pub fn bacteria() -> Result<DataFrame, PolarsError>Expand description
§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:
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