Estimate Predictive Modeling-Based MICs and Thresholds

Estimates (i) predictive modeling-based, (ii) adjusted predictive modeling-based, and (iii) improved adjusted predictive modeling-based minimal important change (MIC) estimates, with optional bootstrap confidence intervals. mic_iapm() can also be used to estimate the interpretation threshold of a predictor.

Usage

mic_iapm(
  mypred,
  anchor,
  mydata,
  anchor_reliability = NULL,
  nboot = 0,
  report_every = 100,
  verbose = FALSE,
  max_attempts = nboot * 5
)

Arguments

mypred: Character string; name of the column containing the change score or predictor score.
anchor: Character string; name of the column containing the binary anchor. The anchor must be binary and coded as 0/1 or TRUE/FALSE.
mydata: Data frame with the change score or predictor score and the anchor in separate columns.
anchor_reliability: Optional anchor reliability. Can be either a single numeric value between 0 and 1, or an object returned by tr_reliability(). If supplied, the improved adjusted predictive modeling-based MIC is also calculated.
nboot: Integer; number of bootstrap samples for estimating 95% confidence intervals. Bootstrapping is performed only when nboot >= 100.
report_every: Integer. The interval at which bootstrap progress should be printed.
verbose: Logical. If TRUE, progress messages are printed.
max_attempts: Integer; maximum number of bootstrap attempts. This avoids an infinite loop when many bootstrap samples fail.

Value

A mic_iapm object containing:

mic_pm: Predictive modeling-based MIC.
mic_apm: Adjusted predictive modeling-based MIC.
mic_iapm: Improved adjusted predictive modeling-based MIC, returned only when anchor_reliability is supplied.
mic_pm_ci: Bootstrap confidence interval for mic_pm, if requested.
mic_apm_ci: Bootstrap confidence interval for mic_apm, if requested.
mic_iapm_ci: Bootstrap confidence interval for mic_iapm, if requested and anchor_reliability is supplied.
mic_ci: Matrix of available MIC estimates and confidence intervals.
anchor_reliability: Anchor reliability used in the improved adjusted predictive modeling calculation.
nboot: Requested number of bootstrap samples.
n_successful_boot: Number of successful bootstrap samples.

Details

For reproducible bootstrap confidence intervals, call set.seed() before calling mic_iapm().

References

Terluin B, Eekhout I, Terwee CB, de Vet HCW. Minimal important change (MIC) based on a predictive modeling approach was more precise than MIC based on receiver operating characteristic analysis. J Clin Epidemiol. 2015;68(12):1388-1396. doi:10.1016/j.jclinepi.2015.03.015

Terluin B, Eekhout I, Terwee CB. The anchor-based minimal important change, based on receiver operating characteristic analysis or predictive modeling, may need to be adjusted for the proportion of improved patients. J Clin Epidemiol. 2017;83:90-100. doi:10.1016/j.jclinepi.2016.12.015

Terluin B, Eekhout I, Terwee CB. Improved adjusted minimal important change took reliability of transition ratings into account. J Clin Epidemiol. 2022;148:48-53. doi:10.1016/j.jclinepi.2022.04.018

Examples

# \donttest{
set.seed(123)
sim <- simdat(N = 200, add_change = TRUE)
dat <- sim$datw

mic_iapm(
  mypred = "change",
  anchor = "trat",
  mydata = dat,
  anchor_reliability = sim$truth$observed_rel_trt,
  nboot = 200
)
#> $mic_pm
#> [1] 1.797553
#> 
#> $mic_apm
#> [1] 1.892168
#> 
#> $mic_iapm
#> [1] 1.994625
#> 
#> $anchor_reliability
#> [1] 0.7617496
#> 
#> $mic_pm_ci
#>      mic    lower    upper 
#> 1.797553 1.015572 2.725703 
#> 
#> $mic_apm_ci
#>      mic    lower    upper 
#> 1.892168 1.119443 2.799902 
#> 
#> $mic_iapm_ci
#>      mic    lower    upper 
#> 1.994625 0.982196 3.114250 
#> 
#> $mic_ci
#>               mic    lower    upper
#> mic_pm   1.797553 1.015572 2.725703
#> mic_apm  1.892168 1.119443 2.799902
#> mic_iapm 1.994625 0.982196 3.114250
#> 
#> $nboot
#> [1] 200
#> 
#> $n_successful_boot
#> [1] 200
#> 
#> attr(,"class")
#> [1] "mic_iapm"
# }