Decision Table for the BOIN Design

Build the lookup table that maps a pair of DLT and patient counts at the current dose to the dose assignment decision.

Usage

boin_decision_table(
  target,
  max_n,
  p_saf = NULL,
  p_tox = NULL,
  cutoff_eli = 0.95,
  stay_on_1_of_3 = FALSE
)

Arguments

target

Numeric scalar. Target DLT probability.

max_n

Integer scalar. Largest number of patients treated at a single dose.

p_saf

Numeric scalar. Highest DLT probability deemed subtherapeutic. Defaults to 0.6 * target.

p_tox

Numeric scalar. Lowest DLT probability deemed overly toxic. Defaults to 1.4 * target.

cutoff_eli

Numeric scalar. Posterior probability cutoff for dose elimination. Defaults to 0.95.

stay_on_1_of_3

Logical scalar. When TRUE, one DLT out of three patients leads to staying at the current dose rather than de-escalating. Defaults to FALSE. See boin_boundary for what the modification does.

Value

An object of class boin_decision_table, which is a character matrix with max_n + 1 rows, labeled by the number of DLTs from 0 to max_n, and max_n columns, labeled by the number of patients from 1 to max_n. Entries are "E" to escalate, "S" to stay, "D" to de-escalate and "DE" to de-escalate and eliminate the dose together with all higher doses. Combinations with more DLTs than patients are NA. The object has print and plot methods; subsetting it returns a plain matrix.

Details

The table is derived from boin_boundary and therefore agrees with the decisions taken by the simulation engine. Elimination takes precedence over the other rules, then escalation, then de-escalation.

See also

boin_boundary, plot.boin_decision_table

Examples

decisions <- boin_decision_table(target = 0.30, max_n = 12)
decisions
#>     Patients
#> DLTs 1 2 3  4  5  6  7  8  9  10 11 12
#>   0  E E E  E  E  E  E  E  E  E  E  E 
#>   1  D D S  S  E  E  E  E  E  E  E  E 
#>   2    D D  D  D  S  S  S  E  E  E  E 
#>   3      DE DE D  D  D  D  S  S  S  S 
#>   4         DE DE DE D  D  D  D  D  S 
#>   5            DE DE DE DE DE D  D  D 
#>   6               DE DE DE DE DE DE D 
#>   7                  DE DE DE DE DE DE
#>   8                     DE DE DE DE DE
#>   9                        DE DE DE DE
#>   10                          DE DE DE
#>   11                             DE DE
#>   12                                DE
#> 
#> E = escalate, S = stay, D = de-escalate, DE = de-escalate and eliminate this dose and above

# Only the sample sizes reached at the end of a cohort of three
print(decisions, cohort_size = 3)
#>     Patients
#> DLTs 3  6  9  12
#>   0  E  E  E  E 
#>   1  S  E  E  E 
#>   2  D  S  E  E 
#>   3  DE D  S  S 
#>   4     DE D  S 
#>   5     DE DE D 
#>   6     DE DE D 
#>   7        DE DE
#>   8        DE DE
#>   9        DE DE
#>   10          DE
#>   11          DE
#>   12          DE
#> 
#> E = escalate, S = stay, D = de-escalate, DE = de-escalate and eliminate this dose and above

# One DLT out of three de-escalates at a target of 0.25
boin_decision_table(target = 0.25, max_n = 9)["1", "3"]
#> [1] "D"

# and stays once the modification is switched on
boin_decision_table(target = 0.25, max_n = 9, stay_on_1_of_3 = TRUE)["1", "3"]
#> [1] "S"