HinksEtAl2010: JIA example data

Description Usage Format Details Source References Examples

Description

Log odds ratios indicating association of a genetic variant (CCR5) with juvenile idiopathic arthritis (JIA).

Usage

1
data("HinksEtAl2010")

Format

The data frame contains the following columns:

study character publication identifier
year numeric publication year
country character country
or numeric odds ratio (OR)
or.lower numeric lower 95 percent confidence bound for OR
or.upper numeric upper 95 percent confidence bound for OR
log.or numeric logarithmic OR
log.or.se numeric standard error of logarithmic OR

Details

Results from a genetic association study (Hinks et al, 2010) were combined with data from two additional studies (Prahalad et al., 2006; Lindner et al., 2007) in order to determine the combined evidence regarding the association of a particular genetic marker (CCR5) with juvenile idiopathic arthritis (JIA).

Source

A. Hinks et al. Association of the CCR5 gene with juvenile idiopathic arthritis. Genes and Immunity, 11(7):584-589, 2010.

References

S. Prahalad et al. Association of two functional polymorphisms in the CCR5 gene with juvenile rheumatoid arthritis Genes and Immunity, 7:468-475, 2006.

E. Lindner et al. Lack of association between the chemokine receptor 5 polymorphism CCR5delta32 in rheumatoid arthritis and juvenile idiopathic arthritis. BMC Medical Genetics, 8:33, 2007.

C. Roever, G. Knapp, T. Friede. Hartung-Knapp-Sidik-Jonkman approach and its modification for random-effects meta-analysis with few studies. BMC Medical Research Methodology, 15:99, 2015.

Examples

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data("HinksEtAl2010")

## Not run: 
# perform meta analysis based on weakly informative half-normal prior:
bma01 <- bayesmeta(y      = HinksEtAl2010$log.or,
                   sigma  = HinksEtAl2010$log.or.se,
                   labels = HinksEtAl2010$study,
                   tau.prior = function(t){dhalfnormal(t,scale=1.0)})

# perform meta analysis based on slightly more informative half-normal prior:
bma02 <- bayesmeta(y      = HinksEtAl2010$log.or,
                   sigma  = HinksEtAl2010$log.or.se,
                   labels = HinksEtAl2010$study,
                   tau.prior = function(t){dhalfnormal(t,scale=0.5)})

# show heterogeneity posteriors:
par(mfrow=c(2,1))
plot(bma01, which=4, prior=TRUE, taulim=c(0,1))
plot(bma02, which=4, prior=TRUE, taulim=c(0,1))
par(mfrow=c(1,1))

# show heterogeneity estimates:
rbind("half-normal(1.0)"=bma01$summary[,"tau"],
      "half-normal(0.5)"=bma02$summary[,"tau"])
# show q-profile confidence interval for tau in comparison:
require("metafor")
ma03 <- rma.uni(yi=log.or, sei=log.or.se, slab=study, data=HinksEtAl2010)
confint(ma03)$random["tau",c("ci.lb","ci.ub")]
# show I2 values in the relevant range:
tau <- seq(0, 0.7, by=0.1)
cbind("tau"=tau,
      "I2" =bma01$I2(tau=tau))

# show effect estimates:
round(rbind("half-normal(1.0)" = bma01$summary[,"mu"],
            "half-normal(0.5)" = bma02$summary[,"mu"]), 5)

# show forest plot:
forestplot(bma02)
# show shrinkage estimates:
bma02$theta

## End(Not run)

bayesmeta documentation built on Sept. 8, 2017, 5:04 p.m.