Operational complexities in international clinical trials: a systematic review of challenges and proposed solutions.

OBJECTIVE: International trials can be challenging to operationalise due to incompatibilities between country-specific policies and infrastructures. The aim of this systematic review was to identify the operational complexities of conducting international trials and identify potential solutions for overcoming them. DESIGN: Systematic review. DATA SOURCES: Medline, Embase and Health Management Information Consortium were searched from 2006 to 30 January 2023. ELIGIBILITY CRITERIA: All studies reporting operational challenges (eg, site selection, trial management, intervention management, data management) of conducting international trials were included. DATA EXTRACTION AND SYNTHESIS: Search results were independently screened by at least two reviewers and data were extracted into a proforma. RESULTS: 38 studies (35 RCTs, 2 reports and 1 qualitative study) fulfilled the inclusion criteria. The median sample size was 1202 (IQR 332-4056) and median number of sites was 40 (IQR 13-78). 88.6% of studies had an academic sponsor and 80% were funded through government sources. Operational complexities were particularly reported during trial set-up due to lack of harmonisation in regulatory approvals and in relation to sponsorship structure, with associated budgetary impacts. Additional challenges included site selection, staff training, lengthy contract negotiations, site monitoring, communication, trial oversight, recruitment, data management, drug procurement and distribution, pharmacy involvement and biospecimen processing and transport. CONCLUSIONS: International collaborative trials are valuable in cases where recruitment may be difficult, diversifying participation and applicability. However, multiple operational and regulatory challenges are encountered when implementing a trial in multiple countries. Careful planning and communication between trials units and investigators, with an emphasis on establishing adequately resourced cross-border sponsorship structures and regulatory approvals, may help to overcome these barriers and realise the benefits of the approach. OPEN SCIENCE FRAMEWORK REGISTRATION NUMBER: osf-registrations-yvtjb-v1.

A cost benefits analysis of the adoption of system of rice intensification: Evidence from smallholder rice farmers within an innovation platform in Oluch irrigation scheme, Kenya.

In the wake of climate change and dwindling natural resources, system of rice intensification has been fronted as an approach to improve rice production in several countries. Besides the benefits such as improved rice productivity, reduced water usage that have widely been observed, there is need to quantify the economic benefits of system of rice intensification accrued to farmers, thereby promoting it as an innovation to improve livelihoods of rice farmers. This aim of this paper is to quantify the economic benefits of undertaking SRI among smallholder rice farmers. We introduced SRI among smallholder farmers in a rural setting in western Kenya, Oluch irrigation scheme, through an innovation platform approach. Over the period of four years (2016-2019), we quantify the benefits accrued to the uptake of the technology among adopters of the technology. Our comparisons are in reference to a baseline study conducted prior to the full-scale promotion of SRI in the study area. Our study findings reveal that the uptake of specific SRI practices increased by at least 30-80%, and acreage under rice farming increased by 50%. Besides, SRI required more production costs per acre (63% increase), although SRI had at least 28.6% higher return per shilling invested. Our findings underscore previous results in the literature that SRI is associated with not only productivity but also economic benefits justifying the need for scaling especially among smallholder farmers. Nonetheless, efficient approaches to scaling such promising technologies are necessary to enhance productivity and subsequently improve livelihoods.

Is health expenditure on immunisation associated with immunisation coverage in sub-Saharan Africa? A multicountry analysis, 2013-2017.

OBJECTIVES: The designing of contextually tailored sustainable plans to finance the procurement of vaccines and the running of appropriate immunisation programmes are necessary to address the high burden of vaccine-preventable diseases and low immunisation coverage in sub-Saharan Africa (SSA). We sought to estimate the minimum fraction of a country's health budget that should be invested in national immunisation programmes to achieve national immunisation coverage of 80% or greater depending on the context, with and without donors' support. DESIGN: Multicountry analysis of secondary data using retrieved publicly available data from the WHO, Global Alliance for Vaccines and Immunization (GAVI) and World Bank databases. SETTING: Data on 24 SSA countries, between 2013 and 2017. METHODS: We model the variations in immunisation coverage across the different SSA countries using a fractional logit model. Three different generalised linear models were fitted to explore how various explanatory variables accounted for the variability in each of the three different vaccines-measles-containing vaccine (MCV)1, diphtheria, pertussis, tetanus (DPT3) and BCG. RESULTS: We observed an association between current health expenditure (as a percentage of gross domestic product) and immunisation coverage for BCG (OR=1.01, 95% CI: 1.01 to 1.04, p=0.008) and DPT3 (OR=1.01, 95% CI: 1.0 to 1.02, p=0.020) vaccines. However, there was no evidence to indicate that health expenditure on immunisation (as a proportion of current health expenditure) could be a strong predictor of immunisation coverage (DPT, OR 0.96 (95% CI 0.78 to 1.19; p=0.702); BCG, OR 0.91 (0.69 to 1.19; p=0.492); MCV, OR 0.91 (0.69 to 1.19; p=0.482)). We demonstrate in selected countries that to achieve the GAVI target of 80% in the countries with low DPT3 coverage, health expenditure would need to be increased by more than 45%. CONCLUSIONS: There is a need to facilitate the development of strategies that support African countries to increase domestic financing for national immunisation programmes towards achieving 2030 targets for immunisation coverage.

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.

Bayesian modelling strategies for borrowing of information in randomised basket trials.

Basket trials are an innovative precision medicine clinical trial design evaluating a single targeted therapy across multiple diseases that share a common characteristic. To date, most basket trials have been conducted in early-phase oncology settings, for which several Bayesian methods permitting information sharing across subtrials have been proposed. With the increasing interest of implementing randomised basket trials, information borrowing could be exploited in two ways; considering the commensurability of either the treatment effects or the outcomes specific to each of the treatment groups between the subtrials. In this article, we extend a previous analysis model based on distributional discrepancy for borrowing over the subtrial treatment effects ('treatment effect borrowing', TEB) to borrowing over the subtrial groupwise responses ('treatment response borrowing', TRB). Simulation results demonstrate that both modelling strategies provide substantial gains over an approach with no borrowing. TRB outperforms TEB especially when subtrial sample sizes are small on all operational characteristics, while the latter has considerable gains in performance over TRB when subtrial sample sizes are large, or the treatment effects and groupwise mean responses are noticeably heterogeneous across subtrials. Further, we notice that TRB, and TEB can potentially lead to different conclusions in the analysis of real data.

Innovative trial approaches in immune-mediated inflammatory diseases: current use and future potential.

BACKGROUND: Despite progress that has been made in the treatment of many immune-mediated inflammatory diseases (IMIDs), there remains a need for improved treatments. Randomised controlled trials (RCTs) provide the highest form of evidence on the effectiveness of a potential new treatment regimen, but they are extremely expensive and time consuming to conduct. Consequently, much focus has been given in recent years to innovative design and analysis methods that could improve the efficiency of RCTs. In this article, we review the current use and future potential of these methods within the context of IMID trials. METHODS: We provide a review of several innovative methods that would provide utility in IMID research. These include novel study designs (adaptive trials, Sequential Multi-Assignment Randomised Trials, basket, and umbrella trials) and data analysis methodologies (augmented analyses of composite responder endpoints, using high-dimensional biomarker information to stratify patients, and emulation of RCTs from routinely collected data). IMID trials are now well-placed to embrace innovative methods. For example, well-developed statistical frameworks for adaptive trial design are ready for implementation, whilst the growing availability of historical datasets makes the use of Bayesian methods particularly applicable. To assess whether and how these innovative methods have been used in practice, we conducted a review via PubMed of clinical trials pertaining to any of 51 IMIDs that were published between 2018 and 20 in five high impact factor clinical journals. RESULTS: Amongst 97 articles included in the review, 19 (19.6%) used an innovative design method, but most of these were relatively straightforward examples of innovative approaches. Only two (2.1%) reported the use of evidence from routinely collected data, cohorts, or biobanks. Eight (9.2%) collected high-dimensional data. CONCLUSIONS: Application of innovative statistical methodology to IMID trials has the potential to greatly improve efficiency, to generalise and extrapolate trial results, and to further personalise treatment strategies. Currently, such methods are infrequently utilised in practice. New research is required to ensure that IMID trials can benefit from the most suitable methods.

Treatment allocation strategies for umbrella trials in the presence of multiple biomarkers: A comparison of methods.

Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers - equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.