You must have solid biostatistics skills to conduct and present research in epidemiology. Being able to perform statistical analysis using different programming languages is equally of paramount importance as public health works with enormous datasets.
Advanced statistics in Epidemiology (10 credits) lasted 7 weeks, which is the longest course we have had so far. In the first semester, we had Basic Statistics where we were introduced to applications of statistics in epidemiology and used SPSS for data analysis. Advanced Statistics is the second course in the second semester of the Epidemiology program.
The aim of this course was to increase our abilities to understand and critically evaluate statistical results that permeate epidemiology. The schedule was quite packed. We had morning lectures (9h-12h) followed by computer labs on two afternoons (13h-16h), and in-class workshops on two afternoons(13h-16h). We covered topics from simple regression to Poisson regression to survival analysis.
We used Stata for all data analysis and it was more hands-on as we also had two home exams that included datasets ranging from hyponatremia and weight loss/gain to cigarette smoking during pregnancy and the risk of giving birth to a low-birthweight baby. The recommended book for this course was Essential of Medical Statistics.
It can get a bit mundane and daunting to talk about hyponatremia in every different type of analysis we learn. It is also not easy to study a course continuously for 6 hours per day, 30 hours per week, 120 hours per month, which totals to 180 hours by the end of the course. However, this only made us semi-experts in Stata, in critically evaluating statistical results and in specifying models for different types of research questions and variables. I am certain any one of us can do subgroup analysis before our morning coffee (yes, that is a big deal!). Our final exam was 3-hours long (theory), and we were also required to interpret results based on several outputs from Stata.
This course promoted analytical and critical thinking in the evaluation of information. This was an essential course in the path to becoming an epidemiologist as we learned how to specify multivariable models based on the scientific question, assess the goodness of our specified model as well as present and explain results in multiple forms. I have a far greater appreciation for the contribution of statistical thinking in public health and in medical research.
Stay tuned, lovely people!
You can contact me at Nuhamin.Petros@stud.ki.se if you have any questions.