Predict new outcomes

Return the expected values, standard errors, and confidence intervals of new outcomes.

Usage

# S3 method for class 'pmrm_fit'
predict(object, data = object$data, adjust = TRUE, confidence = 0.95, ...)

Arguments

object

A fitted model object of class "pmrm_fit".

data

A tibble or data frame with one row per patient visit. This is the new data for making predictions. It must have all the same columns as the original you fit with the model, except that the outcome column can be entirely absent. object$data is an example dataset that will work. It is just like the original data, except that rows with missing responses are removed, and the remaining rows are sorted by patient ID and categorical scheduled visit.

adjust

TRUE or FALSE. adjust = TRUE returns estimates and inference for covariate-adjusted mu_ij values (defined in vignette("models", package = "pmrm")) for new data. adjust = FALSE instead returns inference on mu_ij - W %*% gamma, the non-covariate-adjusted predictions useful in plotting a continuous disease progression trajectory in plot.pmrm_fit().

confidence

Numeric between 0 and 1, the confidence level to use in the 2-sided confidence intervals.

...

Not used.

Value

A tibble with one row for each row in the data argument and columns "estimate", "standard_error", "lower", and "upper". Columns "lower" and "upper" are lower and upper bounds of 2-sided confidence intervals on the means. (The confidence intervals are not actually truly prediction intervals because they do not include variability from residuals.)

See also

Other predictions: fitted.pmrm_fit(), residuals.pmrm_fit()

Examples

  set.seed(0L)
  simulation <- pmrm_simulate_decline_proportional(
    visit_times = seq_len(5L) - 1,
    gamma = c(1, 2)
  )
  fit <- pmrm_model_decline_proportional(
    data = simulation,
    outcome = "y",
    time = "t",
    patient = "patient",
    visit = "visit",
    arm = "arm",
    covariates = ~ w_1 + w_2
  )
  new_data <- pmrm_simulate_decline_proportional(
    patients = 1,
    visit_times = seq_len(5L) - 1,
    gamma = c(1, 2)
  )
  new_data$y <- NULL # Permitted but not strictly necessary.
  predict(fit, new_data)
#> # A tibble: 5 × 7
#>   arm   visit    time estimate standard_error lower  upper
#>   <ord> <ord>   <dbl>    <dbl>          <dbl> <dbl>  <dbl>
#> 1 arm_1 visit_1     0    -3.12         0.0704 -3.26 -2.98 
#> 2 arm_1 visit_2     1     1.15         0.0698  1.01  1.28 
#> 3 arm_1 visit_3     2     3.52         0.0795  3.37  3.68 
#> 4 arm_1 visit_4     3    -1.03         0.0827 -1.19 -0.865
#> 5 arm_1 visit_5     4     1.27         0.0836  1.10  1.43