TITEgBOIN

Help for package TITEgBOIN const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {TITEgBOIN} Contents get_oc_TITE_QuasiBOIN next_TITE_QuasiBOIN select_mtd_TITE_QuasiBOIN Type: Package Title: Time-to-Event Dose-Finding Design for Multiple Toxicity Grades Version: 0.4.0 Description: In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) < doi:10.1158/1078-0432.CCR-16-0592 >), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) < doi:10.1111/rssc.12263 >), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) < doi:10.1093/biostatistics/kxz007 >) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) < doi:10.1002/pst.2182 >) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules. License: GPL-2 Encoding: UTF-8 RoxygenNote: 7.3.2 NeedsCompilation: no Author: Jing Zhu [cre, aut], Jun Zhang [aut], Kentaro Takeda [aut] Maintainer: Jing Zhu <zhujing716@gmail.com> Packaged: 2025-02-24 07:26:40 UTC; A4021579 Repository: CRAN Date/Publication: 2025-02-26 06:30:01 UTC get_oc_TITE_QuasiBOIN Description Obtain the operating characteristics of the model-assisted design for single agent trials by simulating trials using Bayesian optimal interval (BOIN) (Yuan et al. 2016)/ Generalized Bayesian optimal interval (gBOIN) (Mu et al. 2019)/Time-to-event Bayesian optimal interval (TITEBOIN) (Lin et al. 2020)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN) designs(Takeda et al. 2022). Usage get_oc_TITE_QuasiBOIN( target, prob, score = c(0, 0.5, 1, 1.5), TITE = TRUE, ncohort, cohortsize, maxt = 1, accrual = 3, maxpen = 0.5, alpha1 = 0.5, alpha2 = 0.5, n.earlystop = 100, Neli = 3, startdose = 1, p.saf = 0.6 * target, p.tox = 1.4 * target, cutoff.eli = 0.95, extrasafe = FALSE, offset = 0.05, ntrial = 1000, seed = 100, titration = FALSE, cap.titration = 0 ) Arguments target The target toxicity probability (example: target <- 0.30 ) or the target normalized equivalent toxicity score (ETS) (example: target <- 0.47 / 1.5 ). prob A vector (Bayesian optimal interval (BOIN) or Time-to-event Bayesian optimal interval (TITEBOIN) design) /matrix (Generalized Bayesian optimal interval (gBOIN) or Time-to-event generalized Bayesian optimal interval (TITEgBOIN) design) containing the true toxicity probabilities of the investigational dose levels. score For Generalized Bayesian optimal interval (gBOIN)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN), a vector containing the relative severity of different toxicity grades in terms of dose limiting toxicity (DLTs) in the dose-finding procedure. As default, toxicity grades of 0/1,2,3, and 4 are assigned values of 0,0.5,1,1.5. For Bayesian optimal interval (BOIN)/Time-to-event Bayesian optimal interval (TITEBOIN), "NA" should be assigned. TITE For Time-to-event Bayesian optimal interval (TITEBOIN)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN), "TRUE" should be assigned. For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "FALSE" should be assigned. ncohort The total number of cohorts. cohortsize The cohort size. maxt For Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN), the maximum follow-up time. for Bayesian optimal interval (BOIN)/ Generalized Bayesian optimal interval (gBOIN), if you don't need to get 1, the average trial duration needed for the trial, 2, the standard deviation of average trial duration needed for the trial. Then "NA" should be assigned; If you need to get 1,the average trial duration needed for the trial, 2, the standard deviation of average trial duration needed for the trial. Then please specify the accrual rate and the maximum follow-up time. accrual For Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN), the accrual rate, i.e., the number of patients accrued in 1 unit of time, for Bayesian optimal interval (BOIN)/ Generalized Bayesian optimal interval (gBOIN), if you don't need to get 1,the average trial duration needed for the trial, 2, the standard deviation of average trial duration needed for the trial. Then "NA" should be assigned; if you need to get 1,the average trial duration needed for the trial, 2, the standard deviation of average trial duration needed for the trial, Then please specify the accrual rate and the maximum follow-up time. maxpen For Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN), the upper limit of the ratio of pending patients. For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. alpha1 For Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN), a number from (0,1) that assume toxicity outcomes occurred with probability alpha1 in the last fraction of alpha2 of the assessment window. The default is alpha1=0.5 . For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. alpha2 For Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN), a number from (0,1) that assume toxicity outcomes occurred with probability alpha1 in the last fraction of alpha2 of the assessment window. The default is alpha2=0.5 . For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. n.earlystop The early stopping parameter and the decision is to stay. If the number of patients treated at the current dose reaches n.earlystop, stop the trial and select the maxinum tolerated dose (MTD) based on the observed data. The default value n.earlystop=100 essentially turns off this type of early stopping. Neli The sample size cutoff for elimination. The default is Neli=3 . startdose The starting dose level for the trial. p.saf The lower bound. The default value is p.saf=0.6*target . p.tox The upper bound. The default value is p.tox=1.4*target . cutoff.eli The cutoff to eliminate an overly toxic dose for safety. We recommend the default value of cutoff.eli=0.95 for general use. extrasafe Set extrasafe=TRUE to impose a more stringent stopping rule. offset A small positive number (between 0 and 0.5) to control how strict the stopping rule is when extrasafe=TRUE . A larger value leads to a more strict stopping rule. The default value offset=0.05 generally works well. ntrial The total number of trials to be simulated. seed The seed, The default value is seed = 100 titration set titration=TRUE to perform dose escalation with cohort size = 1 to accelerate dose escalation at the beginning of the trial. The default value titration=FALSE . cap.titration cap the titration up to dose level, set cap.titration=3 to cap the titration up to dose level 3 with cohort size = 1. The default value cap.titration=0 . Details This function generates he operating characteristics of the Bayesian optimal interval (BOIN)/ Generalized Bayesian optimal interval (gBOIN)/Time-to-event Bayesian o