combine_data: Combines different effect sizes into a common metric

In RoBMA: Robust Bayesian Meta-Analyses

Combines different effect sizes into a common metric

Description

combine_data combines different effect sizes into a common measure specified in transformation. Either a data.frame data with columns named corresponding to the arguments or vectors with individual values can be passed.

Usage

combine_data(
  d = NULL,
  r = NULL,
  z = NULL,
  logOR = NULL,
  OR = NULL,
  t = NULL,
  y = NULL,
  se = NULL,
  v = NULL,
  n = NULL,
  lCI = NULL,
  uCI = NULL,
  study_names = NULL,
  study_ids = NULL,
  weight = NULL,
  data = NULL,
  transformation = "fishers_z",
  return_all = FALSE,
  ...
)

Arguments

d	a vector of effect sizes measured as Cohen's d
r	a vector of effect sizes measured as correlations
z	a vector of effect sizes measured as Fisher's z
logOR	a vector of effect sizes measured as log odds ratios
OR	a vector of effect sizes measured as odds ratios
t	a vector of t/z-statistics
y	a vector of unspecified effect sizes (note that effect size transformations are unavailable with this type of input)
se	a vector of standard errors of the effect sizes
v	a vector of variances of the effect sizes
n	a vector of overall sample sizes
lCI	a vector of lower bounds of confidence intervals
uCI	a vector of upper bounds of confidence intervals
study_names	an optional argument with the names of the studies
study_ids	an optional argument specifying dependency between the studies (for using a multilevel model). Defaults to NULL for studies being independent.
weight	specifies likelihood weights of the individual estimates. Notes that this is an untested experimental feature.
data	a data frame with column names corresponding to the variable names used to supply data individually
transformation	transformation to be applied to the supplied effect sizes before fitting the individual models. Defaults to "fishers_z". We highly recommend using "fishers_z" transformation since it is the only variance stabilizing measure and does not bias PET and PEESE style models. The other options are "cohens_d", correlation coefficient "r" and "logOR". Supplying "none" will treat the effect sizes as unstandardized and refrain from any transformations.
return_all	whether data frame containing all filled values should be returned. Defaults to FALSE
...	additional arguments.

Details

The aim of the function is to combine different, already calculated, effect size measures. In order to obtain effect size measures from raw values, e.g, mean differences, standard deviations, and sample sizes, use escalc function.

The function checks the input values and in transforming the input into a common effect size measure in the following fashion:

1. obtains missing standard errors by squaring variances

2. obtains missing standard errors from confidence intervals (after transformation to Fisher's z scale for d and r).

3. obtains missing sample sizes (or standard errors for logOR) from t-statistics and effect sizes

4. obtains missing standard errors from sample sizes and effect sizes

5. obtains missing sample sizes from standard errors and effect sizes

6. obtains missing t-statistics from sample sizes and effect sizes (or standard errors and effect sizes for logOR)

7. changes the effect sizes direction to be positive

8. transforms effect sizes into the common effect size

9. transforms standard errors into the common metric

If the transforms is NULL or an unstandardized effect size y is supplied, steps 4-9 are skipped.

Value

combine_data returns a data.frame.

See Also

RoBMA(), check_setup(), effect_sizes(), standard_errors(), and sample_sizes()

RoBMA documentation built on July 26, 2023, 5:13 p.m.