mdru {healthequal}

Mean difference from a reference subgroup (unweighted) (MDRU)

Description

The Mean Difference from a Reference Subgroup (MDR) is an absolute measure of inequality that shows the mean difference between each population subgroup and a reference subgroup. For the unweighted version (MDRU), all subgroups are weighted equally.

Usage

mdru(
  est,
  se = NULL,
  scaleval,
  reference_subgroup,
  sim = NULL,
  seed = 123456,
  ...
)

Arguments

est

The subgroup estimate. Estimates must be available for all subgroups.

se

The standard error of the subgroup estimate. If this is missing, 95% confidence intervals of MDRU cannot be calculated.

scaleval

The scale of the indicator. For example, the scale of an indicator measured as a percentage is 100. The scale of an indicator measured as a rate per 1000 population is 1000.

reference_subgroup

Identifies a reference subgroup with the value of 1.

sim

The number of simulations to estimate 95% confidence intervals

seed

The random number generator (RNG) state for the 95% confidence interval simulation

...

Further arguments passed to or from other methods.

Details

The unweighted version (MDRU) is calculated as the average of absolute differences between the subgroup estimates and the estimate for the reference subgroup, divided by the number of subgroups. For more information on this inequality measure see Schlotheuber, A., & Hosseinpoor, A. R. (2022) below.

95% confidence intervals are calculated using a methodology of simulated estimates. The dataset is simulated a large number of times (e.g., 100) and MDRU is calculated for each of the simulated samples. The 95% confidence intervals are based on the 2.5th and 97.5th percentiles of the MDRU results.

Interpretation: MDRU only has positive values, with larger values indicating higher levels of inequality. MDRU is zero if there is no inequality.

Type of summary measure: Complex; absolute; non-weighted

Applicability: Non-ordered; more than two subgroups

Value

The estimated MDRU value, corresponding estimated standard error, and confidence interval as a data.frame.

References

Schlotheuber, A., & Hosseinpoor, A. R. (2022). Summary measures of health inequality: A review of existing measures and their application. International Journal of Environmental Research and Public Health, 19 (6), 3697.

Examples

# example code
data(NonorderedSample)
head(NonorderedSample)
with(NonorderedSample,
     mdru(est = estimate,
          se = se,
          scaleval = indicator_scale,
          reference_subgroup
         )
     )