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<html><head><title>R: Presence of Bacteria after Drug Treatments</title>
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<table width="100%" summary="page for bacteria {MASS}"><tr><td>bacteria {MASS}</td><td align="right">R Documentation</td></tr></table>
<h2>Presence of Bacteria after Drug Treatments</h2>
<h3>Description</h3>
<p>
Tests of the presence of the bacteria
<EM>H. influenzae</EM>
in children with otitis media in the Northern Territory of Australia.
</p>
<h3>Usage</h3>
<pre>
bacteria
</pre>
<h3>Format</h3>
<p>
This data frame has 220 rows and the following columns:
<dl>
<dt>y</dt><dd>presence or absence: a factor with levels
<code>n</code> and <code>y</code>.</dd>
<dt>ap</dt><dd>active/placebo: a factor with levels <code>a</code> and <code>p</code>.</dd>
<dt>hilo</dt><dd>hi/low compliance: a factor with levels <code>hi</code> amd
<code>lo</code>.</dd>
<dt>week</dt><dd>numeric: week of test.</dd>
<dt>ID</dt><dd>subject ID: a factor.</dd>
<dt>trt</dt><dd>a factor with levels <code>placebo</code>, <code>drug</code> and
<code>drug+</code>, a re-coding of <code>ap</code> and <code>hilo</code>.</dd>
</dl>
<h3>Details</h3>
<p>
Dr A. Leach tested the effects of a drug on 50 children with a history of
otitis media in the Northern Territory of Australia. The children
were randomized to the drug or the a placebo, and also to receive
active encouragement to comply with taking the drug.
</p>
<p>
The presence of
<EM>H. influenzae</EM>
was checked at weeks 0, 2, 4, 6
and 11: 30 of the checks were missing and are not included in this
data frame.
</p>
<h3>Source</h3>
<p>
Menzies School of Health Research 1999–2000 Annual Report pp. 18–21
(<a href="http://www.menzies.edu.au/publications/anreps/MSHR00.pdf">http://www.menzies.edu.au/publications/anreps/MSHR00.pdf</a>).
</p>
<h3>References</h3>
<p>
Venables, W. N. and Ripley, B. D. (2002)
<EM>Modern Applied Statistics with S.</EM> Fourth edition. Springer.
</p>
<h3>Examples</h3>
<pre>
contrasts(bacteria$trt) <- structure(contr.sdif(3),
dimnames = list(NULL, c("drug", "encourage")))
## fixed effects analyses
summary(glm(y ~ trt * week, binomial, data = bacteria))
summary(glm(y ~ trt + week, binomial, data = bacteria))
summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))
# conditional random-effects analysis
library(survival)
bacteria$Time <- rep(1, nrow(bacteria))
coxph(Surv(Time, unclass(y)) ~ week + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID),
data = bacteria, method = "exact")
# PQL glmm analysis
library(nlme)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
</pre>
<hr><div align="center">[Package <em>MASS</em> version 7.2-44 <a href="00Index.html">Index]</a></div>
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