R Dataset / Package MASS / bacteria
Presence of Bacteria after Drug Treatments
Tests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia.
This data frame has 220 rows and the following columns:
presence or absence: a factor with levels
active/placebo: a factor with levels
hi/low compliance: a factor with levels
numeric: week of test.
subject ID: a factor.
a factor with levels
drug+, a re-coding of
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.
Dr Amanda Leach via Mr James McBroom.
Menzies School of Health Research 1999–2000 Annual Report. p.20. http://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.
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))
Dataset imported from https://www.r-project.org.
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