A Repurposing Programme Evaluating Transdermal Oestradiol Patches for the Treatment of Prostate Cancer Within the PATCH and STAMPEDE Trials: Current Results and Adapting Trial Design

AIMS

Androgen deprivation therapy (ADT), usually achieved with luteinising hormone releasing hormone analogues (LHRHa), is central to prostate cancer management. LHRHa reduce both testosterone and oestrogen and are associated with significant long-term toxicity. Previous use of oral oestrogens as ADT was curtailed because of cardiovascular toxicity. Transdermal oestrogen (tE2) patches are a potential alternative ADT, supressing testosterone without the associated oestrogen-depletion toxicities (osteoporosis, hot flushes, metabolic abnormalities) and avoiding cardiovascular toxicity, and we here describe their evaluation in men with prostate cancer.

MATERIALS AND METHODS

The PATCH (NCT00303784) adaptive trials programme (incorporating recruitment through the STAMPEDE [NCT00268476] platform) is evaluating the safety and efficacy of tE2 patches as ADT for men with prostate cancer. An initial randomised (LHRHa versus tE2) phase II study (n = 251) with cardiovascular toxicity as the primary outcome measure has expanded into a phase III evaluation. Those with locally advanced (M0) or metastatic (M1) prostate cancer are eligible. To reflect changes in both management and prognosis, the PATCH programme is now evaluating these cohorts separately.

RESULTS

Recruitment is complete, with 1362 and 1128 in the M0 and M1 cohorts, respectively. Rates of androgen suppression with tE2 were equivalent to LHRHa, with improved metabolic parameters, quality of life and bone health indices (mean absolute change in lumbar spine bone mineral density of -3.0% for LHRHa and +7.9% for tE2 with an estimated difference between arms of 9.3% (95% confidence interval 5.3-13.4). Importantly, rates of cardiovascular events were not significantly different between the two arms and the time to first cardiovascular event did not differ between treatment groups (hazard ratio 1.11, 95% confidence interval 0.80-1.53; P = 0.54). Oncological outcomes are awaited.

FUTURE

Efficacy results for the M0 cohort (primary outcome measure metastases-free survival) are expected in the final quarter of 2023. For M1 patients (primary outcome measure - overall survival), analysis using restricted mean survival time is being explored. Allied translational work on longitudinal samples is underway.

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.

The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies

PURPOSE OF REVIEW

Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians.

RECENT FINDINGS

Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

Recent innovations in adaptive trial designs: A review of design opportunities in translational research

Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.

An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials

BACKGROUND AND OBJECTIVES

Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials.

METHODS

We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials.

RESULTS

We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs.

CONCLUSION

This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.

Measuring the effects of a personalized music intervention on agitated behaviors among nursing home residents with dementia: design features for cluster-randomized adaptive trial

Background

Agitated and aggressive behaviors (behaviors) are common in nursing home (NH) residents with dementia. Medications commonly used to manage behaviors have dangerous side effects. NHs are adopting non-pharmacological interventions to manage behaviors, despite a lack of effectiveness evidence and an understanding of optimal implementation strategies. We are conducting an adaptive trial to evaluate the effects of personalized music on behaviors. Adaptive trials may increase efficiency and reduce costs associated with traditional RCTs by learning and making modifications to the trial while it is ongoing.

Methods

We are conducting two consecutive parallel cluster-randomized trials with 54 NHs in each trial (27 treatment, 27 control). Participating NHs were recruited from 4 corporations which differ in size, ownership structure, geography, and residents’ racial composition. After randomization, there were no significant differences between the NHs randomized to each trial with respect to baseline behaviors, number of eligible residents, degree of cognitive impairment, or antipsychotic use. Agitated behavior frequency is assessed via staff interviews (primary outcome), required nursing staff conducted resident assessments (secondary outcome), and direct observations of residents (secondary outcome). Between the two parallel trials, the adaptive design will be used to test alternative implementation strategies, increasingly enroll residents who are likely to benefit from the intervention, and seamlessly conduct a stage III/IV trial.

Discussion

This adaptive trial allows investigators to estimate the impact of a popular non-pharmaceutical intervention (personalized music) on residents’ behaviors, under pragmatic, real-world conditions testing two implementation strategies. This design has the potential to reduce the research timeline by improving the likelihood of powered results, increasingly enrolling residents most likely to benefit from intervention, sequentially assessing the effectiveness of implementation strategies in the same trial, and creating a statistical model to reduce the future need for onsite data collection. The design may also increase research equity by enrolling and tailoring the intervention to populations otherwise excluded from research. Our design will inform pragmatic testing of other interventions with limited efficacy evidence but widespread stakeholder adoption because of the real-world need for non-pharmaceutical approaches.

{2a} Trial registration

ClinicalTrials.govNCT03821844. Registered on January 30, 2019. This trial registration meets the World Health Organization (WHO) minimum standard.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05620-y.

Clinical Trials for Idiopathic Pulmonary Fibrosis and the Role of Health Systems

We are in the midst of transformative innovation in health care delivery and clinical trials in idiopathic pulmonary fibrosis (IPF). Health systems are uniquely positioned at the crossroad of these shifting paradigms, equipped with the resources to expand the research pipeline in IPF through visionary leadership and targeted investments. The authors hope that by prioritizing development of health information technology, supporting a broader range of clinical trial designs, and cultivating broad stakeholder engagement, health systems will generate data to address knowledge-evidence-practice gaps in IPF. This will continue to improve the ability to deliver high-quality, safe, and effective care.

Design optimization for dose-finding trials: a review

Dose selection is one of the most difficult and crucial decisions to make during drug development. As a consequence, the dose-finding trial is a major milestone in the drug development plan and should be properly designed. This article will review the most recent methodologies for optimizing the design of dose-finding studies: all of them are based on the modeling of the dose-response curve, which is now the gold standard approach for analyzing dose-finding studies instead of the traditional ANOVA/multiple testing approach. We will address the optimization of both fixed and adaptive designs and briefly outline new methodologies currently under investigation, based on utility functions.

Ethics of alternative trial designs and methods in low-resource settings

This editorial introduces articles in this Special Issue, which are based on presentations given at the 2017 meeting of the Global Forum of Bioethics in Research meeting. The main themes presented at the meeting were the use of cluster randomized trials, stepped-wedge cluster randomized trials, and controlled human infection models in research conducted in low-resource settings. The editorial sets out which ethical issues may arise in the context of alternative trial designs and describes the articles in this issue that addresses some or more of the ethical issues, such as justification of the research design, risk-benefit evaluations and consent.

This is a platform alteration: a trial management perspective on the operational aspects of adaptive and platform and umbrella protocols

Background

There are limited research and literature on the trial management challenges encountered in running adaptive platform trials. This trial design allows both (1) the seamless addition of new research comparisons when compelling clinical and scientific research questions emerge, and (2) early stopping of accrual to individual comparisons that do not show sufficient activity without affecting other active comparisons. Adaptive platform design trials also offer many potential benefits over traditional trials, from faster time to accrual to contemporaneously recruiting multiple research comparisons, added flexibility to focus on more promising research comparisons via pre-planned interim analyses and potentially shorter time to primary results. We share here our experiences from a trial management perspective, highlighting the challenges and successes.

Methods

We evaluated the operational aspects of making changes to these adaptive platform trials and identified both common and trial-specific challenges. The operational steps and challenges linked to both the addition of new research comparisons and stopping recruitment following pre-planned interim analysis were considered in our evaluation.

Results

Specific operational challenges in these adaptive platform protocols, additional to those in traditional two-arm trials, were identified. Key lessons are presented describing some of the solutions and considerations over conducting these trials.

Careful consideration on the practicality of the protocol structure (modular versus single protocol), the longevity and continuity of trial oversight committees, and having clear clinical and scientific criteria for the addition of new research comparisons were identified as some of the most common challenges.

Conclusions

Understanding the operational complexities associated with running adaptive platform protocols is paramount for their conduct, adaptive platform trials offer an efficient model to run randomised controlled trials and we are continuing to work to reduce further the effort required from an operational perspective.

Trial registration

FOCUS4: ISRCTN Registry, ISRCTN90061546. Registered on 16 October 2013. STAMPEDE: ISRCTN Registry, ISRCTN78818544. Registered on 2 February 2004.

Changing platforms without stopping the train: experiences of data management and data management systems when adapting platform protocols by adding and closing comparisons

Background

There is limited research and literature on the data management challenges encountered in multi-arm, multi-stage platform and umbrella protocols. These trial designs allow both (1) seamless addition of new research comparisons and (2) early stopping of accrual to individual comparisons that do not show sufficient activity. FOCUS4 (colorectal cancer) and STAMPEDE (prostate cancer), run from the Medical Research Council Clinical Trials Unit (CTU) at UCL, are two leading UK examples of clinical trials implementing adaptive platform protocol designs. To date, STAMPEDE has added five new research comparisons, closed two research comparisons following pre-planned interim analysis (lack of benefit), adapted the control arm following results from STAMPEDE and other relevant trials, and completed recruitment to six research comparisons. FOCUS4 has closed one research comparison following pre-planned interim analysis (lack of benefit) and added one new research comparison, with a number of further comparisons in the pipeline. We share our experiences from the operational aspects of running these adaptive trials, focusing on data management.

Methods

We held discussion groups with STAMPEDE and FOCUS4 CTU data management staff to identify data management challenges specific to adaptive platform protocols. We collated data on a number of case report form (CRF) changes, database amendments and database growth since each trial began.

Discussion

We found similar adaptive protocol-specific challenges in both trials. Adding comparisons to and removing them from open trials provides extra layers of complexity to CRF and database development. At the start of an adaptive trial, CRFs and databases must be designed to be flexible and scalable in order to cope with the continuous changes, ensuring future data requirements are considered where possible. When adding or stopping a comparison, the challenge is to incorporate new data requirements while ensuring data collection within ongoing comparisons is unaffected. Some changes may apply to all comparisons; others may be comparison-specific or applicable only to patients recruited during a specific time period. We discuss the advantages and disadvantages of the different approaches to CRF and database design we implemented in these trials, particularly in relation to use and maintenance of generic versus comparison-specific CRFs and databases. The work required to add or remove a comparison, including the development and testing of changes, updating of documentation, and training of sites, must be undertaken alongside data management of ongoing comparisons. Adequate resource is required for these competing data management tasks, especially in trials with long follow-up. A plan is needed for regular and pre-analysis data cleaning for multiple comparisons that could recruit at different rates and periods of time. Data-cleaning activities may need to be split and prioritised, especially if analyses for different comparisons overlap in time.

Conclusions

Adaptive trials offer an efficient model to run randomised controlled trials, but setting up and conducting the data management activities in these trials can be operationally challenging. Trialists and funders must plan for scalability in data collection and the resource required to cope with additional competing data management tasks.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3322-7) contains supplementary material, which is available to authorized users.

Bayesian adaptive clinical trials of combination treatments

Randomized clinical trials (RCT) increasingly investigate combination therapies. Strong biological rationale or early clinical evidence commonly suggest that the effect of the combination treatment is importantly greater than the maximum effect of any of the individual treatments. While these relationships are commonly well-accepted, RCTs do not incorporate them into the design or analysis plans. We therefore propose a simple Bayesian framework for incorporating the known relationships that the effectiveness of a combination treatment exceeds that of any individual treatment, but does not necessarily exceed the sum of individual effects. We term the collation of these two relationships 'fractional additivity'. We performed a binary outcome simulation study of a response adaptive randomized three-arm clinical trial with treatment arms A, B, and A&B that allowed for dropping an inferior treatment arm and terminating the trial early for superiority during any of 4 interim analyses. We compared the Bayesian fractional additivity model to a conventional analysis by measuring the expected proportion of failures, sample size at trial termination, time to termination, and root mean squared error of final estimates. We also compared the fractional additivity model to a 'full additivity' model where the effect of A&B was assumed to be the sum of the effect of A and B. In simulation scenarios where important fractional additivity or full additivity existed, the Bayesian fractional additivity model yielded a 3-4% relative reduction in expected number of failures, and a 30%-50% relative reduction in sample size at trial termination compared to a conventional analysis. These results held true even when the Bayesian fractional additivity model employed a biased prior. The full additivity model had slightly higher gains, but too frequently terminated the trial at the first interim look. In scenarios where no or weak fractional additivity exists, the expected sample size and time to termination were similar for the Bayesian fractional additivity model with a moderately optimistic bias about fractional additivity and the conventional model. Lastly, the fractional additivity model generally yielded similar or lower root mean squared error compared to the other models. In conclusion, our proposed Bayesian fractional additivity model provides an efficient approach for estimating effects of combination treatments in clinical trials. The approach is not only highly applicable in adaptive clinical trials, but also provides added power in a conventional RCT.

Investigating interventions to increase uptake of HIV testing and linkage into care or prevention for male partners of pregnant women in antenatal clinics in Blantyre, Malawi: study protocol for a cluster randomised trial

BACKGROUND

Despite large-scale efforts to diagnose people living with HIV, 54% remain undiagnosed in sub-Saharan Africa. The gap in knowledge of HIV status and uptake of follow-on services remains wide with much lower rates of HIV testing among men compared to women. Here, we design a study to investigate the effect on uptake of HIV testing and linkage into care or prevention of partner-delivered HIV self-testing alone or with an additional intervention among male partners of pregnant women.

METHODS

A phase II, adaptive, multi-arm, multi-stage cluster randomised trial, randomising antenatal clinic (ANC) days to six different trial arms. Pregnant women accessing ANC in urban Malawi for the first time will be recruited into either the standard of care (SOC) arm (invitation letter to the male partner offering HIV testing) or one of five intervention arms offering oral HIV self-test kits. Three of the five intervention arms will additionally offer the male partner a financial incentive (fixed or lottery amount) conditional on linkage after self-testing with one arm testing phone call reminders. Assuming that 25% of male partners link to care or prevention in the SOC arm, six clinic days, with a harmonic mean of 21 eligible participants, per arm will provide 80% power to detect a 0.15 absolute difference in the primary outcome. Cluster proportions will be analysed by a cluster summaries approach with adjustment for clustering and multiplicity.

DISCUSSION

This trial applies adaptive methods which are novel and efficient designs. The methodology and lessons learned here will be important as proof of concept of how to design and conduct similar studies in the future. Although small, this trial will potentially present good evidence on the type of effective interventions for improving linkage into ART or prevention. The trial results will also have important policy implications on how to implement HIVST targeting male partners of pregnant women who are accessing ANC for the first time while paying particular attention to safety concerns. Contamination may occur if women in the intervention arms share their self-test kits with women in the SOC arm.

TRIAL REGISTRATION

ISRCTN, ID: 18421340 . Registered on 31 March 2016.

Personalized Comments on Challenges and Opportunities in Kidney Disease Therapeutics: The Glom-NExT Symposium

In the face of ever-increasing incidence and prevalence of kidney disease worldwide, the unmet need for new treatments is unprecedented. Precision medicine is defined as the use of modern technologies to identify mechanisms of diseases in individual patients, and thus deploy treatment using tailored, targeted approaches, in the hopes of avoiding unnecessary toxicities and complications. Is there a place for kidney disease therapeutics in this space? If so, what is required to make significant progress toward precision nephrology? To answer these critical questions, we present a series of personalized comments corresponding to the responses offered to these very questions during the Inaugural Glom-NExT Symposium held at Harvard Medical School on October 23, 2014, a national meeting focused exclusively on kidney disease therapeutics.

Strategies for clinical trials in type 1 diabetes

During the past one to two decades, substantial progress has been made in our understanding of the immunopathology of type 1 diabetes (T1D) and the potential for immune interventions that can alter the natural history of the disease. This progress has resulted from the use of standardized study designs, endpoints, and, to a certain extent, mechanistic analyses in intervention trials in the setting of new-onset T1D. To date, most of these trials have involved single-agent interventions but, increasingly, future trials will test therapeutic combinations that are based on a compelling scientific rationale and testable mechanistic hypotheses. These increasingly complex trials will benefit from novel trial designs (such as factorial or adaptive designs), enhanced clinical endpoints that more directly assess islet pathology (such as β-cell death assays and islet or pancreatic imaging), improved responder analyses, and sophisticated mechanistic assays that provide deep phenotyping of lymphocyte subsets, gene expression profiling, in vitro T cell functional assessments, and antigen-specific responses. With this developing armamentarium of enhanced trial designs, endpoints, and clinical and mechanistic response analyses, we can expect substantial progress in better understanding the breakdown in immunologic tolerance in T1D and how to restore it to achieve significant and long-lasting preservation of islet function.

Data management Redefined

Core perspectives on the traditional approach to CDM are rapidly changing and EDC and new eclincal initiatives are redefining the face of data management. Associated with EDC are not only the higher efficiencies, resulting in lower study costs, but its applications in key areas such as adaptive trials and clinical event adjudication; however the cost and effort involved in deployment and integration remain a deterrent. The role of the data manager may change to that of a data broker who manages the exchange of data from multiple sources, and semantic interoperability, data standards and data privacy will prove to be the defining factors. Simulation modeling, pharmacogenomics, personalized medicine and EHRs will no longer exist as silos and seamless data flows will be the drivers of healthcare solutions.