Isotonic Estimates of the DLT Probability

Estimate the DLT probability at each dose under the constraint that toxicity does not decrease with dose, using the pool adjacent violators algorithm.

Usage

boin_isotonic(n_pts, n_tox, admissible = NULL)

Arguments

n_pts

Integer matrix with one row per trial and one column per dose, or a vector for a single trial, giving the number of patients treated.

n_tox

Integer matrix or vector of the same shape as n_pts, giving the number of DLTs observed.

admissible

Logical matrix of the same shape as n_pts, or NULL. Doses that are FALSE are left out of the estimation and returned as NA. Defaults to all doses that have treated at least one patient.

Value

A numeric matrix with one row per trial and one column per dose. Doses that did not enter the estimation are NA.

Details

Pseudo-counts of 0.05 DLTs and 0.1 patients are added before the fit, and doses are pooled with inverse variance weights. Doses left out through admissible do not influence the estimates of the remaining doses, which matters when eliminated doses must be excluded before selecting the MTD.

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.

See also

boin_select_mtd

Examples

# A single trial
boin_isotonic(n_pts = c(3, 6, 9, 12), n_tox = c(0, 1, 3, 4))
#>            [,1]      [,2]      [,3]      [,4]
#> [1,] 0.01612903 0.1721311 0.3349084 0.3349084

# Several trials at once
n_pts <- matrix(c(3, 6, 9, 12,
                  3, 6, 9, 12,
                  3, 6, 9, 12), nrow = 3, byrow = TRUE)
n_tox <- matrix(c(0, 1, 3, 4,
                  0, 0, 2, 3,
                  1, 2, 4, 6), nrow = 3, byrow = TRUE)
boin_isotonic(n_pts, n_tox)
#>            [,1]       [,2]      [,3]      [,4]
#> [1,] 0.01612903 0.17213115 0.3349084 0.3349084
#> [2,] 0.01000763 0.01000763 0.2252747 0.2520661
#> [3,] 0.33703115 0.33703115 0.4450549 0.5000000