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Topic: Medicine of the future Reading: Harbron, Chris (2006). Statistics and the medicine of the future. Significance 3 (2), 66-68. Group 5: Shu Min, Yan Ling (Presenter), Yi Mou Outline • Development of medicine through the years • Development of technologies for personalized medicine • The role of statistics • Challenges • Future outlook Development of medicine Water Retention Development of medicine • One pill has to fit all • In trials of new drugs, when a small proportion react badly to them, the new drug will be rule out of use and unavailable to everyone Personalized medicine? Omics • The pharmaceutical industry is looking into the development of a range of new technologies known as the “omics” • Refers to a field of study in biology that ends in –omics, such as genomics or proteomics • Aim to understand mechanisms of disease and examine cell processes at a very detailed molecular level Omics • Genomic technology - identify associations of genes with any disease/drug responses 20,000 genes in the human genome • Proteomic technology -identify the proteins that result in the progression of the disease 3 million different human protein species Large datasets So how? The role of statistics • To organize, analyze and make inference from the data Challenges • Difficult to identify biases or outliers in the data of these small molecules • Multivariate predictive modelling • Efficiently process large quantities of data, adapt algorithms to cope with the size of the dataset Challenges – Interpretation of results “With so many different analytes, whether they be genes, proteins or metabolites, some false positives of highly significant associations are likely to appear by chance.” Challenges • Comparisons for the testing of multiple hypothesis (Classical method: Bonferroni method) • In medical testing, the false discovery rate is a more powerful test than the Bonferroni method. • The false discovery rate accepts that you will select some differences between groups as interesting and assesses the quality of these differences and their likelihood of being genuine. • Attach biological meaning to the statistical results The future • This area of personalized medicine has great potential • Difficult to find new drugs that are safe and effective for all • Development of omics technologies to ensure continuing improvements in medical treatment • Technical and practical challenges of handling complex data, challenge of interpreting and attaching biological meaning to these results • Collaboration of many disciplines Thank you! References • http://www.statisticshowto.com/conservative/ • https://www.fda.gov/downloads/ScienceResearch/SpecialTopics/Pers onalizedMedicine/UCM372421.pdf • http://www.surveysystem.com/signif.htm • https://www.ncbi.nlm.nih.gov/pubmed/24831050
