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 )
prop1event | Proportion of successes in treatment group (proportion of outcome = yes). |
---|---|
grp1n | Treatment group sample size. |
prop2event | Proportion of successes in control group (proportion of outcome = yes). |
grp2n | Control group sample size. |
es.type | Type of effect size that should be returned.
|
study | 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.lo
and ci.hi
, the weight factor w
and the
total sample size totaln
.
If 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 Unpublished manuscript: George Mason University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