next_TITE_QuasiBOIN {TITEgBOIN} | R Documentation |
next_TITE_QuasiBOIN
Description
Determine the dose for the next cohort of new patients for single-agent trials using 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.
Usage
next_TITE_QuasiBOIN(
target,
n,
npend,
y,
ft,
d,
maxt = 28,
p.saf = 0.6 * target,
p.tox = 1.4 * target,
elimination = NA,
cutoff.eli = 0.95,
extrasafe = FALSE,
offset = 0.05,
n.earlystop = 100,
maxpen = 0.5,
Neli = 3,
print_d = FALSE,
gdesign = FALSE
)
Arguments
target |
The target toxicity probability (example: |
n |
Number of patients treated at each dose level. |
npend |
For Time-to-event Bayesian optimal interval (TITEBOIN)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN), the number of pending patients at each dose level.For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. |
y |
Number of patients with dose limiting toxicity (DLT) or the sum of Normalized equivalent toxicity score (ETS). |
ft |
For Time-to-event Bayesian optimal interval (TITEBOIN)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN), Total follow-up time for pending patients for toxicity at each dose level (days). For Bayesian optimal interval (BOIN)/ Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. |
d |
Current dose level. |
maxt |
For Time-to-event Bayesian optimal interval (TITEBOIN)/Time-to-event generalized Bayesian optimal interval (TITEgBOIN), length of assessment window for toxicity (days). For Bayesian optimal interval (BOIN)/Generalized Bayesian optimal interval (gBOIN), "NA" should be assigned. |
p.saf |
The lower bound. The default value is |
p.tox |
The upper bound. The default value is |
elimination |
Elimination of each dose (0,1 should be assigned, 0 means the dose is not eliminated,
1 means the dose is eliminated due to over toxic( |
cutoff.eli |
The cutoff to eliminate an overly toxic dose for safety.
We recommend the default value of ( |
extrasafe |
Set |
offset |
A small positive number (between 0 and 0.5) to control how strict the
stopping rule is when |
n.earlystop |
The early stopping parameter. The default value is |
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. |
Neli |
The sample size cutoff for elimination. The default is |
print_d |
Print the additional result or not. The default value is |
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 |
Value
next_TITE_QuasiBOIN()
returns the toxicity probability and the recommended dose level for the next cohort
including: (1) the lower Bayesian optimal boundary (lambda_e
)
(2) the upper Bayesian optimal boundary (lambda_d
)
(3) The number of patients or the effective sampe size (ESS) at each dose level (ESS
)
(4) The dose limiting toxicity (DLT) rate or mu (the estimated quasi-Bernoulli toxicity probability) at each dose level (mu
)
(5) the recommended dose level for the next cohort as a numeric value under (d
)
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
target<-0.3
next_TITE_QuasiBOIN(target=target,n=c(3,3,4,4,4,0),npend=NA, y=c(0,0,1,1,1,0), ft=NA,
d=5, maxt=NA,p.saf= 0.6 * target, p.tox = 1.4 * target,elimination=NA,
cutoff.eli = 0.95,extrasafe = FALSE, n.earlystop = 10,
maxpen=NA,print_d = TRUE,gdesign=FALSE)
#For Generalized Bayesian optimal interval (gBOIN) design
target=0.47/1.5
next_TITE_QuasiBOIN(target=target,n=c(3,3,4,4,4,0),npend=NA,
y=c(0, 0, 0.5/1.5, 1.0/1.5, 1.5/1.5, 0),ft=NA, d=5, maxt=NA,
p.saf= 0.6 * target, p.tox = 1.4 * target,elimination=NA,
cutoff.eli = 0.95,extrasafe = FALSE, n.earlystop = 10,
maxpen=NA,print_d = TRUE,gdesign=TRUE)
#For Time-to-event bayesian optimal interval (TITEBOIN) design
target=0.3
next_TITE_QuasiBOIN(target=target,n=c(3,3,4,4,4,0),npend=c(0,0,0,1,2,0), y=c(0,0,1,1,1,0),
ft=c(0, 0, 0, 14, 28, 0),d=5, maxt=28,p.saf= 0.6 * target,
p.tox = 1.4 * target,elimination=NA,cutoff.eli = 0.95,
extrasafe = FALSE, n.earlystop = 10,maxpen=0.5,print_d = TRUE,
gdesign=FALSE)
#For Time-to-event generalized bayesian optimal interval (TITEgBOIN) design
target=0.47/1.5
next_TITE_QuasiBOIN(target=target,n=c(3,3,4,4,4,0),npend=c(0,0,0,1,2,0),
y=c(0, 0, 0.5/1.5, 1.0/1.5, 1.5/1.5, 0),ft=c(0, 0, 0, 14, 28, 0),
d=5, maxt=28,p.saf= 0.6 * target, p.tox = 1.4 * target,
elimination=NA,cutoff.eli = 0.95,extrasafe = FALSE,
n.earlystop = 10,maxpen=0.5,print_d = TRUE,gdesign=TRUE)