Compute effect size log odds from effect size d.

convert_d2logit(
d,
se,
v,
totaln,
es.type = c("logit", "cox"),
info = NULL,
study = NULL
)

## Arguments

d The effect size d. The standard error of d. One of se or v must be specified. The variance of d. One of se or v must be specified. A vector of total sample size(s). Type of effect size odds ratio that should be returned. May be es.type = "logit" or es.type = "cox" (see 'Details'). String with information on the transformation. Used for the print-method. Usually, this argument can be ignored 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.

## Details

Conversion from d to odds ratios can be done with two methods:

es.type = "logit"

uses the Hasselblad and Hedges logit method.

es.type = "cox"

uses the modified logit method as proposed by Cox. This method performs slightly better for rare or frequent events, i.e. if the success rate is close to 0 or 1.

## Note

Effect size, variance, standard error and confidence intervals are returned on the log-scale. To get the odds ratios and exponentiated confidence intervals, use convert_d2or.

## 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 Unpublished manuscript: George Mason University

Cox DR. 1970. Analysis of binary data. New York: Chapman & Hall/CRC

Hasselblad V, Hedges LV. 1995. Meta-analysis of screening and diagnostic tests. Psychological Bulletin 117(1): 167–178. doi: 10.1037/0033-2909.117.1.167

## Examples

# to logits
convert_d2logit(0.7, se = 0.5)#>
#> Effect Size Calculation for Meta Analysis
#>
#>      Conversion: effect size d to effect size logit
#>     Effect Size:   1.2697
#>  Standard Error:   0.9069
#>        Variance:   0.8225
#>        Lower CI:  -0.5078
#>        Upper CI:   3.0472
#>          Weight:   1.2159
# to Cox-logits
convert_d2logit(0.7, v = 0.25, es.type = "cox")#>
#> Effect Size Calculation for Meta Analysis
#>
#>      Conversion: effect size d to effect size logit(Cox)
#>     Effect Size:   1.1550
#>  Standard Error:   0.8253
#>        Variance:   0.6812
#>        Lower CI:  -0.4627
#>        Upper CI:   2.7727
#>          Weight:   1.4680