esc_bin_prop: Compute effect size from binary proportions

In esc: Effect Size Computation for Meta Analysis

Description

Compute effect size from binary proportions

Usage

esc_bin_prop(
  prop1event,
  grp1n,
  prop2event,
  grp2n,
  es.type = c("logit", "d", "g", "or", "r", "f", "eta", "cox.d"),
  study = NULL
)

Arguments

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. "d" returns standardized mean difference effect size d "f" returns effect size Cohen's f "g" returns adjusted standardized mean difference effect size Hedges' g "or" returns effect size as odds ratio "cox.or" returns effect size as Cox-odds ratio (see convert_d2or for details) "logit" returns effect size as log odds "cox.log" returns effect size as Cox-log odds (see convert_d2logit for details) "r" returns correlation effect size r "eta" returns effect size eta squared
study	Optional string with the study name. Using combine_esc or as.data.frame on esc-objects will add this as column in the returned data frame.

Value

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.

Note

If es.type = "r", Fisher's transformation for the effect size r and their confidence intervals are also returned.

References

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 Unpublished manuscript: George Mason University

Examples

# effect size log odds
esc_bin_prop(prop1event = .375, grp1n = 80, prop2event = .47, grp2n = 85)

# effect size odds ratio
esc_bin_prop(prop1event = .375, grp1n = 80, prop2event = .47, grp2n = 85,
             es.type = "or")

Example output

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 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

esc documentation built on Dec. 4, 2019, 5:07 p.m.