| select_mtd_TITE_QuasiBOIN {TITEgBOIN} | R Documentation |
Obtain the maximum tolerated dose (MTD) of Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN)/Time-to-event bayesian optimal interval (TITEBOIN)/Time-to-event generalized bayesian optimal interval (TITEgBOIN) designs
Description
Obtain the maximum tolerated dose (MTD) of 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) (Takeda et al. 2022) designs
Usage
select_mtd_TITE_QuasiBOIN(target,ntox, npts, Neli=3, cutoff.eli = 0.95,
extrasafe = FALSE, offset = 0.05,print = FALSE,
gdesign=FALSE)
Arguments
target |
the target toxicity probability (example: target <- 0.30) or the target normalized equivalent toxicity score (ETS) (example: target <- 0.47 / 1.5). |
ntox |
number of patients with dose limiting toxicity (DLT) or the sum of normalized equivalent toxicity score (ETS). |
npts |
the number of patients enrolled at each dose level. |
Neli |
the sample size cutoff for elimination. The default is Neli=3. |
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. |
print |
print the additional result or not. The default value is print=FALSE. |
gdesign |
for Bayesian optimal interval (BOIN) and Time-to-event bayesian optimal interval (TITEBOIN), "FALSE" should be assigned. for Generalized Bayesian optimal interval (gBOIN) and Time-to-event generalized bayesian optimal interval (TITEgBOIN), "TRUE" should be assigned . The default is gdesign=FALSE. |
Value
select_mtd_TITE_QuasiBOIN() returns the selected dose
Author(s)
Jing Zhu, Jun Zhang, Kentato Takeda
References
1. 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.
2. Yuan, Y., Hess, K. R., Hilsenbeck, S. G., & Gilbert, M. R. (2016). Bayesian optimal interval design: a simple and well-performing design for phase I oncology trials. Clinical Cancer Research, 22(17), 4291-4301.
3. Zhou, H., Yuan, Y., & Nie, L. (2018). Accuracy, safety, and reliability of novel phase I trial designs. Clinical Cancer Research, 24(18), 4357-4364.
4. Zhou, Y., Lin, R., Kuo, Y. W., Lee, J. J., & Yuan, Y. (2021). BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials. JCO Clinical Cancer Informatics, 5, 91-101.
5. Takeda K, Xia Q, Liu S, Rong A. TITE-gBOIN: Time-to-event Bayesian optimal interval design to accelerate dose-finding accounting for toxicity grades. Pharm Stat. 2022 Mar;21(2):496-506. doi: 10.1002/pst.2182. Epub 2021 Dec 3. PMID: 34862715.
6. Yuan, Y., Lin, R., Li, D., Nie, L. and Warren, K.E. (2018). Time-to-event Bayesian Optimal Interval Design to Accelerate Phase I Trials. Clinical Cancer Research, 24(20): 4921-4930.
7. Rongji Mu, Ying Yuan, Jin Xu, Sumithra J. Mandrekar, Jun Yin, gBOIN: A Unified Model-Assisted Phase I Trial Design Accounting for Toxicity Grades, and Binary or Continuous End Points, Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 68, Issue 2, February 2019, Pages 289–308, https://doi.org/10.1111/rssc.12263.
8. Lin R, Yuan Y. Time-to-event model-assisted designs for dose-finding trials with delayed toxicity. Biostatistics. 2020 Oct 1;21(4):807-824. doi: 10.1093/biostatistics/kxz007. PMID: 30984972; PMCID: PMC8559898.
9. Hsu C, Pan H, Mu R (2022). _UnifiedDoseFinding: Dose-Finding Methods for Non-Binary Outcomes_. R package version 0.1.9, <https://CRAN.R-project.org/package=UnifiedDoseFinding>.
Examples
#For Bayesian optimal interval (BOIN) design/Time-to-event bayesian optimal interval (TITEBOIN)
#design
target<-0.3
y<-c(0,0,1,2,3,0)
n<-c(3,3,6,9,9,0)
select_mtd_TITE_QuasiBOIN(target=target,ntox=y,npts=n,print=TRUE,gdesign=FALSE)
#For Generalized Bayesian optimal interval (gBOIN) design/Time-to-event generalized bayesian
#optimal interval (TITEgBOIN) design
target<-0.47/1.5
y<-c(0,0,2/1.5,3.5/1.5,5.5/1.5,0)
n<-c(3,3,6,9,9,0)
select_mtd_TITE_QuasiBOIN(target=target,ntox=y,npts=n,print=TRUE,gdesign=TRUE)