) inefficiency in treatment effect estimation, (3) volatility in sample-size distributions that can cause a nontrivial proportion of trials to assign more patients to an inferior arm, (4) difficulty of validly analyzing results, and (5) the potential for selection bias and other issues inherent to being unblinded to ongoing results. The problems of RAR are most acute in the very setting for which RAR has been proposed, namely long-duration "platform" trials and infectious disease settings where temporal trends are ubiquitous. Response-adaptive randomization can eliminate the benefits that randomization, the most powerful tool in clinical trials, provides. Use of RAR is discouraged.

Adaptive interventions for optimizing malaria control: an implementation study protocol for a block-cluster randomized, sequential multiple assignment trial

Background

In the past two decades, the massive scale-up of long-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) has led to significant reductions in malaria mortality and morbidity. Nonetheless, the malaria burden remains high, and a dozen countries in Africa show a trend of increasing malaria incidence over the past several years. This underscores the need to improve the effectiveness of interventions by optimizing first-line intervention tools and integrating newly approved products into control programs. Because transmission settings and vector ecologies vary from place to place, malaria interventions should be adapted and readapted over time in response to evolving malaria risks. An adaptive approach based on local malaria epidemiology and vector ecology may lead to significant reductions in malaria incidence and transmission risk.

Methods/design

This study will use a longitudinal block-cluster sequential multiple assignment randomized trial (SMART) design with longitudinal outcome measures for a period of 3 years to develop an adaptive intervention for malaria control in western Kenya, the first adaptive trial for malaria control. The primary outcome is clinical malaria incidence rate. This will be a two-stage trial with 36 clusters for the initial trial. At the beginning of stage 1, all clusters will be randomized with equal probability to either LLIN, piperonyl butoxide-treated LLIN (PBO Nets), or LLIN + IRS by block randomization based on their respective malaria risks. Intervention effectiveness will be evaluated with 12 months of follow-up monitoring. At the end of the 12-month follow-up, clusters will be assessed for “response” versus “non-response” to PBO Nets or LLIN + IRS based on the change in clinical malaria incidence rate and a pre-defined threshold value of cost-effectiveness set by the Ministry of Health. At the beginning of stage 2, if an intervention was effective in stage 1, then the intervention will be continued. Non-responders to stage 1 PBO Net treatment will be randomized equally to either PBO Nets + LSM (larval source management) or an intervention determined by an enhanced reinforcement learning method. Similarly, non-responders to stage 1 LLIN + IRS treatment will be randomized equally to either LLIN + IRS + LSM or PBO Nets + IRS. There will be an 18-month evaluation follow-up period for stage 2 interventions. We will monitor indoor and outdoor vector abundance using light traps. Clinical malaria will be monitored through active case surveillance. Cost-effectiveness of the interventions will be assessed using Q-learning.

Discussion

This novel adaptive intervention strategy will optimize existing malaria vector control tools while allowing for the integration of new control products and approaches in the future to find the most cost-effective malaria control strategies in different settings. Given the urgent global need for optimization of malaria control tools, this study can have far-reaching implications for malaria control and elimination.

Trial registration

US National Institutes of Health, study ID NCT04182126. Registered on 26 November 2019.

The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.

The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design

Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.

Non-inferiority versus superiority trial design for new antibiotics in an era of high antimicrobial resistance: the case for post-marketing, adaptive randomised controlled trials

Antimicrobial resistance is one of the most important threats to global health security. A range of Gram-negative bacteria associated with high morbidity and mortality are now resistant to almost all available antibiotics. In this context of urgency to develop novel drugs, new antibiotics for multidrug-resistant Gram-negative bacteria (namely, ceftazidime-avibactam, plazomicin, and meropenem-vaborbactam) have been approved by regulatory authorities based on non-inferiority trials that provided no direct evidence of their efficacy against multidrug-resistant bacteria such as Enterobacteriaceae spp, Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Burkholderia cepacia, and Acinetobacter baumannii. The use of non-inferiority and superiority trials, and selection of appropriate and optimal study designs, remains a major challenge in the development, registration, and post-marketing implementation of new antibiotics. Using an example of the development process of ceftazidime-avibactam, we propose a strategy for a new research framework based on adaptive randomised clinical trials. The operational research strategy has the aim of assessing the efficacy of new antibiotics in special groups of patients, such as those infected with multidrug-resistant bacteria, who were not included in earlier phase studies, and for whom it is important to establish an appropriate standard of care.