Randomised trials at the level of the individual

No frugal innovation without frugal evaluation: the Global IDEAL Sub-Framework

Objective

The Global IDEAL Sub-Framework Study aimed to combine the intended effects of the 2009/2019 IDEAL (Idea, Development, Exploration, Assessment, Long-term study) Framework recommendations on evaluating surgical innovation with the vision outlined by the 2015 Lancet Commission on Global Surgery to provide recommendations for evaluating surgical innovation in low-resource environments.

Design

A mixture of methods including an online global survey and semistructured interviews (SSIs). Quantitative data were summarized with descriptive statistics and qualitative data were analyzed using the Framework Method.

Participants

Surgeons and surgical researchers from any country.

Main outcome measures

Findings were used to suggest the nature of adaptations to the IDEAL Framework to address the particular problems of evaluation in low-resource settings.

Results

The online survey yielded 66 responses representing experience from 40 countries, and nine individual SSIs were conducted. Most respondents (n=49; 74.2%) had experience evaluating surgical technologies across a range of life cycle stages. Innovation was most frequently adopted based on colleague recommendation or clinical evaluation in other countries. Four themes emerged, centered around: frugal innovation in technological development; evaluating the same technology/innovation in different contexts; additional methodologies important in evaluation of surgical innovation in low/middle-income countries; and support for low-income country researchers along the evaluation pathway.

Conclusions

The Global IDEAL Sub-Framework provides suggestions for modified IDEAL recommendations aimed at dealing with the special problems found in this setting. These will require validation in a stakeholder consensus forum, and qualitative assessment in pilot studies. From assisting researchers with identification of the correct evaluation stage, to providing context-specific recommendations relevant to the whole evaluation pathway, this process will aim to develop a comprehensive and applicable set of guidance that will benefit surgical innovation and patients globally.

A systematic methodology review of fluorescence-guided cancer surgery to inform the development of a core master protocol and outcome set

BACKGROUND

Fluorescence-guided precision cancer surgery may improve survival and minimize patient morbidity. Efficient development of promising interventions is however hindered by a lack of common methodology. This methodology review aimed to synthesize descriptions of technique, governance processes, surgical learning and outcome reporting in studies of fluorescence-guided cancer surgery to provide guidance for the harmonized design of future studies.

METHODS

A systematic search of MEDLINE, EMBASE and CENTRAL databases from 2016-2020 identified studies of all designs describing the use of fluorescence in cancer surgery. Dual screening and data extraction was conducted by two independent teams.

RESULTS

Of 13,108 screened articles, 426 full text articles were included. The number of publications per year increased from 66 in 2016 to 115 in 2020. Indocyanine green was the most commonly used fluorescence agent (391, 91.8%). The most common reported purpose of fluorescence guided surgery was for lymph node mapping (195, 5%) and non-specific tumour visualization (94, 2%). Reporting about surgical learning and governance processes incomplete. A total of 2,577 verbatim outcomes were identified, with the commonly reported outcome lymph node detection (796, 30%). Measures of recurrence (32, 1.2%), change in operative plan (23, 0.9%), health economics (2, 0.1%), learning curve (2, 0.1%) and quality of life (2, 0.1%) were rarely reported.

CONCLUSION

There was evidence of methodological heterogeneity that may hinder efficient evaluation of fluorescence surgery. Harmonization of the design of future studies may streamline innovation.

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.

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.

Thyroid cancer

The past 5-10 years have brought in a new era in the care of patients with thyroid cancer, with the introduction of transformative diagnostic and management options. Several international ultrasound-based thyroid nodule risk stratification systems have been developed with the goal of reducing unnecessary biopsies. Less invasive alternatives to surgery for low-risk thyroid cancer, such as active surveillance and minimally invasive interventions, are being explored. New systemic therapies are now available for patients with advanced thyroid cancer. However, in the setting of these advances, disparities exist in the diagnosis and management of thyroid cancer. As new management options are becoming available for thyroid cancer, it is essential to support population-based studies and randomised clinical trials that will inform evidence-based clinical practice guidelines on the management of thyroid cancer, and to include diverse patient populations in research to better understand and subsequently address existing barriers to equitable thyroid cancer care.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

The economic challenges of new drug development

The COVID-19 pandemic has witnessed highly successful efforts to produce effective vaccines and treatments at an unprecedented pace. This perspective discusses factors that made this possible, from long-term investments in research infrastructure to major government interventions that absorbed much of the risk from research and development. We discuss key economic obstacles in the discovery of new drugs for infectious diseases, from novel antibiotics to diseases that primarily affect the poor. The world's collective experience of the pandemic may present an opportunity to reform traditional economic models of drug discovery to better address unmet needs. A tax-funded global institution could provide incentives for drug discovery based on their global health impact. International co-operation would be needed to agree and commit to adequate funding mechanisms, and the necessary political will would require strong public support. With the current heightened appreciation of the need for global health system resilience, there may be no better opportunity than now.

OUP accepted manuscript

Background

Rotarix (GlaxoSmithKline) oral rotavirus vaccine is licensed as 2 doses in the first 6 months of life. In settings with high child mortality rates, clinical protection conferred by 2 doses of Rotarix is reduced. We assessed vaccine immune response when an additional dose of Rotarix was given to Australian Aboriginal children 6 to <12 months old.

Methods

ORVAC is a 2-stage, double-blind, randomized, placebo-controlled trial. Australian Aboriginal children 6 to <12 months old who had received 1 or 2 prior doses of Rotarix rotavirus vaccine were randomized 1:1 to receive an additional dose of Rotarix or matched placebo. The primary immunological end point was seroresponse defined as an anti-rotavirus immunoglobulin A level ≥20 AU/mL, 28–56 days after the additional dose of Rotarix or placebo.

Results

Between March 2018 and August 2020, a total of 253 infants were enrolled. Of these, 178 infants (70%) had analyzable serological results after follow-up; 89 were randomized to receive Rotarix, and 89 to receive placebo. The proportion with seroresponse was 85% after Rotarix compared with 72% after placebo. There were no occurrences of intussusception or any serious adverse events.

Conclusions

An additional dose of Rotarix administered to Australian Aboriginal infants 6 to <12 months old increased the proportion with a vaccine seroresponse.

Clinical Trials Registration

NCT02941107.

How COVID-19 has fundamentally changed clinical research in global health

COVID-19 has had negative repercussions on the entire global population. Despite there being a common goal that should have unified resources and efforts, there have been an overwhelmingly large number of clinical trials that have been registered that are of questionable methodological quality. As the final paper of this Series, we discuss how the medical research community has responded to COVID-19. We recognise the incredible pressure that this pandemic has put on researchers, regulators, and policy makers, all of whom were doing their best to move quickly but safely in a time of tremendous uncertainty. However, the research community's response to the COVID-19 pandemic has prominently highlighted many fundamental issues that exist in clinical trial research under the current system and its incentive structures. The COVID-19 pandemic has not only re-emphasised the importance of well designed randomised clinical trials but also highlighted the need for large-scale clinical trials structured according to a master protocol in a coordinated and collaborative manner. There is also a need for structures and incentives to enable faster data sharing of anonymised datasets, and a need to provide similar opportunities to those in high-income countries for clinical trial research in low-resource regions where clinical trial research receives considerably less research funding.

Urgently seeking efficiency and sustainability of clinical trials in global health

This paper shows the scale of global health research and the context in which we frame the subsequent papers in the Series. In this Series paper, we provide a historical perspective on clinical trial research by revisiting the 1948 streptomycin trial for pulmonary tuberculosis, which was the first documented randomised clinical trial in the English language, and we discuss its close connection with global health. We describe the current state of clinical trial research globally by providing an overview of clinical trials that have been registered in the WHO International Clinical Trial Registry since 2010. We discuss challenges with current trial planning and designs that are often used in clinical trial research undertaken in low-income and middle-income countries, as an overview of the global health trials landscape. Finally, we discuss the importance of collaborative work in global health research towards generating sustainable and culturally appropriate research environments.