+------------+-------------------+
| bacteria   | R Documentation   |
+------------+-------------------+

Presence of Bacteria after Drug Treatments
------------------------------------------

Description
~~~~~~~~~~~

Tests of the presence of the bacteria *H. influenzae* in children with
otitis media in the Northern Territory of Australia.

Usage
~~~~~

::

    bacteria

Format
~~~~~~

This data frame has 220 rows and the following columns:

y
    presence or absence: a factor with levels ``n`` and ``y``.

ap
    active/placebo: a factor with levels ``a`` and ``p``.

hilo
    hi/low compliance: a factor with levels ``hi`` amd ``lo``.

week
    numeric: week of test.

ID
    subject ID: a factor.

trt
    a factor with levels ``placebo``, ``drug`` and ``drug+``, a
    re-coding of ``ap`` and ``hilo``.

Details
~~~~~~~

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.

The presence of *H. influenzae* was checked at weeks 0, 2, 4, 6 and 11:
30 of the checks were missing and are not included in this data frame.

Source
~~~~~~

Menzies School of Health Research 1999–2000 Annual Report pp. 18–21
(`http://www.menzies.edu.au/publications/anreps/MSHR00.pdf <http://www.menzies.edu.au/publications/anreps/MSHR00.pdf>`_).

References
~~~~~~~~~~

Venables, W. N. and Ripley, B. D. (2002) *Modern Applied Statistics with
S.* Fourth edition. Springer.

Examples
~~~~~~~~

::

    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))

