Prepare parameter dataframe for meta analysis of proportions

Usage

filter_df_for_metaprop(df, num_col, denom_col)

Arguments

df: a parameter dataframe. This must have columns for each of the following: parameter_value, parameter_unit, plus two columns for the numerator and the denominator of the proportion of interest. This dataframe will typically be the params data.frame from the output of load_epidata.
num_col: a string specifying the column name for the column containing the numerator of the proportion of interest.
denom_col: a string specifying the column name for the column containing the denominator of the proportion of interest.

Value

a parameter dataframe with relevant rows selected to enable meta analysis of proportions.

Details

The function checks that the format of df is adequate for conducting a meta analysis of proportions. It filters the dataframe to only include rows that meet the required format by (1) removing rows where the denominator is missing, and (2) removing rows where both the numerator column or parameter value are missing. If the numerator column is missing and the parameter value is present, the numerator is imputed as the parameter value divided by 100 times the denominator.

Examples

## preparing data for meta analyses of CFR for Lassa

df <- load_epidata("lassa")[["params"]]
#> Rows: 107 Columns: 2
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ";"
#> chr (2): parameter_type_short, parameter_type_full
#> 
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> ✔ Data loaded for lassa
cfr_df <- df[df$parameter_type %in% "Severity - case fatality rate (CFR)",]
cfr_filtered <- filter_df_for_metaprop(cfr_df,
  num_col = "cfr_ifr_numerator", denom_col = "cfr_ifr_denominator")
#> parameter_value must be present if parameter_unit is present. 6 rows with
#> non-NA parameter_value and NA parameter_unit will be removed.
## cfr_filtered could then be used directly in meta analyses as:
## mtan <- metaprop(data = cfr_filtered, ...)