smdi_rf {smdi}

Computes random forest-based AUC

Description

The function trains and fits a random forest model to assess the ability to predict missingness for the specified covariate(s). If missing indicator can be predicted as a function of observed covariates, MAR may be a likely scenario and would imply that imputation may be feasible.

Important: don't include variables like ID variables, ZIP codes, dates, etc.

Usage

smdi_rf(
  data = NULL,
  covar = NULL,
  train_test_ratio = c(0.7, 0.3),
  set_seed = 42,
  ntree = 1000,
  n_cores = 1
)

Arguments

data

dataframe or tibble object with partially observed/missing variables

covar

character covariate or covariate vector with partially observed variable/column name(s) to investigate. If NULL, the function automatically includes all columns with at least one missing observation and all remaining covariates will be used as predictors

train_test_ratio

numeric vector to indicate the test/train split ratio, e.g. c(.7, .3) which is the default

set_seed

seed for reproducibility, defaults to 42

ntree

integer, number of trees (defaults to 1000 trees)

n_cores

integer, if >1, computations will be parallelized across amount of cores specified in n_cores (only UNIX systems)

Details

The random forest utilizes the randomForest engine.

CAVE: If the missingness indicator variables of other partially observed covariates (indicated by suffix _NA) have an extremely high variable importance (combined with an unusually high AUC), this might be an indicator of a monotone missing data pattern. In this case it is advisable to exclude other partially observed covariates and run missingness diagnostics separately.

Value

returns an rf object which comes as a list that contains the ROC AUC value and corresponding variable importance in training dataset (latter as ggplot object). That is, for each covar, the following outputs are provided:

• rf_table: The area under the receiver operating curve (AUC) as a measure of the ability to predict the missingness of the partially observed covariate

• rf_plot: ggplot object illustrating the variable importance for the prediction made expressed by the mean decrease in accuracy per predictor. That is how much would the accuracy of the prediction (# of correct predictions/Total # of predictions made) decrease, had we left out this specific predictor.

References

Sondhi A, Weberpals J, Yerram P, Jiang C, Taylor M, Samant M, Cherng S. A systematic approach towards missing lab data in electronic health records: A case study in non-small cell lung cancer and multiple myeloma. CPT Pharmacometrics Syst Pharmacol. 2023 Jun 15. <doi: 10.1002/psp4.12998.> Epub ahead of print. PMID: 37322818.

See Also

randomForest

Examples

library(smdi)

smdi_rf(data = smdi_data, covar = "ecog_cat")