TITEgBOIN (version 0.3.0)

select_mtd_TITE_QuasiBOIN: 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)

Value

select_mtd_TITE_QuasiBOIN() returns the selected dose

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.

Author

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)