METAxDATA: Kaitlyn Hair - A "Living" Evidence Framework for Alzheimer's Disease Studies
Channel: QUEST Center BIH
Date: 2019-10-23

This lightning talk was delivered on October 15th 2019 at the BIH QUEST Center METAxDATA event. 
Film production by Maximilian Alexander, www.luma-frame.com

Kaitlyn is a PhD student at the University of Edinburgh working with the CAMARADES group. Her research involves performing systematic reviews and meta-analyses of preclinical data to investigate how the experimental design and rigor of studies contributes to the observed treatment effects, which often do not translate into human clinical trial benefits. She is also interested in developing automated tools for evidence synthesis to accelerate and expand the scope of the "living" systematic review process.

A "Living" Evidence Framework for Alzheimer's Disease Studies
Alzheimer’s disease (AD) is a devastating and incurable neurodegenerative disorder. Numerous drug candidates have been reported to reduce AD phenotypes in preclinical animal models, yet have failed to translate into any meaningful benefits for patients in clinical trials. We can use systematic reviews and meta-analyses to summarise existing knowledge and assess the impact of study design variables on observed effects. Previous systematic reviews of the preclinical AD literature identified poor methodological rigor and low reporting of measures to mitigate risks of bias. Such shortcomings can lead to overestimates of treatment effect, threaten reproducibility, and contribute to translational failure. Despite the utility of systematic reviews to guide and improve the quality of future preclinical research, they are resource-intensive and often out of date by the time they are complete. Therefore, we have created a "living" framework for the continual synthesis and quality assessment of in vivo AD experiments using automation tools. We classified a sample of papers to provide instances to train a machine learning algorithm, which could then detect if a paper contained primary research in transgenic AD animal models. Using validated text-mining approaches, we have assessed reporting quality and have categorised studies based on the disease model(s), treatment(s), and outcome measure(s) reported. We have visualised these findings in a curated, publicly available online database. We expect that this will not only accelerate meta-research in this area, but provide an important resource for all stakeholders in AD research.