Prepare parameter dataframe for meta analysis of means

Usage

filter_df_for_metamean(df)

Arguments

df: a parameter dataframe. This must have columns for each of the following: parameter_value, parameter_unit, population_sample_size, parameter_value_type, parameter_uncertainty_singe_type, parameter_uncertainty_type, parameter_uncertainty_lower_value, parameter_uncertainty_upper_value. This will typically be the params data.frame from the output of load_epidata.

Value

a parameter dataframe with relevant rows selected and additional columns added to facilitate the meta analysis of means. The additional columns are: xbar, median, q1, q3, min, max.

Details

The function checks that the format of df is adequate for conducting a meta analysis of means. It filters the dataframe to only include rows that meet the required format. We can only conduct a meta analysis for a parameter if its estimates have been reported as (a) mean and standard deviation, (b) median and interquartile range, or (c) median and range. This function filters the parameter dataframe to only include rows that meet these criteria. It also checks that the parameter values are all in the same units; and that the sample size is reported for each parameter value.

Examples

## preparing data for meta analyses of delay from symptom onset to
## hospitalisation 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
o2h_df <- df[df$parameter_type %in% "Human delay - symptom onset>admission to care", ]
o2h_df_filtered <- filter_df_for_metamean(o2h_df)
#> parameter_value must be present if parameter_unit is present. 1 row with non-NA
#> parameter_value and NA parameter_unit will be removed.
## o2h_df_filtered could then be used directly in meta analyses as:
## mtan <- metamean(data = o2h_df_filtered, ...)