Blog Archives

Example 8.2: Digits of Pi, redux

July 12, 2010
By
Example 8.2: Digits of Pi, redux

In example 8.1, we considered some simple tests for the randomness of the digits of Pi. Here we develop a different test and implement it. If each digit appears in each place with equal and independent probability, then the places between recurrences...

Read more »

Example 8.1: Digits of Pi

July 6, 2010
By
Example 8.1: Digits of Pi

Do the digits of Pi appear in a random order? If so, the trillions of digits of Pi calculated can serve as a useful random number generator. This post was inspired by this entry on Matt Asher's blog. Generating pseudo-random numbers is a key piece o...

Read more »

Example 7.42: Testing the proportionality assumption

June 21, 2010
By
Example 7.42: Testing the proportionality assumption

In addition to the non-parametric tools discussed in recent entries, it's common to use proportional hazards regression, (section 4.3.1) also called Cox regression, in evaluating survival data.It's important in such models to test the proportionality a...

Read more »

Example 7.36: Propensity score stratification

May 10, 2010
By
Example 7.36: Propensity score stratification

In examples 7.34 and 7.35 we described methods using propensity scores to account for possible confounding factors in an observational study.In addition to adjusting for the propensity score in a multiple regression and matching on the propensity score...

Read more »

Example 7.35: Propensity score matching

May 3, 2010
By
Example 7.35: Propensity score matching

As discussed in example 7.34, it's sometimes preferable to match on propensity scores, rather than adjust for them as a covariate.SASWe use a suite of macros written by Jon Kosanke and Erik Bergstralh at the Mayo Clinic. The dist macro calculates the ...

Read more »

Example 7.34: Propensity scores and causal inference from observational studies

April 26, 2010
By
Example 7.34: Propensity scores and causal inference from observational studies

Propensity scores can be used to help make causal interpretation of observational data more plausible, by adjusting for other factors that may responsible for differences between groups. Heuristically, we estimate the probability of exposure, rather t...

Read more »

Example 7.33: Specifying fonts in graphics

April 19, 2010
By
Example 7.33: Specifying fonts in graphics

For interactive data analysis, the default fonts used by SAS and R are acceptable, if not beautiful. However, for publication, it may be important to manipulate the fonts. For example, it would be desirable for the fonts in legends, axis labels, or o...

Read more »

Example 7.31: Contour plot of BMI by weight and height

April 5, 2010
By
Example 7.31: Contour plot of BMI by weight and height

A contour plot is a simple way to plot a surface in two dimensions. Lines with a constant Z value are plotted on the X-Y plane.Typical uses include weather maps displaying "isobars" (lines of constant pressure), and maps displaying lines of constant e...

Read more »

Example 7.30: Simulate censored survival data

March 30, 2010
By
Example 7.30: Simulate censored survival data

To simulate survival data with censoring, we need to model the hazard functions for both time to event and time to censoring. We simulate both event times from a Weibull distribution with a scale parameter of 1 (this is equivalent to an exponential ra...

Read more »

Example 7.29: Bubble plots colored by a fourth variable

March 27, 2010
By
Example 7.29: Bubble plots colored by a fourth variable

In Example 7.28, we generated a bubble plot showing the relationship among CESD, age, and number of drinks, for women. An anonymous commenter asked whether it would be possible to color the circles according to gender. In the comments, we showed simp...

Read more »