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Create readable summary of PTSD diagnostic changes

Source: R/helping_functions.R
create_readable_summary.Rd

Formats the output of summarize_ptsd_changes() into a more readable table with proper labels and formatting of percentages and metrics.

Usage

create_readable_summary(summary_stats)

Arguments

summary_stats

A dataframe output from summarize_ptsd_changes() containing raw diagnostic metrics and counts

Value

A formatted dataframe with the following columns:

  • Scenario: Name of the diagnostic criterion

  • Total Diagnosed: Count and percentage of diagnosed cases

  • Total Non-Diagnosed: Count and percentage of non-diagnosed cases

  • True Positive: Count of cases diagnosed under both criteria

  • True Negative: Count of cases not diagnosed under either criterion

  • Newly Diagnosed: Count of new positive diagnoses (false positive)

  • Newly Non-Diagnosed: Count of new negative diagnoses (false negative)

  • True Cases: Total correctly classified cases

  • False Cases: Total misclassified cases

  • Sensitivity, Specificity, PPV, NPV: Diagnostic accuracy metrics (4 decimals)

Details

Reformats the diagnostic metrics into a presentation-ready format:

  • Combines counts with percentages for diagnosed/non-diagnosed cases

  • Rounds diagnostic accuracy metrics to 4 decimal places

  • Provides clear column headers for all metrics

Examples

# Using the output from summarize_ptsd_changes
n_cases <- 100
sample_data <- data.frame(
  PTSD_orig = sample(c(TRUE, FALSE), n_cases, replace = TRUE),
  PTSD_alt1 = sample(c(TRUE, FALSE), n_cases, replace = TRUE)
)

# Generate and format summary
diagnostic_metrics <- summarize_ptsd_changes(sample_data)
readable_summary <- create_readable_summary(diagnostic_metrics)
print(readable_summary)
#>    Scenario Total Diagnosed Total Non-Diagnosed True Positive True Negative
#> 1 PTSD_orig        52 (52%)            48 (48%)            52            48
#> 2 PTSD_alt1        63 (63%)            37 (37%)            31            16
#>   Newly Diagnosed Newly Non-Diagnosed True Cases False Cases Sensitivity
#> 1               0                   0        100           0      1.0000
#> 2              32                  21         47          53      0.5962
#>   Specificity    PPV    NPV
#> 1      1.0000 1.0000 1.0000
#> 2      0.3333 0.4921 0.4324