Function to generate a metric vs drift plot

Usage

plot_metric_vs_drift(
  metric,
  results_metrics_df,
  theta_0,
  case_study,
  method,
  category,
  control_drift,
  target_sample_size_per_arm,
  parameters_combinations,
  xvars,
  add_baselines = FALSE,
  target_to_source_std_ratio = 1,
  join_points = FALSE,
  source_denominator_change_factor = 1,
  analysis_config
)

Arguments

metric: The metric to plot
results_metrics_df: The dataframe containing the results and metrics
theta_0: The true treatment effect
case_study: The case study name
method: The method name
category: The category to group the results by: "parameters", "target_sample_size_per_arm", "source_denominator_change_factor", "target_to_source_std_ratio", or one of the time-to-event design axes "control_drift", "dropout_probability" and "event_time_distribution", which hold the other two axes at their primary value.
control_drift: Logical indicating whether to filter results by control drift (TRUE) or treatment drift (FALSE)
target_sample_size_per_arm: The target sample size per arm
parameters_combinations: The combinations of parameters to filter the results by
xvars: A list of x-variables for control drift and treatment drift
add_baselines: Logical indicating whether to add baselines to the plot
target_to_source_std_ratio: Ratio between the target and source studies sampling standard deviation.
join_points: Whether to join the point with a line or not.
source_denominator_change_factor: The source denominator change factor of the scenario to keep.
analysis_config: The analysis configuration.

Value

None