QualityMeasure (version 2.0.1)

calcReliability: Calculate reliability of quality measure performance

Description

This function calculates several estimates of quality measure performance.

Usage

calcReliability(
  df = NULL,
  model = NULL,
  entity = "entity",
  y = "y",
  data.type = "binary",
  show.all = FALSE,
  ctrPerf = controlPerf(),
  ctrRel = controlRel()
)

Value

A list with reliability estimates. rel.results is a dataframe summarizing estimates from the various methods. Output from each method's respective function is also included. More details on output from each method can be found within the help documentation for the respective function for that method. For example, see calcSSR() for more detail on SSR.out.

Arguments

df

observation-level data; if null, will use the dataframe from the model object

model

model; if null, will use an unadjusted model

entity

data column containing the accountable entity identifier

y

data column containing the outcome variable

data.type

acceptable values are binary for 0/1 data and continuous for continuous data (default: binary)

show.all

logical indicator for whether full list of reliability method estimates should be calculated (default: FALSE)

ctrPerf

parameters to control performance measure calculation

ctrRel

parameters to control reliability estimation

Author

Kenneth Nieser ([e-mail])

References

Nieser KJ, Harris AH. Comparing methods for assessing the reliability of health care quality measures. Statistics in Medicine. 2024 Oct 15;43(23):4575-94.

See Also

calcAOV(), calcBetaBin(), calcHLGMRel(), calcHLMRel(), calcResamplingIUR(), calcSSR()

Examples

### Simulate data with binary outcome
df <- simulateData(n.entity = 50, n.obs = 100, mu = .2, r = .7)

# Calculate reliability
out <- calcReliability(df = df, entity = 'entity', y = 'y', ctrRel = controlRel(n.resamples = 10))

# Plot estimates
plotReliability(out)