Abstract
A phase I/II trial design utilizes both toxicity and efficacy outcomes to make the decision of dose assignment for patients. Because assessing the efficacy endpoint often requires a relatively long follow-up time, phase I/II trials are more susceptible to the missing data problem caused by informative dropouts that are correlated with treatment efficacy and toxicity. In addition, patient outcomes may not be scored quickly enough to apply decision rules that choose treatments or doses for newly accrued patients. To address these issues, we propose a Bayesian phase I/II design that jointly models efficacy, toxicity, and dropout as time-to-event data. Correlations among the three time-to-event outcomes are taken into account by a shared frailty. This joint model strategy accounts for the informative dropouts and has an additional advantage of accommodating a high accrual rate without suspending patient enrollment when toxicity or efficacy outcomes require a long follow-up. Under the Bayesian paradigm, we continuously update the posterior estimate of the model and assign incoming patients to the most desirable dose based on an efficacy-toxicity trade-off utility. Simulation studies show that the proposed design has good operating characteristics with a high probability of selecting the target dose and assigning the most patients to the target dose.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 217-226 |
| Number of pages | 10 |
| Journal | Statistics and its Interface |
| Volume | 8 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Externally published | Yes |
Fingerprint
Keywords
- Bayesian adaptive design
- Dose finding
- Missing data
- Nonignorable dropout
- Trade-off
ASJC Scopus subject areas
- Statistics and Probability
- Applied Mathematics
Cite this
A Bayesian phase I/II clinical trial design in the presence of informative dropouts. / Guo, Beibei; Zang, Yong; Yuan, Ying.
In: Statistics and its Interface, Vol. 8, No. 2, 01.01.2015, p. 217-226.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - A Bayesian phase I/II clinical trial design in the presence of informative dropouts
AU - Guo, Beibei
AU - Zang, Yong
AU - Yuan, Ying
PY - 2015/1/1
Y1 - 2015/1/1
N2 - A phase I/II trial design utilizes both toxicity and efficacy outcomes to make the decision of dose assignment for patients. Because assessing the efficacy endpoint often requires a relatively long follow-up time, phase I/II trials are more susceptible to the missing data problem caused by informative dropouts that are correlated with treatment efficacy and toxicity. In addition, patient outcomes may not be scored quickly enough to apply decision rules that choose treatments or doses for newly accrued patients. To address these issues, we propose a Bayesian phase I/II design that jointly models efficacy, toxicity, and dropout as time-to-event data. Correlations among the three time-to-event outcomes are taken into account by a shared frailty. This joint model strategy accounts for the informative dropouts and has an additional advantage of accommodating a high accrual rate without suspending patient enrollment when toxicity or efficacy outcomes require a long follow-up. Under the Bayesian paradigm, we continuously update the posterior estimate of the model and assign incoming patients to the most desirable dose based on an efficacy-toxicity trade-off utility. Simulation studies show that the proposed design has good operating characteristics with a high probability of selecting the target dose and assigning the most patients to the target dose.
AB - A phase I/II trial design utilizes both toxicity and efficacy outcomes to make the decision of dose assignment for patients. Because assessing the efficacy endpoint often requires a relatively long follow-up time, phase I/II trials are more susceptible to the missing data problem caused by informative dropouts that are correlated with treatment efficacy and toxicity. In addition, patient outcomes may not be scored quickly enough to apply decision rules that choose treatments or doses for newly accrued patients. To address these issues, we propose a Bayesian phase I/II design that jointly models efficacy, toxicity, and dropout as time-to-event data. Correlations among the three time-to-event outcomes are taken into account by a shared frailty. This joint model strategy accounts for the informative dropouts and has an additional advantage of accommodating a high accrual rate without suspending patient enrollment when toxicity or efficacy outcomes require a long follow-up. Under the Bayesian paradigm, we continuously update the posterior estimate of the model and assign incoming patients to the most desirable dose based on an efficacy-toxicity trade-off utility. Simulation studies show that the proposed design has good operating characteristics with a high probability of selecting the target dose and assigning the most patients to the target dose.
KW - Bayesian adaptive design
KW - Dose finding
KW - Missing data
KW - Nonignorable dropout
KW - Trade-off
UR - http://www.scopus.com/inward/record.url?scp=84924417289&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84924417289&partnerID=8YFLogxK
U2 - 10.4310/SII.2015.v8.n2.a9
DO - 10.4310/SII.2015.v8.n2.a9
M3 - Article
VL - 8
SP - 217
EP - 226
JO - Statistics and its Interface
JF - Statistics and its Interface
SN - 1938-7989
IS - 2
ER -