What’s epidemiology? A definition with a side of SAS

I’ll be teaching a graduate course in epidemiology in the spring and giving a talk on biostatistics at SAS Global Forum in April, so I thought I’d jump ahead and start rambling on about it now.

When I tell people that I teach epidemiology, the first question I usually get is,

What’s epidemiology?

In short, epidemiology is quantifying disease. There are five ways (at least) statistics can be applied to the study of disease:

  1. How common is it? This is a question of prevalence (how likely you are to have it) and incidence (how likely you are to get it). If you think those two are the same, you should take a course in epidemiology. Or, if you are busy, you can just read my blog post tomorrow or this paper from the Western Users of SAS Software Conference by Chu and Xie (2013).
  2. What causes it? What are the factors that increase (or decrease) your risk of contracting a disease? My first thought here is PROC LOGISTIC. It’s not my only thought, but my first one.
  3. What pattern(s) does it follow? What is the prognosis? Are you likely to die of it quickly, eventually or never? To determine if a treatment is effective for cancer of the eyelashes, we need to first have an idea of what the probability of disability or death is when one is left untreated and over how long of a period of time, that is, what is the “natural progression” of a disease? PROC LIFETEST and PROC PHREG lead to mind here.
  4. How effective are attempts to prevent or treat a disease? Several options suggest themselves here – PHREG for comparing how long people survive under different conditions, LOGISTIC for testing for significant differences in the probability of death. You could even use ordinary least squares (OLS) regression methods if you were interested in a measure like quality of life scaled scores.
  5. Developing policies to minimize disease.

The last one might not sound like a strictly statistical task to you, but I would argue that it is, that a key feature, perhaps THE key feature of statistics, and what makes it different from pure mathematics, is the application to answer a question.

I would argue (and so would Leon Gordis, who, literally, wrote the book on Epidemiology ) that a major part of epidemiology is applying the results from those first four aspects to make decisions that benefit public health.

Why do developing countries have the types of public health problems that the U.S and western Europe had over 100 years ago? Because, due to public health programs like improved sanitation and vaccines taken by all the people who were not raised by morons, we have greatly reduced tuberculosis and diarrheal diseases.

Okay, back to work, and more later as I work on my class notes and assignments.


Speaking of random amazingness …. Jarrad Connor, who I follow on twitter tipped me off to the unintended virtual pandemic in World of Warcraft as a model of how disease spreads.

—- My Day Job –

I make games that make people smarter. Learn math, learn history. Figure out why there is a muskrat in the middle of this basket.


Put “AnnMaria said so” in the comments section when you buy a game and I’ll have our staff send you a login for the beta version of Forgotten Trail as a bonus.



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  1. Fascinating. I’ve been eyeball deep in Multiple Myeloma stats since my dad was diagnosed a few years ago. I am curious how prevalent you think data manipulation is in the healthcare industry. On one hand I’ve been taught that numbers never lie. On the other, I understand how easy it is to finesse or manipulate numbers to express whatever outcome a doctor/drug manufacturer/etc prefers.

  2. I would look for articles in academic journals. Most reputable journals require disclosure of any conflict of interest and I’d take those studies funded by a company that markets the product being studied with a large grain of salt. It doesn’t mean the results are NOT correct. I was the statistician on a drug company funded study once and I could have cared less who was funding it – but it was one study, not my entire livelihood or a huge amount of money to fund my entire research program. I think it would be really hard to be not at all affected under those circumstances, no matter how hard one tries. My other suggestion is that you would want to look not just at a single study but for a consensus in the literature. If 100 people, without any conflict of interest, all find the same thing, you can feel pretty confident.

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