TITEgBOIN (version 0.4.0)

next_TITE_QuasiBOIN: 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
)

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)

Arguments

target

The target toxicity probability (example: target <- 0.30) or the target normalized equivalent toxicity score (ETS) (example: target <- 0.47 / 1.5).

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.saf=0.6*target.

p.tox

The upper bound. The default value is p.tox=1.4*target.

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(elimination=NA, 0 is defaulted for each dose level)).

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.

n.earlystop

The early stopping parameter. The default value is n.earlystop=100.

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 Neli=3.

print_d

Print the additional result or not. The default value is print_d=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.

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)