metaConvert-package {metaConvert}

metaConvert: An R Package Dedicated to Automated Effect Size Calculations

Description

The metaConvert package automatically estimates 11 effect size measures from a well-formatted dataframe. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two dataframes. This package is mainly designed to assist in conducting a systematic review with a meta-analysis, but it can be useful to any researcher interested in estimating an effect size.

Overview of the package

To visualize all the types of input data that can be used to estimate the 11 effect size measures available in metaConvert, you can use the see_input_data() function.

Estimate effect sizes

To automatically estimate effect sizes directly from a dataset, you can use the convert_df() function.

Aggregate dependent effect sizes

To automatically aggregate dependent effect sizes using Borenstein's formulas, you can use the aggregate_df() function. This function can handle dependent effect sizes from multiple subgroups, or dependent effect sizes from the same participants.

Flag differences between two datasets

If pairs of data extractors have generated similar datasets that should be compared, you can use the compare_df() function.

Prepare a dataset extraction sheet

If you have not started data extraction yet, you can use the data_extraction_sheet() function to obtain a perfectly formatted data extraction sheet.

Well-formatted dataset

One of the specificities of the metaConvert package is that its core function (convert_df) does not have arguments to specify the names of the variables contained in the dataset. While this allow using a convenient automatic process in the calculations, this requires that the datasets passed to this function respect a very precise formatting (which we will refer to as well-formatted dataset).

Rather than a long description of all column names, we built several tools that help you find required information.

1. You can use the data_extraction_sheet() function that generates an excel/csv/txt file containing all the column names available, as well as a description of the information it should contain.

2. You can use the see_input_data() function that generates a list of all available types of input data as well as their estimated/converted effect size measures. This function also points out to the corresponding helper tables available in https://metaconvert.org

Effect size measures available

Eleven effect size measures are accepted:

• "d": standardized mean difference (i.e., Cohen's d)

• "g": Hedges' g

• "md": mean difference

• "r": Correlation coefficient

• "z": Fisher's r-to-z correlation

• "or" or "logor": odds ratio or its logarithm

• "rr" or "logrr": risk ratio or its logarithm

• "irr" or "logirr": incidence rate ratio or its logarithm

• "nnt": number needed to treat

• "logcvr": log coefficient of variation

• "logvr": log variability ratio

Output

All the functions of the metaConvert package that are dedicated to effect size calculations (i.e., all the functions named es_from_*) return a dataframe that contain, depending on the function - some of the following columns:

info_used	input data used to generate the effect size.
md	value of the mean difference.
md_se	standard error of the mean difference.
md_ci_lo	lower bound of the 95% CI of the mean difference.
md_ci_up	upper bound of the 95% CI of the mean difference.
d	value of the Cohen's d.
d_se	standard error of the Cohen's d.
d_ci_lo	lower bound of the 95% CI of the Cohen's d.
d_ci_up	upper bound of the 95% CI of the Cohen's d.
g	value of the Hedges' g.
g_se	standard error of the Hedges' g.
g_ci_lo	lower bound of the 95% CI of the Hedges' g.
g_ci_up	upper bound of the 95% CI of the Hedges' g.
r	value of the correlation coefficient.
r_se	standard error of the correlation coefficient.
r_ci_lo	lower bound of the 95% CI of the correlation coefficient.
r_ci_up	upper bound of the 95% CI of the correlation coefficient.
z	value of the r-to-z transformed correlation coefficient.
z_se	standard error of the r-to-z transformed correlation coefficient.
z_ci_lo	lower bound of the 95% CI of the r-to-z transformed correlation coefficient.
z_ci_up	upper bound of the 95% CI of the r-to-z transformed correlation coefficient.
logor	value of the log odds ratio.
logor_se	standard error of the log odds ratio.
logor_ci_lo	lower bound of the 95% CI of the log odds ratio.
logor_ci_up	upper bound of the 95% CI of the log odds ratio.
logrr	value of the log risk ratio.
logrr_se	standard error of the log risk ratio.
logrr_ci_lo	lower bound of the 95% CI of the log risk ratio.
logrr_ci_up	upper bound of the 95% CI of the log risk ratio.
logirr	value of the log incidence rate ratio.
logirr_se	standard error of the log incidence rate ratio.
logirr_ci_lo	lower bound of the 95% CI of the log incidence rate ratio.
logirr_ci_up	upper bound of the 95% CI of the log incidence rate ratio.
logvr	value of the log variability ratio.
logvr_se	standard error of the log variability ratio.
logvr_ci_lo	lower bound of the 95% CI of the log variability ratio.
logvr_ci_up	upper bound of the 95% CI of the log variability ratio.
logcvr	value of the log coefficient of variation.
logcvr_se	standard error of the log coefficient of variation.
logcvr_ci_lo	lower bound of the 95% CI of the log coefficient of variation.
logcvr_ci_up	upper bound of the 95% CI of the log coefficient of variation.
nnt	number needed to treat.