# Blog Archives

## Example 8.5: bubble plots part 3

September 14, 2010
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An anonymous commenter expressed a desire to see how one might use SAS to draw a bubble plot with bubbles in three colors, corresponding to a fourth variable in the data set. (x, y, z for bubble size, and the category variable.) In a previous entries...

## Example 8.4: Including subsetting conditions in output

September 7, 2010
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A number of analyses perform operations on subsets. Making it clear what observations have been excluded or included is helpful to include in the output.SASThe where statement (section A.6.3) is a powerful and useful tool for subsetting on the fly. (...

## Summer hiatus

August 2, 2010
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We're taking a break from posting for most of August. We'll be back in a month with new examples, including R- and SAS-applicable tricks and tools.Please drop us any ideas in the comments or by e-mail. We love feedback of any kind.

## Example 8.2: Digits of Pi, redux

July 12, 2010
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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...

## Example 8.1: Digits of Pi

July 6, 2010
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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...

## Example 7.42: Testing the proportionality assumption

June 21, 2010
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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...

## Example 7.36: Propensity score stratification

May 10, 2010
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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...

## Example 7.35: Propensity score matching

May 3, 2010
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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 ...

## Example 7.34: Propensity scores and causal inference from observational studies

April 26, 2010
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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...

## Example 7.33: Specifying fonts in graphics

April 19, 2010
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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...