Biostatistics: Possible Remedy in the Era of Fake News?

Well before the debate of "Fake News" started trending, researchers at Stanford University started working on a project aimed at studying how well we evaluate the information, especially from online sources. Starting in early 2015 researchers studied the behaviour of students from schools and universities like Stanford for 18 months. In the summary of the report researchers summed up their disappointment by stating "in every case and at every level, we were taken aback by students’ lack of preparation." The participant did a pretty poor job in assessing the credibility of information and sources. Though it is unfortunate it might not be entirely shocking as it confirms the pattern we are observing around us.

In the era of social media journalism, the reliability of information appears to have taken the back seat. Facebook announced it's intention to crack down on fake news. Recently Twitter has also joined the call. Maybe few other companies will follow the suit. Though commendable initiative, it doesn't seem enough for the enormous scale of the problem. There are 85+ virtual communities worldwide with atleast a million registered users each (like Facebook and Twitter). Additionally, there are few dozen instant messaging services like Whatsapp. Making all these platforms accountable seems like practically an impossible task. And even if a lot of them implement some measure of regulation, can we trust these platforms with their self-moderation policies?

The Hierarchy of Evidence

I was fortunate enough to get to work with the data from cross-sectional studies, case control studies and cohort studies in my first job after the grad school. In my next job I was working with data from thousands of randomized control trials. Naturally, I was trying to draw parallels and understand the complete picture. So I signed up for a few MOOC courses which helped to connect the dots. 

This posts contains my notes from the courses.

When it comes to (public health) experiments we could categorize the experiments into two buckets.

Note: This is not a rigid, universal and a perfect classification. 

An observational study is a study where the researchers only collect the data based on observations and then make some inference based on it. The researchers have no control over the variables in an observational study.

An experiment is a method of applying some intervention to a group and recording the effects. The researchers have the control over the intervention being chosen for the study.

P-P plot vs Q-Q plot

P-P plot and Q-Q plot are called probability plots.

Probability plot helps us to compare two data sets in terms of distribution. Generally one set is theoretical and one set is empirical (not mandatory though).

The two types of probability plots are

• Q-Q plot (more common)
• P-P plot

Before getting in to details consider following,

Survival Analysis

While working on few assignments related to exploring disorder in cohort studies I came across the concept of Survival analysis. It seems very useful in many real life scenarios.

What is Survival analysis?

"Survival analysis is a branch of statistics which deals with analysis of time duration to until one or more events happen".[1] The event of interest can be development of a disease, failure of a mechanical system or a person getting married.

It is also called as the time to event analysis.

In survival analysis, subjects are generally followed over a certain time period and the focus is on the time at which the event of interest occurs.

Network Analysis application in Genetic Studies

Background

SNPs are DNA sequence variations in a population. For example DNA molecule 1 has C-G pair of alleles and DNA molecule 2 has T-A pair of alleles as shown below.

"Dna-SNP" by David Hall (Gringer) - Own work. Licensed under CC BY 2.5 via Wikimedia Commons.

In human genome there are around 3 billion base pairs like as we can see above. Variations in these affect traits like eye color, height and disorders like obesity, diabetes etc.