## Saturday, November 24, 2007

### Quick lesson: how does SAS calculate confidence intervals?

Occasionally, I'll go through the referrals to this site. One referral I found was "how does SAS calculate confidence intervals." Here's a brief explanation.

1. What kind of confidence intervals are you talking about? Regression coefficients, sample means, sample variances, prediction intervals, differences of sample means, ratios?
2. Once you've got that figured out, go to the SAS manual. If you have SAS, it's in the help online. Otherwise, you can probably find it on the web. Go to the documentation for the right procedure (depends on what confidence interval you are calculating). In there, there is a section on details and computational considerations. In those sections SAS details the statistical theory they use for all of their procedures as well as any methods they use to make the procedure more efficient. If that isn't enough, they give copious references.

Back to work for me.

## Wednesday, November 14, 2007

### Bayes is big

When I was in graduate school, Bayesian statistics was a small, but important, part of my statistical inference curriculum. When I graduated, I all but forgot it. But a couple of years ago, I saw the storm on the horizon, and started furious self-study, including theory and computation. A few months ago, that storm hit shore. At the Joint Statistical Meetings 2007, I got raised eyebrows when I told former colleagues that life was turning me into a Bayesian, but I also met some prominent figures in the Bayesian biostatistics movement.

And now it's happening again.

About a year ago, I predicted over at Derek Lowe's excellent blog that a drug development program based on a full Bayesian approach would be 10 years off, though drug safety would probably see the largest immediate application. I was wrong. The storm is creeping inland. Be ready for it.

## Wednesday, November 7, 2007

### When graphics fail

I love graphical methods, and think that biostatisticians ought to use them more (in many sectors of my industry, they are using graphical methods more and are advancing the field). However, there are times when graphics deceive us even if they are done correctly, such as when one is trying to compare two overlaid time series.

Junk charts shows a recent example from the NYT. I think this example also shows that creating truly illuminating graphics is both an art and a science.