Professor

Department of Statistics &

Child and Family Research Institute

University of British Columbia

Here are my current/recent teaching materials, including course materials and other material potentially of interest to aspiring statisticians. Courses from my past include Biostatistics I, a more-or-less introductory level applied biostatistics course aimed at graduate students in the health sciences at the University of Calgary. Biostatistics II, is follow-up course covering generalized linear models. Lastly there are compact PDF-versions of standard statistical tables.

My collection of Sample Size Calculators provides a set of simple-to-use JavaScript utilities for doing basic sample size calculations. I also provide some resources for calculating model-adjusted survival curves, in particular an S function and SAS code for direct standardization (aka the "corrected group prognosis" approach).

While I've got your attention, I'll take the opportunity to share some of my statistical pet peeves.

There appears to be no standard name for such plots. They are widely
disliked in the statistical community, where they are sometimes referred
to as *dynamite plots* (though I prefer *pinhead plots* as more
descriptive).
Faculty in Biostatistics at Vanderbilt University have gone as far as banning
them altogether, stating:

*
Dynamite plots often hide important information. This is particularly
true of small or skewed data sets. Researchers are highly discouraged
from using them, and department members have the option to decline
participation in papers in which the lead author requires the use of
these plots.*.

* Data are precious and usually expensive; treat them nicely. Respect
them as individuals! *

Post-hoc power calculations are usually conducted to help inform the interpretation of non-significant results. Confidence intervals provide a more sound and simpler approach. The advice section of Russ Lenth's power and sample-size page provides expanded discussion of this and other issues, in particular rather scathing criticisms of the use of the "effect size" approach to sample size calculation.

Lastly there's the issue of model interpretation. Many statistical models, such as proportional hazards models and logistic regression models are parameterized in such a way that model parameters are not directly interpretable. For example, it's hard to explain what an odds ratio really means (beyond the mathematical formula), unless one is dealing with rare outcomes, in which case they approximate the more easily understood relative risk.

I've helped author a couple of papers on the topic, but I've recently come across the efforts of a group of Harvard researchers who've made a concerted attempt to provide a general framework for doing this. Implementations of there idea exist in STATA (the Clarify program) and in R (the Selig package). Please visit Imai, Kosuke, Gary King and Olivia Lau. 2005. "Zelig: Everyone's Statistical Software" to learn more.