bacteria {MASS} | 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
andy
.- ap
active/placebo: a factor with levels
a
andp
.- hilo
hi/low compliance: a factor with levels
hi
amdlo
.- week
numeric: week of test.
- ID
subject ID: a factor.
- trt
a factor with levels
placebo
,drug
anddrug+
, a re-coding ofap
andhilo
.
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
Dr Amanda Leach via Mr James McBroom.
References
Menzies School of Health Research 1999–2000 Annual Report. p.20. https://www.menzies.edu.au/icms_docs/172302_2000_Annual_report.pdf.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Examples
contrasts(bacteria$trt) <- structure(contr.sdif(3),
dimnames = list(NULL, c("drug", "encourage")))
## fixed effects analyses
## IGNORE_RDIFF_BEGIN
summary(glm(y ~ trt * week, binomial, data = bacteria))
summary(glm(y ~ trt + week, binomial, data = bacteria))
summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))
## IGNORE_RDIFF_END
# conditional random-effects analysis
library(survival)
bacteria$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)
## IGNORE_RDIFF_BEGIN
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))
## IGNORE_RDIFF_END