Compute effect size from binary proportions
esc_bin_prop( prop1event, grp1n, prop2event, grp2n, es.type = c("logit", "d", "g", "or", "r", "f", "eta", "cox.d"), study = NULL )
Proportion of successes in treatment group (proportion of outcome = yes).
Treatment group sample size.
Proportion of successes in control group (proportion of outcome = yes).
Control group sample size.
Type of effect size that should be returned.
Optional string with the study name. Using
The effect size
es, the standard error
se, the variance
of the effect size
var, the lower and upper confidence limits
ci.hi, the weight factor
w and the
total sample size
es.type = "r", Fisher's transformation for the effect size
r and their confidence intervals are also returned.
Lipsey MW, Wilson DB. 2001. Practical meta-analysis. Thousand Oaks, Calif: Sage Publications
Wilson DB. 2016. Formulas Used by the "Practical Meta-Analysis Effect Size Calculator". Unpublished manuscript: George Mason University
# effect size log odds esc_bin_prop(prop1event = .375, grp1n = 80, prop2event = .47, grp2n = 85)#> #> Effect Size Calculation for Meta Analysis #> #> Conversion: binary proportion to effect size logits #> Effect Size: -0.3930 #> Standard Error: 0.3171 #> Variance: 0.1006 #> Lower CI: -1.0146 #> Upper CI: 0.2285 #> Weight: 9.9448# effect size odds ratio esc_bin_prop(prop1event = .375, grp1n = 80, prop2event = .47, grp2n = 85, es.type = "or")#> #> Effect Size Calculation for Meta Analysis #> #> Conversion: binary proportion coefficient to effect size odds ratio #> Effect Size: 0.6750 #> Standard Error: 0.3171 #> Variance: 0.1006 #> Lower CI: 0.3626 #> Upper CI: 1.2567 #> Weight: 9.9448