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Assessing temporal stability of bioscience evidence base


Project Description

World-class bioscience is critically dependent on statistical methodologies allowing synthesis of available bioscience data for generating new biological understanding and development of policies and management strategies. One such methodology is a meta-analysis which allows to statistically combine estimates of the effect from multiple studies on the same topic. Over the last few decades meta-analysis had revolutionized many scientific fields, helping to establish evidence-based practices and to resolve seemingly contradictory research outcomes. However, the conduct of meta-analysis provides just a snapshot of the available evidence at a more or less arbitrary point in time whereas scientific evidence is not static and tends to change over time as more research on the topic accumulates. New studies may either strengthen or challenge the conclusions of previous reports, resulting in changes in the mean effect over time. If the above changes in cumulative evidence over time are rapid and of considerable magnitude, the conclusions of meta-analysis will strongly depend on when the review was conducted and the policy-relevant recommendations derived from these reviews will quickly go out of date. Worryingly, a growing number of studies in different research fields demonstrates that significant changes in the magnitude, statistical significance or even sign of the reported effects over time are common. The observed temporal changes in effects have been attributed to time-lag bias in publication of nonsignificant results, changes in methodology and statistical power of primary studies, or real temporal changes in biological effects.

The proposed project will undertake the first comprehensive analysis of patterns, causes and potential implications of temporal changes in effects in biosciences using the novel statistical methods developed by the project co-supervisor Prof. Kulinskaya as well as Big Data approach and text mining. Training will be provided in relevant research methodology, including meta-analysis, handling and analysis of datasets and statistical techniques.

The project will be jointly supervised by Prof. Julia Koricheva (School of Biological Sciences, Royal Holloway University of London) and Prof. Elena Kulinskaya (School of Computing Sciences, University of East Anglia). Prof. Koricheva is an ecologist with expertise in research synthesis and methods and applications of meta-analysis in ecology. Prof. Kulinskaya is a statistician with expertise in methods of meta-analysis and Big Data analysis.

Interviews are provisionally planned for the period 25th February to 8th March 2019.
Please follow the application link below to our school web page for full instructions on eligibility and how to apply

Funding Notes

The studentship award will cover the cost of institutional tuition fees and provide an annual tax-free living stipend with at the standard RCUK rate with London weighting. The amount is currently at £16,777pa for the 18/19 Academic Year

References

Dogo, S. H., Clark, A. & Kulinskaya, E. (2017). Sequential change detection and monitoring of temporal trends in random-effects meta-analysis. Research Synthesis Methods, 8, 220-235.

Gurevitch, J., Koricheva, J., Nakagawa, S. & Stewart, G. (2018). Meta-analysis and the science of research synthesis. Nature, 555, 175-182.

Jennions, M. D. & Møller, A. P. (2002). Relationships fade with time: a meta-analysis of temporal trends in publication in ecology and evolution. Proceedings of the Royal Society of London B, 269, 43-48.

Lehrer J. (2010). The truth wears off. Is there something wrong with the scientific method? The New Yorker https://www.newyorker.com/magazine/2010/12/13/the-truth-wears-off.

Leimu, R. & Koricheva, J. (2004). Cumulative meta-analysis: a new tool for detection of temporal trends and publication bias in ecology. Proceedings of the Royal Society B-Biological Sciences, 271, 1961-1966.

Monsarrat, P. & Vergnes, J.-N. (2018). The intriguing evolution of effect sizes in biomedical research over time: smaller but more often statistically significant. GigaScience, 7, 1-10.

How good is research at Royal Holloway, University of London in Biological Sciences?

FTE Category A staff submitted: 24.00

Research output data provided by the Research Excellence Framework (REF)

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