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metacor

metacor is an R package for advanced meta-analysis of pre-post studies, with or without a control group. It provides flexible effect size calculation, robust imputation strategies for missing SDs or correlations, and reproducible reporting.

Installation

You can install the development version of metacor from GitHub

# Install the remotes package if needed:
# install.packages("remotes")

remotes::install_github("https://github.com/ikerugr/metacor")

Example

Suppose you have a data frame of pre-post means, SDs, and sample sizes. Here’s a minimal example with fake data:

library(metacor)

# Example dataset (add your real studies for actual analysis)
df <- data.frame(
  study_name = c("Study1", "Study2", "Study3", "Study4","Study5", "Study6", "Study7", "Study8", "Study9"),
  p_value_Int = c(1.038814e-07, NA, NA, NA, NA, 2.100000e-02, NA, NA, NA),
  n_Int = c(10, 10, 10, 10, 15, 15, 10, 10, 10),
  meanPre_Int = c(8.17, 10.09, 10.18, 9.85, 9.51,7.70, 10.00,  11.53, 11.20),
  meanPost_Int = c(10.12, 12.50, 12.56,10.41, 10.88, 9.20, 10.80,13.42,12.00),
  sd_pre_Int = c(1.83,0.67,0.66,0.90,0.62, 0.90, 0.70, 0.60, 1.90),
  sd_post_Int = c(1.85, 0.72, 0.97, 0.67, 0.76, 1.10, 0.70,0.80,1.80),
  upperCI_Int = c(NA, NA,NA, NA,NA, NA,NA, NA, NA),
  lowerCI_Int = c(NA, NA,NA, NA,NA, NA,NA, NA, NA))

results <- metacor_dual(df,
                        digits = 3,
                        method = "both",
                        apply_hedges = TRUE,
                        add_to_df = TRUE,
                        SMD_method = "SMDpre",
                        MeanDifferences = TRUE,
                        impute_method = "cv",
                        verbose = TRUE,
                        report_imputations = TRUE,
                        custom_sd_diff_int = NULL,
                        custom_sd_diff_con = NULL,
                        single_group = TRUE)
#> Warning in metacor_dual(df, digits = 3, method = "both", apply_hedges = TRUE, :
#> No real SD diff values available to impute (con).
#> Imputed sd_diff_int at row 2 using 'cv' (0.8494): 2.0470
#> Warning in metacor_dual(df, digits = 3, method = "both", apply_hedges = TRUE, : Row 2: Imputed sd_diff_int = 2.0470 gives r_int = -3.3405 (outside [-0.9999, 0.9999]).
#> → Suggested sd_diff_int range: [0.0510, 1.3900].
#> → r_int not assigned.
#> Imputed sd_diff_int at row 3 using 'cv' (0.8494): 2.0215
#> Warning in metacor_dual(df, digits = 3, method = "both", apply_hedges = TRUE, : Row 3: Imputed sd_diff_int = 2.0215 gives r_int = -2.1166 (outside [-0.9999, 0.9999]).
#> → Suggested sd_diff_int range: [0.3102, 1.6300].
#> → r_int not assigned.
#> Imputed sd_diff_int at row 4 using 'cv' (0.8494): 0.4757
#> Imputed sd_diff_int at row 5 using 'cv' (0.8494): 1.1636
#> Imputed sd_diff_int at row 7 using 'cv' (0.8494): 0.6795
#> Imputed sd_diff_int at row 8 using 'cv' (0.8494): 1.6053
#> Warning in metacor_dual(df, digits = 3, method = "both", apply_hedges = TRUE, : Row 8: Imputed sd_diff_int = 1.6053 gives r_int = -1.6428 (outside [-0.9999, 0.9999]).
#> → Suggested sd_diff_int range: [0.2002, 1.4000].
#> → r_int not assigned.
#> Imputed sd_diff_int at row 9 using 'cv' (0.8494): 0.6795

By default, a detailed Word report (imputation_report.docx) is generated, describing all imputations made. You can adjust arguments for imputation method, effect size, verbosity, and more.

Features

More information

vignette(“metacor-intro”)

Author

Iker J. Bautista (2025) Chichester University

This README was generated with readme.Rmd.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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