PVBcorrect (version 0.3.1)

acc_ipb: PVB correction by inverse probability bootstrap sampling (IPB)

Description

Perform PVB correction by inverse probability bootstrap sampling.

Usage

acc_ipb(
  data,
  test,
  disease,
  covariate = NULL,
  saturated_model = FALSE,
  option = 2,
  ci = FALSE,
  ci_level = 0.95,
  ci_type = "norm",
  b = 1000,
  seednum = NULL,
  return_data = FALSE,
  return_detail = FALSE,
  description = TRUE
)

Value

A list object containing:

data_each_sample

Raw data for each bootstrap sample, available with return_data = TRUE

acc_each_sample

Accuracy results for each bootstrap sample, available with return_detail = TRUE

acc_results

The accuracy results.

Arguments

data

A data frame, with at least "Test" and "Disease" variables.

test

The "Test" variable name, i.e. the test result. The variable must be in binary; positive = 1, negative = 0 format.

disease

The "Disease" variable name, i.e. the true disease status. The variable must be in binary; positive = 1, negative = 0 format.

covariate

The name(s) of covariate(s), i.e. other variables associated with either test or disease status. Specify as name vector, e.g. c("X1", "X2") for two or more variables. The variables must be in formats acceptable to GLM.

saturated_model

Set as TRUE to obtain the original Begg and Greenes' (1983) when all possible interactions are included.

option

1 = IPW weight, 2 = W_h weight, described in Arifin (2023), modified weight of Krautenbacher (2017). The default is option = 2. For small weights, option = 2 is more stable (Arifin, 2023).

ci

View confidence interval (CI). The default is FALSE.

ci_level

Set the CI width. The default is 0.95 i.e. 95% CI.

ci_type

Set confidence interval (CI) type. Acceptable types are "norm", "basic", "perc", and "bca", for bootstrapped CI.

b

The number of bootstrap samples, b.

seednum

Set the seed number for the bootstrapped CI. The default is not set, so it depends on the user to set it outside or inside the function.

return_data

Return data for the bootstrapped samples.

return_detail

Return accuracy measures for each of the bootstrapped samples.

description

Print the name of this analysis. The default is TRUE. This can be turned off for repeated analysis, for example in bootstrapped results.

References

1. Arifin, W. N., & Yusof, U. K. (2022). Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests. Diagnostics, 12(11), 2839.

2. Arifin, W. N. (2023). Partial verification bias correction in diagnostic accuracy studies using propensity score-based methods (PhD thesis, Universiti Sains Malaysia). https://erepo.usm.my/handle/123456789/19184

3. Krautenbacher, N., Theis, F. J., & Fuchs, C. (2017). Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies. Computational and Mathematical Methods in Medicine, 2017, 1–18.

4. Nahorniak, M., Larsen, D. P., Volk, C., & Jordan, C. E. (2015). Using inverse probability bootstrap sampling to eliminate sample induced bias in model based analysis of unequal probability samples. PLoS One, 10(6), e0131765.

Examples

# point estimates
acc_ipb(data = cad_pvb, test = "T", disease = "D", b = 100, seednum = 12345)
acc_ipb(data = cad_pvb, test = "T", disease = "D", covariate = "X3",
        b = 100, seednum = 12345)

# with confidence interval
acc_ipb(data = cad_pvb, test = "T", disease = "D", ci = TRUE,
        b = 100, seednum = 12345)  # use small b for testing