Decision Panel Threshold Calculator (p

Purpose (Clinician-Friendly)

Use your own harm/benefit assumptions to calculate the threshold probability p_t at which you would switch from “no treatment” to “treat”. Then read the Decision Curve at that p_t.

Formula: p_t = harm_false_positive / (harm_false_positive + benefit_true_positive)

Tips: Only the ratio matters; you can measure “harm” and “benefit” in any consistent units (risk, cost, discomfort, etc.).

Quick Calculator

# Enter your assumptions
harm_false_positive   <- 1   # e.g., biopsy harm units
benefit_true_positive <- 4   # e.g., benefit units of early treatment

# Basic validation
stopifnot(harm_false_positive >= 0)
stopifnot(benefit_true_positive > 0)

# Threshold probability p_t = harm / (harm + benefit)
p_t <- harm_false_positive / (harm_false_positive + benefit_true_positive)

cat(sprintf("Recommended threshold p_t = %.2f (%.0f%%)\n", p_t, 100 * p_t))

Sensitivity Check (Optional)

# Explore how p_t changes if benefit varies
benefit_seq <- c(2, 3, 4, 6, 8)
pt_seq <- harm_false_positive / (harm_false_positive + benefit_seq)

result <- data.frame(benefit_true_positive = benefit_seq,
                     p_t = round(pt_seq, 2),
                     p_t_pct = paste0(round(100*pt_seq), "%"))
result

Next Steps

In jamovi, open the meddecide menu > Decision Panel > Decision Curve, set a threshold range that includes your p_t (e.g., 0.20), and pick the strategy with the highest net benefit at that p_t.
See also: decisionpanel_clinician_guide.md and START-HERE.md.

Visualizing p_t vs Benefit

benefit_seq <- seq(1, 10, by = 1)
pt_seq <- harm_false_positive / (harm_false_positive + benefit_seq)
df <- data.frame(benefit_true_positive = benefit_seq,
                 p_t = pt_seq)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  ggplot(df, aes(x = benefit_true_positive, y = p_t)) +
    geom_line(color = "#2C7FB8", linewidth = 1) +
    geom_point(color = "#2C7FB8") +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    labs(x = "Benefit of a True Positive (units)",
         y = "Threshold probability p_t",
         title = "Threshold p_t decreases as benefit increases",
         subtitle = "p_t = harm_false_positive / (harm_false_positive + benefit_true_positive)") +
    theme_minimal(base_size = 12)
}

Visualizing p_t vs Harm (benefit fixed)

# Fix the benefit and vary harm
benefit_true_positive <- if (!exists("benefit_true_positive")) 4 else benefit_true_positive
harm_seq <- seq(0, 10, by = 1)
pt_seq_harm <- harm_seq / (harm_seq + benefit_true_positive)
df_harm <- data.frame(harm_false_positive = harm_seq,
                      p_t = pt_seq_harm)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  library(ggplot2)
  ggplot(df_harm, aes(x = harm_false_positive, y = p_t)) +
    geom_line(color = "#D95F02", linewidth = 1) +
    geom_point(color = "#D95F02") +
    scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
    labs(x = "Harm of a False Positive (units)",
         y = "Threshold probability p_t",
         title = "Threshold p_t increases as harm increases",
         subtitle = paste0("Benefit fixed at ", benefit_true_positive, " units")) +
    theme_minimal(base_size = 12)
}

Decision Panel Threshold Calculator (p_t)