On May 3rd, 2017, I was released from the hospital following an overnight stay for the treatment of a pulmonary embolism. I am now almost fully recovered. I think this experience is a great opportunity to teach statistics through a real-life example. I learned three things from this experience.
Vastly different fields can have the same underlying statistical processes
So far I have worked almost exclusively with political science data. My research is about how to estimate a proportion from a sample and how to compare it to other proportions. When my doctor told me I might have a pulmonary embolism, I wanted to see the data for myself. So I read the journal articles, FDA case reports, and any data I could find to try to get an estimate for the chance I had a pulmonary embolism. What I quickly realized is that the data about adverse drug reactions had similarities with the political science data I was familiar working with. The data had issues with nonresponse bias and limitations due to unideal sample sizes. Although political science and pharmacology are very different fields the share similar kinds of statistical problems.
Bayesian statistics is a powerful tool in many fields
Through this process, I saw how Bayesian statistics could help solve a difficult and important problem. My doctor came by and saw me during my brief hospital stay. She talked about how while she knew that it was unlikely any random woman in her twenties would have a pulmonary embolism, but the details of my case suggested that the probability I had a pulmonary embolism was significant. In short, the Bayesian mindset is about incorporating your prior beliefs and adapting them in the presence of additional information. I don’t think my doctor used Bayes Theorem (the formal formula for estimating a probability given prior information), but she used Bayesian reasoning. She had initial beliefs about the cause of my symptoms, and she updated her beliefs when she got new information (like lab results). This is probably normal reasoning for a doctor trying to diagnose a patient, but it showed me how Bayesian statistics could be applied to other fields. A more formal use of Bayesian statistics would provide even better information to estimate probabilities. I always knew Bayesian statistics could be useful in other cases besides politics, but this experience showed me a new area I am interested in researching.
I am interested in other fields to apply statistics to besides politics
I wish I could have discovered my interest in biostatistics without a life-threating medical event, but I am glad. I was exposed to a problem that is important and would use some of the same techniques I was exposed to during my work on political science. While I still love political science statistics, I feel like I have now answered the question on what I can research in years where is no major election. I enjoyed reading clinical trials and studies and analyzing their statistics. Maybe someday I can even study how to improve statistical methods to prevent and diagnosis pulmonary embolisms like mine.
Six months after returning home from the hospital, I am grateful that God has found a way to use my PE for good.
This work by BrittanyAlexander is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International