Estimate decision sensitivy DSA using linear metamodel — estimate_decision

This function performs a logistic regression analysis and determines the decision sensitivity to parameter value using the logistic regression.

Usage

estimate_decision_sensitivity(df, y, x, y_binomial = FALSE, limit = 0)

Arguments

df: a dataframe. This dataframe should contain both dependent and independent variables.
y: character. Name of the output variable in the dataframe. This will be the dependent variable of the logistic regression model.
x: character or a vector for characters. Name of the input variable in the dataframe. This(these) will be the independent variable(s) of the logistic regression model.
y_binomial: logical. Is `y` already a binomial outcome? Default is `FALSE.` If `TRUE`, the `y` variable will be used as such, otherwise, the `y` variable will be converted to a binomial variable using the `limit` argument.
limit: numeric. Determines the limit when outcomes from `y` are categorised as 'success' (1) or not (0).

Value

A dataframe with the parameter values of the fitted logistic regression and the decision sensitivity associated with each parameter included in the logistic regression model.

Details

The method for these analyses is described in [Merz et al. 1992](https://doi.org/10.1177

Examples

# Determining decision sensitivity using a non-binomial outcome
data(df_pa)
df_pa$inmb <- df_pa$inc_qaly * 100000 - df_pa$inc_costs
estimate_decision_sensitivity(df = df_pa,
                              y = "inmb",
                              x = c("p_pfsd", "p_pdd"),
                              y_binomial = FALSE
                              )
#>        Estimate  Std. Error z value   Pr(>|z|) Low_CI    High_CI   Importance
#> p_pfsd "-43.456" "1.141"    "-38.076" "0"      "-45.693" "-41.219" "63.9 %"  
#> p_pdd  "12.99"   "0.645"    "20.141"  "0"      "11.726"  "14.255"  "36.1 %"