dat.chiarito2020: Studies Investigating the Efficacy of P2Y12-Inhibitors in...

In metadat: Meta-Analysis Datasets

Studies Investigating the Efficacy of P2Y12-Inhibitors in Atherosclerosis

Description

Data on baseline parameters, efficacy endpoints and risk-of-bias from nine studies.

Usage

dat.chiarito2020

Format

The data frame contains the following columns:

study	character	study identifier
year	numeric	publication year
p2y12	character	type of P2Y12 inhibitor
duration.months	numeric	study duration
p2y12.daily.dosage	character	daily dosage of P2Y12 inhibitor
aspirin.daily.dosage	character	daily dosage of aspirin
p2y12.daily.mg	numeric	daily dose of P2Y12 inhibitor (mg)
aspirin.daily.mg	numeric	daily dose of aspirin (mg)
baseline.age	numeric	mean age at baseline
baseline.males	numeric	proportion of males in study population
baseline.diabetes	numeric	proportion of diabetic patients in study population
baseline.hypertension	numeric	proportion of patients with hypertension in study population
baseline.dyslipidaemia	numeric	proportion of patients with dyslipidaemia in study population
p2y12.mi	numeric	number of patients with myocardial infarction in experimental group
p2y12.stroke	numeric	number of patients with stroke in experimental group
p2y12.death	numeric	number of deaths in experimental group
p2y12.vdeath	numeric	number of vascular deaths in experimental group
p2y12.total	numeric	total number of patients experimental group
aspirin.mi	numeric	number of patients with myocardial infarction in control group
aspirin.stroke	numeric	number of patients with stroke in control group
aspirin.death	numeric	number of deaths in control group
aspirin.vdeath	numeric	number of vascular deaths in control group
aspirin.total	numeric	total number of patients in control group
rob.R	factor	risk of bias arising from the randomization process (R)
rob.D	factor	risk of bias due to deviations from intended interventions (D)
rob.Mi	factor	risk of bias due to missing outcome data (Mi)
rob.Me	factor	risk of bias in measurement of the outcome (Me)
rob.S	factor	risk of bias in selection of the reported result (S)
rob.overall	factor	overall risk of bias

Details

Chiarito et al. (2020) reviewed studies investigating the efficacy of P2Y12-inhibitors (compared to aspirin) for the secondary prevention of myocardial infarction in patients with established atherosclerosis.

A diagnosis of atherosclerosis, the development of lesions in arteries, is a risk factor for myocardial infarction or stroke, and a range of therapies (including aspirin, other drugs, or surgery) are available. P2Y12-inhibitors are antiplatelet drugs and as such might serve as an alternative to the commonly used aspirin; it was of interest to review and summarize the current scientific evidence on the efficacy of this type of drug in comparison to aspirin. To this end, Chiarito et al. (2020) performed a systematic review and meta-analysis; the systematic review had been pre-registered (PROSPERO CRD42018115037), and the investigation included a risk-of-bias assessment of the nine studies included. Co-primary endpoints were myocardial infarction and stroke, secondary endpoints were death and vascular death.

Concepts

medicine, cardiology, odds ratios, risk-of-bias

Author(s)

Christian Röver, [e-mail]

Source

Chiarito, M., Sanz-Sánchez, J., Cannata, F., Cao, D., Sturla, M., Panico, C., Godino, C., Regazzoli, D., Reimers, B., De Caterina, R., Condorelli, G., Ferrante, G., & Stefanini, G. G. (2020). Monotherapy with a P2Y12 inhibitor or aspirin for secondary prevention in patients with established atherosclerosis: A systematic review and meta-analysis. The Lancet, 395(10235), 1487–1495. ⁠https://doi.org/10.1016/s0140-6736(20)30315-9⁠

References

Zapf, A., Röver, C. (2023). Metaanalyse. In J. Gertheiss, M. Schmid, & M. Spindler (Eds.), Moderne Verfahren der Angewandten Statistik. Berlin, Germany: Springer Spektrum. ⁠https://doi.org/10.1007/978-3-662-63496-7_19-1⁠

Examples

dat.chiarito2020

## Not run: 
library(metafor)
library(meta)
library(bayesmeta)

# show plain data on stroke
dat.chiarito2020[-5, c("study","p2y12.stroke","p2y12.total","aspirin.stroke","aspirin.total")]

# show event rates in both treatment groups
cbind.data.frame("study"   = dat.chiarito2020$study,
                 "p2y12"   = dat.chiarito2020$p2y12.stroke / dat.chiarito2020$p2y12.total,
                 "aspirin" = dat.chiarito2020$aspirin.stroke / dat.chiarito2020$aspirin.total)

# compute effect measures (log-ORs)
# (using the "escalc()" function from the "metafor" package)
es.stroke <- escalc(measure="OR",
                    ai=p2y12.stroke,   n1i=p2y12.total,
                    ci=aspirin.stroke, n2i=aspirin.total,
                    slab=study, subset=is.finite(p2y12.stroke),
                    data=dat.chiarito2020)

# show effect measures (log-ORs)
summary(es.stroke)

# show effect measures (ORs und 95 percent CI)
cbind("study" = es.stroke$study,
      exp(summary(es.stroke)[,c("yi","ci.lb","ci.ub")]))

# show data in a forest plot
forestplot(es.stroke, xlab="log-OR")
forestplot(es.stroke, expo=TRUE, xlog=TRUE, xlab="odds ratio (OR)")

############################################################
#  meta-analysis (frequentist, "metafor" package)

# using default settings (random-effects model, REML-estimator)
rma.uni(es.stroke)

# analysis using Paule-Mandel estimator and HKSJ intervals
rma01 <- rma.uni(es.stroke, method="PM", test="knha")

# show results
rma01

# illustrate in a forest plot
forest(rma01)
forest(rma01, atransf=exp)

############################################################
#  meta-analysis (frequentist, "meta" package)

# analogous analysis to above, using the "meta" package
meta01 <- metabin(event.e=p2y12.stroke,   n.e=p2y12.total,
                  event.c=aspirin.stroke, n.c=aspirin.total,
                  studlab=study, data=dat.chiarito2020,
                  subset=is.finite(p2y12.stroke),
                  sm="OR",
                  method.tau="PM",
                  method.random.ci="HK", method="Inverse")

meta01
summary(meta01)
forest(meta01)

############################################################
#  meta-analysis (Bayesian, "bayesmeta" package)

# analysis using default settings (uniform prior for mu und tau)
bma01 <- bayesmeta(es.stroke)

# show results
bma01

# show forest plots (for log-OR and OR)
forestplot(bma01, xlab="log-OR")
forestplot(bma01, exponentiate=TRUE, xlog=TRUE, xlab="OR")

## End(Not run)

metadat documentation built on April 29, 2026, 5:10 p.m.