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Improving the Design of Vector Control Trials


   School of Health & Wellbeing

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  Dr H Ferguson  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Vector-borne Diseases (VBDs) are a major source of global morbidity and mortality. Interventions for many VBDs are based on control of mosquito populations. Cluster Randomized Controlled Trial (cRCTs) are the gold standard approach for evaluating vector control; but the costs and logistics of implementing sufficiently large and appropriately powered cRCTs are often prohibitive.

Leading stakeholders including the WHO and the Gates Foundation have highlighted the need to identify and validate alternative, more flexible designs for vector control trials that would enable high quality evidence to be collected through simpler trial design. This project will address this need through investigation of the sensitivity of vector control trial outcomes to alterations of the standard cRCT framework and guidelines for vector control trials [1]; with emphasis on improving methodology for clinical trials of ecologically-dependent interventions. In particular the project will assess

i. Implications of varying trial duration on robustness of outcomes. 

ii. Sensitivity of trial outcomes to variation in ecological and epidemiological conditions

iii. Can epidemiological outcomes be reliably predicted from entomological indicator variables?

iv. How can methodology for sample size calculation in clinical trials be improved to better incorporate uncertainty arising from complex designs and ecological heterogeneity?

In combination, this will contribute to improving methodology for vector control trials by highlighting optimal designs for generalizability and reducing cost. More broadly it will generate improved methodology for incorporating environmental heterogeneity into the design of trials of interventions whose impacts vary across ecological contexts. Findings will be of most direct relevance to malaria in Africa; but will apply more widely to trials of other VBDs (dengue, Chikungunya, Zika) and other environmentally transmitted infectious diseases.

What will studentship encompass: Focussing on malaria transmission in Africa, this project will begin with an initial stage of quantitative evidence review and meta-analyses. The aim will be to review outcomes from high quality trials of the core interventions recommended for vector control, Insecticide Treated Nets and Indoor Residual Spraying, quantify the extent of variability between trial settings, and test for associations between trial outcomes and baseline ecological and epidemiological conditions using meta-analysis. The 1st year will also focus on building knowledge of vector control, trial design, evidence review and quantitative analyses. In the 2nd stage, these skills will be applied to assess the impact of varying vector control trial design on the robustness and generalizability of results through computational simulation, and assessment of relationships between epidemiological outcomes and entomological indicators. Findings will have direct relevance to global policy-making in vector control through generation of guidance on the strengths and risks of different trial designs.

This project will be co-supervised by Prof Heather Ferguson, Dr Paul Johnson and Prof Alex McConnachie in UoG, and Dr Samson Kiware at IHI. Prof Ferguson is co-chair of the WHO Vector Control Advisory Group and will provide guidance on mosquito vector control. Dr Johnson is a statistician with expertise in design and analysis of vector control trials, and will provide guidance on simulation modelling. Prof McConnachie is Professor of Clinical Trial Biostatistics and will advise on trial design. Dr Kiware leads the Advanced Statistics and Modelling Unit at IHI, and will provide training in malaria transmission modelling.

This project will primarily involve quantitative analysis and evidence review based at the University of Glasgow. There will be opportunity for a 3-4 month placement within the team of SK at IHI for specialist training in VBD transmission models, and observe how vector control methods are developed and evaluated in the field.

Candidates should hold , or expect to receive, a 2.1 or above Honours degree or Masters qualification with a substantial quantitative component; ideally with experience or knowledge of VBDs or epidemiology.

HOW TO APPLY

You are applying for a PhD studentship from the MRC TMRP DTP. A list of potential projects and the application form is available online at:

http://www.methodologyhubs.mrc.ac.uk/about/tmrp-doctoral-training-partnership/

Please complete the form fully. Incomplete forms will not be considered. CVs will not be accepted for this scheme.

Please apply giving details for your first choice project. You can provide details of up to two other TMRP DTP projects you may be interested in at section B of the application form.

Before making an application, applicants should contact the project primary supervisor to find out more about the project and to discuss their interests in the research.

The deadline for applications is 4pm (GMT) 18 February 2022. Late applications will not be considered.

Completed application forms must be returned to: [Email Address Removed]

Informal enquiries may be made to [Email Address Removed]


Funding Notes

Studentships are funded by the Medical Research Council (MRC) for 3 years. Funding will cover tuition fees at the UK rate only, a Research Training and Support Grant (RTSG) and stipend (stipend to include London Weighting where appropriate). We aim to support the most outstanding applicants from outside the UK and are able to offer a limited number of bursaries that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

References

1. World Health, Organization., How to Design Vector Control Efficacy Trials. 2017, Geneva.
2. Johnson, P.C.D., et al., Power analysis for generalized linear mixed models in ecology and evolution. Methods in Ecology and Evolution, 2015. 6(2): p. 133-142.
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