Studies on Anti-Infective-Treated Central Venous Catheters for Prevention of Catheter-Related Bloodstream Infections
Results from 18 studies comparing the risk of catheter-related bloodstream infection when using anti-infective-treated versus standard catheters for total parenteral nutrition or chemotherapy.
dat.nielweise2008
The data frame contains the following columns:
| study | numeric |
study number |
| authors | character |
study authors |
| year | numeric |
publication year |
| x1i | numeric |
number of CRBSIs in patients receiving an anti-infective catheter |
| t1i | numeric |
total number of catheter days for patients receiving an anti-infective catheter |
| x2i | numeric |
number of CRBSIs in patients receiving a standard catheter |
| t2i | numeric |
total number of catheter days for patients receiving a standard catheter |
The use of a central venous catheter may lead to a catheter-related bloodstream infection (CRBSI), which in turn increases the risk of morbidity and mortality. Anti-infective-treated catheters have been developed that are meant to reduce the risk of CRBSIs. Niel-Weise et al. (2008) conducted a meta-analysis of studies comparing infection risk when using anti-infective-treated versus standard catheters for total parenteral nutrition or chemotherapy. The results from 9 such studies are included in this dataset.
The dataset was used in the article by Stijnen et al. (2010) to illustrate various generalized linear mixed-effects models for the meta-analysis of incidence rates and incidence rate ratios (see ‘References’).
Niel-Weise, B. S., Stijnen, T., & van den Broek, P. J. (2008). Anti-infective-treated central venous catheters for total parenteral nutrition or chemotherapy: A systematic review. Journal of Hospital Infection, 69, 114–123.
Stijnen, T., Hamza, T. H., & Ozdemir, P. (2010). Random effects meta-analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data. Statistics in Medicine, 29, 3046–3067.
### copy data into 'dat' and examine data dat <- dat.nielweise2008 dat ### standard (inverse-variance) random-effects model res <- rma(measure="IRR", x1i=x1i, t1i=t1i, x2i=x2i, t2i=t2i, data=dat) print(res, digits=3) predict(res, transf=exp, digits=2) ### random-effects conditional Poisson model ## Not run: res <- rma.glmm(measure="IRR", x1i=x1i, t1i=t1i, x2i=x2i, t2i=t2i, data=dat, model="CM.EL") print(res, digits=3) predict(res, transf=exp, digits=2) ## End(Not run)
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