Apply a normative T-score model to new data

Applies a previously fitted normative regression model to new data and computes predicted values, z-scores, and T-scores using the same preprocessing settings used during model development.

Usage

ApplyNormativeTScores(df, normative_obj, score_prefix = "Norm")

Arguments

df

A data frame containing the test variable, count variable, and all predictors required by the normative model.

normative_obj

A list returned by CreateNormativeTScores().

score_prefix

A character string prefix used when naming output columns. Defaults to "Norm".

Value

A tibble containing the original data plus scored columns:

Raw

The raw input score.

Scaled

The transformed analysis-scale score.

Predicted

The predicted score from the normative model.

Z

The z-score.

T

The T-score.

Details

This function is designed to work with the output of CreateNormativeTScores(). It uses the saved model and preprocessing settings to score new observations consistently.

Examples

df <- tibble::tibble(
  Group = c(
    rep("Reference", 8),
    rep("Clinical", 2)
  ),
  Age = c(30, 34, 38, 42, 46, 50, 54, 58, 40, 52),
  Education = factor(c(
    "College", "College", "Graduate", "Graduate",
    "College", "Graduate", "College", "Graduate",
    "College", "Graduate"
  )),
  Sex = factor(c(
    "F", "M", "F", "M", "F", "M", "F", "M", "F", "M"
  )),
  Visit = c(1, 1, 1, 1, 2, 2, 2, 2, 1, 2),
  TrailsA = c(35, 38, 40, 43, 36, 39, 41, 44, 47, 49) * 1000
)

norm_obj <- CreateNormativeTScores(
  df = df,
  test_var = "TrailsA",
  count_var = "Visit",
  covariates = c("Age", "Education", "Sex"),
  reference_var = "Group",
  reference_value = "Reference",
  include_practice_effect = TRUE,
  reverse_score = TRUE,
  convert_seconds = TRUE,
  log_transform = TRUE,
  return_plots = FALSE
)

scored_df <- ApplyNormativeTScores(
  df = df,
  normative_obj = norm_obj
)