KarnerEtAl2014: COPD example data

In bayesmeta: Bayesian Random-Effects Meta-Analysis and Meta-Regression

COPD example data

Description

Data on several endpoints from a systematic review in chronic obstructive pulmonary disease (COPD).

Usage

data("KarnerEtAl2014")

Format

The data frame contains the following columns:

study	character	publication identifier (first author and publication year)
year	numeric	publication year
duration	factor	study duration (<1 year vs. \geq 1 year)
inhaler	factor	type of inhaler investigated (“dry powder” or “soft mist”)
baseline.age	numeric	mean age at baseline
baseline.males	numeric	proportion of males among study participants
baseline.fev1	numeric	mean FEV1 at baseline (L)
baseline.fev1pp	numeric	mean FEV1 (percent of predicted) at baseline
baseline.pyr	numeric	mean number of pack-years (smoking history)
tiotropium.total	numeric	total number of patients in the treatment group
tiotropium.exa	numeric	number of patients with \geq 1 exacerbation in the treatment group
tiotropium.sexa	numeric	number of patients with \geq 1 severe exacerbation in the treatment group
tiotropium.hospi	numeric	number of patients with \geq 1 hospitalisation (all-cause) in the treatment group
tiotropium.deaths	numeric	number of deaths in the treatment group
tiotropium.sae	numeric	number of patients with \geq 1 serious adverse event (non-fatal) in the treatment group
tiotropium.dropout	numeric	number of withdrawals in the treatment group
placebo.total	numeric	total number of patients in the control group
placebo.exa	numeric	number of patients with \geq 1 exacerbation in the control group
placebo.sexa	numeric	number of patients with \geq 1 severe exacerbation in the control group
placebo.hospi	numeric	number of patients with \geq 1 hospitalisation (all-cause) in the control group
placebo.deaths	numeric	number of deaths in the control group
placebo.sae	numeric	number of patients with \geq 1 serious adverse event (non-fatal) in the control group
placebo.dropout	numeric	number of withdrawals in the control group
sgrq.md, sgrq.se	numeric	mean difference and associated standard error for St. George's respiratory questionnaire (SGRQ) total score
fev1.md, fev1.se	numeric	mean difference and associated standard error for forced expiratory volume in 1 second (FEV1, mL)

Details

Chronic obstructive pulmonary disease (COPD) is a chronic and progressive condition characterized by recurrent exacerbation phases. Various treatment options are available, aimed at both providing relief during an acute exacerbation, and at delaying overall disease progression. A common drug used in the management of COPD is tiotropium, a long-acting muscarinic antagonist (LAMA), which is administered via an inhaler device.

Karner et al. (2014) conducted a systematic review in order to evaluate the evidence on the effects of tiotropium in comparison to placebo. 22 placebo-controlled studies were found, and a range of endpoints and subgroups were considered. The data reproduced here relate to analyses 1.1, 1.9, 1.14, 1.15, 1.19, 1.26, 1.27 and 1.28 in the original investigation. A number of study-level covariables are also provided.

Source

C. Karner, J. Chong, P. Poole. Tiotropium versus placebo for chronic obstructive pulmonary disease. Cochrane Database of Systematic Reviews, 7:CD009285, 2014. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/14651858.CD009285.pub3")}.

Examples

data("KarnerEtAl2014")
## Not run: 
# compute effect sizes (log odds ratios) from exacerbation count data
# (using the "metafor" package's "escalc()" function):
karner.exa <- escalc(measure="OR",
                     ai=tiotropium.exa, n1i=tiotropium.total,
                     ci=placebo.exa,    n2i=placebo.total,
                     slab=study, data=KarnerEtAl2014)
# show forest plot:
forestplot(karner.exa, title="exacerbation",
           exponentiate=TRUE, xlog=TRUE,
           xlab="odds ratio")

# derive St. George's Respiratory Questionnaire (SGRQ) effect sizes:
karner.sgrq <- escalc(measure="MD",
                      yi=sgrq.md, sei=sgrq.se,
                      slab=study, data=KarnerEtAl2014,
                      subset=is.finite(KarnerEtAl2014$sgrq.md))
# show forest plot:
forestplot(karner.sgrq, title="SGRQ",
           xlab="mean difference")


## End(Not run)

bayesmeta documentation built on July 9, 2023, 5:12 p.m.