Print a TITE-BOIN Decision Table
Source:R/print.tite_boin_decision_table.R
print.tite_boin_decision_table.RdDisplay the decision table produced by tite_boin_decision_table
in the layout of the published tables: one row per number of patients
treated, number of DLTs and range of pending patients sharing a decision,
with the boundaries on the follow-up statistic in the Escalate, Stay and
De-escalate columns.
Usage
# S3 method for class 'tite_boin_decision_table'
print(x, cohort_size = NULL, digits = 2, ...)Arguments
- x
An object of class
tite_boin_decision_table.- cohort_size
Integer scalar or
NULL. When supplied, only the sample sizes that are multiples ofcohort_sizeare shown, which is how the published tables are laid out.- digits
Integer scalar. Number of decimal places of the boundaries. Defaults to 2, as in the published tables.
- ...
Further arguments, currently ignored.
Details
A boundary shown as ">= 2.15" under Escalate means that the dose is
escalated when the follow-up statistic is at least 2.15; the statistic is
STFT for method = "imputation" and ESS for method = "ess".
"Suspend if >= 4.23" under Escalate marks an escalation blocked by
min_completed. When the table was built with a positive
min_follow_up, an escalation with pending patients carries the
condition on MF, for example ">= 0.22 & MF >= 0.25"; accrual is
suspended when the condition on the follow-up statistic holds but the one
on MF does not.
A data frame that no longer has the columns or attributes of a decision table, for example after selecting columns, is printed as a plain data frame.
Examples
decisions <- tite_boin_decision_table(target = 0.3, max_n = 15)
# Table S1 of Yuan et al. (2018)
print(decisions, cohort_size = 3)
#> TITE-BOIN decision table with target 0.3
#> Method: single mean imputation (Yuan et al., 2018); boundaries refer to STFT
#>
#> Patients DLTs Pending Escalate Stay De-escalate
#> 3 0 <= 1 Y
#> 3 0 >= 2 Suspend Suspend Suspend
#> 3 1 0 Y
#> 3 1 1 > 0.88 <= 0.88
#> 3 1 >= 2 Suspend Suspend Suspend
#> 3 2 <= 1 Y
#> 3 3 0 Y & Elim
#> 6 0 <= 3 Y
#> 6 0 >= 4 Suspend Suspend Suspend
#> 6 1 <= 1 Y
#> 6 1 2 >= 0.60 < 0.60
#> 6 1 3 >= 1.96 < 1.96
#> 6 1 >= 4 Suspend Suspend Suspend
#> 6 2 0 Y
#> 6 2 1 > 0.73 <= 0.73
#> 6 2 2 > 1.80 <= 1.80
#> 6 2 3 > 2.87 <= 2.87
#> 6 2 >= 4 Suspend Suspend Suspend
#> 6 3 <= 3 Y
#> 6 >= 4 <= 2 Y & Elim
#> 9 0 <= 4 Y
#> 9 0 >= 5 Suspend Suspend Suspend
#> 9 1 <= 4 Y
#> 9 1 >= 5 Suspend Suspend Suspend
#> 9 2 0 Y
#> 9 2 1 >= 0.59 < 0.59
#> 9 2 2 >= 1.65 < 1.65
#> 9 2 3 >= 2.71 < 2.71
#> 9 2 4 >= 3.77 < 3.77
#> 9 2 >= 5 Suspend Suspend Suspend
#> 9 3 0 Y
#> 9 3 1 > 0.58 <= 0.58
#> 9 3 2 > 1.65 <= 1.65
#> 9 3 3 > 2.72 <= 2.72
#> 9 3 4 > 3.79 <= 3.79
#> 9 3 >= 5 Suspend Suspend Suspend
#> 9 4 <= 5 Y
#> 9 >= 5 <= 4 Y & Elim
#> 12 0 <= 6 Y
#> 12 0 >= 7 Suspend Suspend Suspend
#> 12 1 <= 6 Y
#> 12 1 >= 7 Suspend Suspend Suspend
#> 12 2 <= 3 Y
#> 12 2 4 >= 1.33 < 1.33
#> 12 2 5 >= 2.72 < 2.72
#> 12 2 6 >= 4.11 < 4.11
#> 12 2 >= 7 Suspend Suspend Suspend
#> 12 3 <= 6 Y
#> 12 3 >= 7 Suspend Suspend Suspend
#> 12 4 0 Y
#> 12 4 1 > 0.43 <= 0.43
#> 12 4 2 > 1.50 <= 1.50
#> 12 4 3 > 2.57 <= 2.57
#> 12 4 4 > 3.65 <= 3.65
#> 12 4 5 > 4.72 <= 4.72
#> 12 4 6 > 5.79 <= 5.79
#> 12 4 >= 7 Suspend Suspend Suspend
#> 12 5, 6 <= 7 Y
#> 12 >= 7 <= 5 Y & Elim
#> 15 0 <= 7 Y
#> 15 0 >= 8 Suspend Suspend Suspend
#> 15 1 <= 7 Y
#> 15 1 >= 8 Suspend Suspend Suspend
#> 15 2 <= 5 Y
#> 15 2 6 >= 0.35 < 0.35
#> 15 2 7 >= 2.07 < 2.07
#> 15 2 >= 8 Suspend Suspend Suspend
#> 15 3 <= 1 Y
#> 15 3 2 >= 0.11 < 0.11
#> 15 3 3 >= 1.29 < 1.29
#> 15 3 4 >= 2.46 < 2.46
#> 15 3 5 >= 3.64 < 3.64
#> 15 3 6 >= 4.81 < 4.81
#> 15 3 7 >= 5.98 < 5.98
#> 15 3 >= 8 Suspend Suspend Suspend
#> 15 4 <= 7 Y
#> 15 4 >= 8 Suspend Suspend Suspend
#> 15 5 0 Y
#> 15 5 1 > 0.28 <= 0.28
#> 15 5 2 > 1.35 <= 1.35
#> 15 5 3 > 2.42 <= 2.42
#> 15 5 4 > 3.50 <= 3.50
#> 15 5 5 > 4.57 <= 4.57
#> 15 5 6 > 5.64 <= 5.64
#> 15 5 7 > 6.72 <= 6.72
#> 15 5 >= 8 Suspend Suspend Suspend
#> 15 6, 7 <= 9 Y
#> 15 >= 8 <= 7 Y & Elim
#>
#> Patients = treated at the current dose, DLTs = DLTs observed, Pending = assessment not yet completed
#> STFT = total follow-up time of the pending patients divided by the length of the assessment window
#> Accrual is suspended when more than 50% of the patients are pending
#> Y & Elim = de-escalate and eliminate this dose and all higher doses
# Boundaries with three decimal places
print(decisions[decisions$n == 9, ], digits = 3)
#> TITE-BOIN decision table with target 0.3
#> Method: single mean imputation (Yuan et al., 2018); boundaries refer to STFT
#>
#> Patients DLTs Pending Escalate Stay De-escalate
#> 9 0 <= 4 Y
#> 9 0 >= 5 Suspend Suspend Suspend
#> 9 1 <= 4 Y
#> 9 1 >= 5 Suspend Suspend Suspend
#> 9 2 0 Y
#> 9 2 1 >= 0.591 < 0.591
#> 9 2 2 >= 1.651 < 1.651
#> 9 2 3 >= 2.710 < 2.710
#> 9 2 4 >= 3.770 < 3.770
#> 9 2 >= 5 Suspend Suspend Suspend
#> 9 3 0 Y
#> 9 3 1 > 0.579 <= 0.579
#> 9 3 2 > 1.651 <= 1.651
#> 9 3 3 > 2.723 <= 2.723
#> 9 3 4 > 3.795 <= 3.795
#> 9 3 >= 5 Suspend Suspend Suspend
#> 9 4 <= 5 Y
#> 9 >= 5 <= 4 Y & Elim
#>
#> Patients = treated at the current dose, DLTs = DLTs observed, Pending = assessment not yet completed
#> STFT = total follow-up time of the pending patients divided by the length of the assessment window
#> Accrual is suspended when more than 50% of the patients are pending
#> Y & Elim = de-escalate and eliminate this dose and all higher doses