Download Group 5

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