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Introduction to metacor

metacor: Meta-analysis with correlated data

The metacor package provides tools for conducting meta-analyses when your effect sizes involve pre-post correlations, missing SDs, and flexible imputation methods. It is especially useful for single-group or controlled designs with incomplete data.

Main Features

Quick Start Example

Let’s illustrate a simple workflow with example data:

library(metacor)

# Example dataset (for pre/post design only)
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, : Row 6: r_int = -1.5024 (outside [-0.9999, 0.9999]) calculated from input data.
#> Check input values or p-value/CI for possible inconsistencies.
#>  r_int set to NA.

Imputation and Reporting

If your dataset has missing values for correlations or SD differences, metacor_dual() will automatically perform imputations based on the method you choose (e.g., “direct”, “mean”, “cv”). You can also request a Word report with all imputations performed by setting the argument report_imputations = TRUE.

result <- metacor_dual(df, report_imputations = TRUE)
#> Warning in metacor_dual(df, report_imputations = TRUE): Row 6: r_int = -1.5024 (outside [-0.9999, 0.9999]) calculated from input data.
#> Check input values or p-value/CI for possible inconsistencies.
#>  r_int set to NA.
#> Warning in metacor_dual(df, report_imputations = TRUE): No real SD diff values
#> available to impute (con).

The report will be saved as imputation_report.docx in your working directory.

Advanced Usage

You can customise the imputation methods and other arguments. For more details, see the documentation:

?metacor_dual

References

Fu, R., Vandermeer, B. W., Shamliyan, T. A., O’Neil, M. E., Yazdi, F., Fox, S. H., & Morton, S. C. (2013). Handling Continuous Outcomes in Quantitative Synthesis. Methods Guide for Comparative Effectiveness Reviews. AHRQ Publication No. 13-EHC103-EF. Link

Session info

sessionInfo()
#> R version 4.3.1 (2023-06-16)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS 15.5
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Madrid
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] metacor_1.1.2
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.6.5       zip_2.3.1         cli_3.6.3         knitr_1.48       
#>  [5] rlang_1.1.4       xfun_0.46         stringi_1.8.4     textshaping_0.4.0
#>  [9] jsonlite_1.8.8    glue_1.8.0        openssl_2.2.0     askpass_1.2.0    
#> [13] htmltools_0.5.8.1 ragg_1.4.0        sass_0.4.9        rmarkdown_2.27   
#> [17] evaluate_1.0.4    jquerylib_0.1.4   fastmap_1.2.0     yaml_2.3.10      
#> [21] lifecycle_1.0.4   stringr_1.5.1     compiler_4.3.1    officer_0.6.10   
#> [25] rstudioapi_0.16.0 systemfonts_1.1.0 digest_0.6.36     R6_2.5.1         
#> [29] magrittr_2.0.3    bslib_0.7.0       uuid_1.2-0        tools_4.3.1      
#> [33] xml2_1.3.6        cachem_1.1.0

…

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.