simFastBOIN (version 1.3.2)

isotonic_regression: Isotonic Regression for Toxicity Rate Estimation

Description

Apply isotonic regression to estimate toxicity rates under the monotonicity constraint (toxicity increases with dose) using the Pool Adjacent Violators Algorithm (PAVA).

Usage

isotonic_regression(n_pts_mat, n_tox_mat)

Value

Numeric matrix of size n_trials x n_doses. Isotonic regression estimates of toxicity rates at each dose for each trial. Values are NA for untreated doses (n_pts = 0).

Arguments

n_pts_mat

Numeric matrix of size n_trials x n_doses. Number of patients treated at each dose level for each trial.

n_tox_mat

Numeric matrix of size n_trials x n_doses. Number of dose-limiting toxicities (DLTs) observed at each dose level for each trial.

Details

Pseudocounts (0.05 for toxicity, 0.1 for total patients) are added before estimation, reflecting a Beta-Binomial prior. PAVA is applied independently per trial using weighted pooling to enforce monotonicity: dose-toxicity relationships must be non-decreasing across dose levels.

References

Liu, S. and Yuan, Y. (2015). Bayesian Optimal Interval Designs for Phase I Clinical Trials. Journal of the Royal Statistical Society: Series C, 64, 507-523.

Examples

# Example 1: Single trial
n_pts_single <- matrix(c(3, 6, 9, 12), nrow = 1)
n_tox_single <- matrix(c(0, 1, 3, 4), nrow = 1)

iso_est <- isotonic_regression(n_pts_single, n_tox_single)
print(iso_est)

# Example 2: Multiple trials
n_pts_mat <- matrix(c(3, 6, 9, 12,
                      3, 6, 9, 12,
                      3, 6, 9, 12), nrow = 3, byrow = TRUE)
n_tox_mat <- matrix(c(0, 1, 3, 4,
                      0, 0, 2, 3,
                      1, 2, 4, 6), nrow = 3, byrow = TRUE)

iso_est <- isotonic_regression(n_pts_mat, n_tox_mat)
print(iso_est)