HinksEtAl2010: JIA example data

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

JIA example data

Description

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

Usage

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/gene.2010.25")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/sj.gene.6364317")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/1471-2350-8-33")}.

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s12874-015-0091-1")}.

Examples

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 July 9, 2023, 5:12 p.m.