Nothing

```
SetEffectSizesDependentOnPub <-
function(table,
binary=NA, mean.sd=NA,
higher.is.better=NA,
level=95,
binary.measure="RR", continuous.measure="SMD",
summary.measure="SMD", method="DL"){
# Assigns effects that are based on the CI of the summary effect across published studies.
#
# Args:
# table: the data set
# binary: True/False - The data is binary/count.
# mean.sd: T/F - The data is in the form of means and standard deviations.
# higher.is.better: T/R - Higher counts/effect sizes are desired.
# binary.measure: if(binary == TRUE),
# which effect size measure do we use for the conversion?
# "RR" = relative risk
# "OR" = odds ratio
# continuous.measure: if(binary == FALSE && mean.sd == TRUE),
# which effect size measure do we use for the conversion?
# "SMD" = standardized mean difference (default)
# "SMDH" = standardized mean difference w/o assuming equal population variances
# in the two groups
# summary.measure: Which effect size measure do we use for the summary effect?
# method: "DL" for the DerSimonian & Laird method (1996) (default)
# level: confidence level = 1 - alpha
# Returns: A list of effect sizes.
# Dependencies:
# Calls: FillInMissingEffectSizeDefaults(), CalculateSummaryEffect()
# ExtractPublishedStudies(), ConvertBinaryToEffectSize(), ConvertMeanSDToSMD(),
## Extract published studies
pub <- ExtractPublishedStudies(table)
## Convert count data to log RR and its variance for each study
if(binary==TRUE){
pub <- ConvertBinaryToEffectSize(pub, measure=binary.measure)
} else if(mean.sd==TRUE){
pub <- ConvertMeanSDToSMD(pub, measure=continuous.measure)
}
## Calculate summary effect across all published studies
pub.summary <- CalculateSummaryEffect(pub, summary.measure=summary.measure,
method=method, level=level)
if(binary == TRUE){
pub.effect <- pub.summary$exp.m
pub.effect.lcl <- pub.summary$exp.m.lcl
pub.effect.ucl <- pub.summary$exp.m.ucl
} else {
pub.effect <- pub.summary$m
pub.effect.lcl <- pub.summary$m.lcl
pub.effect.ucl <- pub.summary$m.ucl
}
# Calculate
halfdown <- 0.5 * (pub.effect + pub.effect.lcl)
halfup <- 0.5 * (pub.effect + pub.effect.ucl)
if(higher.is.better == TRUE){
vposcl <- pub.effect.ucl
poscl <- halfup
negcl <- halfdown
vnegcl <- pub.effect.lcl
} else {
vposcl <- pub.effect.lcl
poscl <- halfdown
negcl <- halfup
vnegcl <- pub.effect.ucl
}
current <- pub.effect
effect.sizes.list.pub.ci <- c(vposcl,poscl,current,negcl,vnegcl)
return(effect.sizes.list.pub.ci)
}
```

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