Description Usage Arguments Details Value Note References Examples
View source: R/convert_d2logit.R
Compute effect size log odds
from effect size d
.
1 2 3 4 5 6 7 8 9 | convert_d2logit(
d,
se,
v,
totaln,
es.type = c("logit", "cox"),
info = NULL,
study = NULL
)
|
d |
The effect size |
se |
The standard error of |
v |
The variance of |
totaln |
A vector of total sample size(s). |
es.type |
Type of effect size odds ratio that should be returned.
May be |
info |
String with information on the transformation. Used for the print-method. Usually, this argument can be ignored |
study |
Optional string with the study name. Using |
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.
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
.
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
.
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 Unpublished manuscript: George Mason University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
1 2 3 4 5 | # to logits
convert_d2logit(0.7, se = 0.5)
# to Cox-logits
convert_d2logit(0.7, v = 0.25, es.type = "cox")
|
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