Intuitive Biostatistics

Matt Briggs reviews, in depth, an uncontrolled study of the effectiveness of Yoga and points out the paper really doesn't show more than regression to the mean. It is a fun read, and makes important points about how to read clinical papers.

This is the first in a series of case studies I'm creating to help scientists learn the fine points of data analysis.

One consequence of heart failure is that the heart gets larger (cardiomegaly). This is a physiological adaptation to allow the heart to pump enough blood to perfuse the organs. Eventually the heart grows so large it is not able to pump blood efficiently. Therefore it makes sense to seek a drug that would reduce cardiomegaly in heart failure.

This study tested such a drug in an animal model of heart failure. Half the rats were given the surgery to create heart failure, and half were given sham surgery. In each of those groups, half of the rats were injected with an experimental drug and half were injected with vehicle as a control. (The data are real, but the investigators prefer to remain anonymous).

Is the drug effective in blunting the increase in heart weight?

Think about how you would analyze the data before reading the case-study. Better, download the raw data and do your own analyses.

When you have thought about how you would analyze the data, read this 15 page case-study. Here is the corresponding Prism file. Please email me with your suggestions about this case study, or ideas for future case studies.
 

Each circle represents the spot where a rocket sent from Germany landed in London. Is there a pattern? It sure looks like it, but in fact the distribution is random, matching the distribution of the Poisson distribution. A chi-square test compares the expected and observed distributions  (RD Clarke, Journal of the Institute of Actuaries, vol. 72, 1946, p. 481). Tierny discusses psychological aspects of this example -- people are more likely to see patterns in random data when very stressed. 

"If you know twelve concepts about a given topic you will look like an expert to people who only know two or three."   Scott Adams, creator of Dilbert

Here is my attempt at explaining the twelve key concepts in statistics.

It has been published that beautiful people tend to have more daughters than sons. Andrew Gelman reviews these data in a very general article that really is about statistical thinking, and how easy it is to be mislead by statistics. The conclusion does not seem to be solid. 

Reference: American Scientist, 97:310-316, 2009

It turns out that flipping a coin is not always random!

The US Preventive Services Task Force has published new guidelines on use of mammograms to detect breast cancer, recommending that they begin at age 50 (rather than 40). Here is a summary in the New York Times, and comments by Matt Briggs (and more). Use this web calculator to play with the numbers. This is a good example to review the concepts of sensitivity and specificity, and false-positive and false-negative test results. This article, written by Dr. Daniel Frank for his patients, does a great job of explaining the big-picture and the concept of Number Needed to Treat (although he never actually uses that term).