# Rollin Brant's Home Page

#### Rollin Brant

Professor (Emeritus)

Department of Statistics

University of British Columbia

##### Quick Links

Sample Size Calculators
Up Down Design Utilities
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 pet peeves, statistical and otherwise.
### Pinhead (a.k.a. Dynamite) Plots

The use of plots like the following is wide-spread.

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.*.

This position is further supported at
this web-page, which also provides
a color display poster
(Beware of Dynamite!) which ends with the following admonishment.
* Data are precious and usually expensive; treat them nicely. Respect
them as individuals! *

### Stepwise Regression

Much to the chagrin of the statistical community, stepwise regression
remains the most widely followed approach to model selection in multiple
regression. The FAQ section of STATA's web-site provides a very helpful
discussion titled
Problems with Stepwise Regression,
with insightful comments
from Frank Harrell and Ronan Conroy.
A helpful introduction to alternate approaches can be found in the paper
Suggestions for presenting the results of data analyses
by
Anderson, David R., William A. Link, Douglas H. Johnson, and Kenneth P.
Burnham.
### Post hoc power calculations

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.

### Interpreting Statistical Models

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 Zelig package).
Please visit Zelig Project to learn more

### Email Etiquette

We're drowning in email. And the many hours we spend on it are
generating ever more work for our friends and colleagues. We can reverse
this spiral only by mutual agreement.
Please see this proposed Email Charter
reproduced from the (sadly defunct) emailcharter.org.