Prepare a meta-analysis data set for metaconfoundr

metaconfoundr() standardizes data frames with information on how well a set of studies control for a set of variables. In this approach, a set of domain experts agree on the variables that are required to properly control for confounding for a scientific question. Then, for a given confounder, the studies are described as being adequately controlled, inadequately controlled, or controlled with some concerns. metaconfoundr() is intended to standardize data for use in mc_heatmap() and mc_trafficlight(). See the vignette on data preparation for more information on how to set up your evaluation.

metaconfoundr(.df, data_format = mc_detect_layout())

Arguments

.df

A data frame. See the vignette on data preparation for more details.

data_format

The format of the data. Detected automatically by default, but explicit options include mc_longer() and mc_wider()

Value

a tibble

Examples

metaconfoundr(ipi)
#> # A tibble: 407 × 5
#>    construct         variable     is_confounder study        control_quality
#>    <chr>             <chr>        <chr>         <chr>        <ord>          
#>  1 Sociodemographics Maternal age Y             Zhu_2001a    adequate       
#>  2 Sociodemographics Maternal age Y             Zhu_2001b    adequate       
#>  3 Sociodemographics Maternal age Y             Zhu_1999     adequate       
#>  4 Sociodemographics Maternal age Y             Smith_2003   adequate       
#>  5 Sociodemographics Maternal age Y             Shachar_2016 adequate       
#>  6 Sociodemographics Maternal age Y             Salihu_2012a adequate       
#>  7 Sociodemographics Maternal age Y             Salihu_2012b adequate       
#>  8 Sociodemographics Maternal age Y             Hanley_2017  adequate       
#>  9 Sociodemographics Maternal age Y             deWeger_2011 adequate       
#> 10 Sociodemographics Maternal age Y             Coo_2017     adequate       
#> # … with 397 more rows

metaconfoundr(ipi_wide)
#> # A tibble: 407 × 5
#>    construct         variable     is_confounder study        control_quality
#>    <chr>             <chr>        <chr>         <chr>        <ord>          
#>  1 Sociodemographics Maternal age Y             Zhu_2001a    adequate       
#>  2 Sociodemographics Maternal age Y             Zhu_2001b    adequate       
#>  3 Sociodemographics Maternal age Y             Zhu_1999     adequate       
#>  4 Sociodemographics Maternal age Y             Smith_2003   adequate       
#>  5 Sociodemographics Maternal age Y             Shachar_2016 adequate       
#>  6 Sociodemographics Maternal age Y             Salihu_2012a adequate       
#>  7 Sociodemographics Maternal age Y             Salihu_2012b adequate       
#>  8 Sociodemographics Maternal age Y             Hanley_2017  adequate       
#>  9 Sociodemographics Maternal age Y             deWeger_2011 adequate       
#> 10 Sociodemographics Maternal age Y             Coo_2017     adequate       
#> # … with 397 more rows

ipi_wide2 <- ipi_wide %>%
  dplyr::rename(scope = construct)

metaconfoundr(ipi_wide2, mc_wider(construct = "scope"))
#> # A tibble: 407 × 5
#>    construct         variable     is_confounder study        control_quality
#>    <chr>             <chr>        <chr>         <chr>        <ord>          
#>  1 Sociodemographics Maternal age Y             Zhu_2001a    adequate       
#>  2 Sociodemographics Maternal age Y             Zhu_2001b    adequate       
#>  3 Sociodemographics Maternal age Y             Zhu_1999     adequate       
#>  4 Sociodemographics Maternal age Y             Smith_2003   adequate       
#>  5 Sociodemographics Maternal age Y             Shachar_2016 adequate       
#>  6 Sociodemographics Maternal age Y             Salihu_2012a adequate       
#>  7 Sociodemographics Maternal age Y             Salihu_2012b adequate       
#>  8 Sociodemographics Maternal age Y             Hanley_2017  adequate       
#>  9 Sociodemographics Maternal age Y             deWeger_2011 adequate       
#> 10 Sociodemographics Maternal age Y             Coo_2017     adequate       
#> # … with 397 more rows