An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology
Restricted accessResearch articleFirst published online February, 2020
An adaptive design for the identification of the optimal dose using joint modeling of continuous repeated biomarker measurements and time-to-toxicity in phase I/II clinical trials in oncology
We present a new adaptive dose-finding method, based on a joint modeling of longitudinal continuous biomarker activity measurements and time to first dose limiting toxicity, with a shared random effect. Estimation relies on likelihood that does not require approximation, an important property in the context of small sample sizes, typical of phase I/II trials. We address the important case of missing at random data that stem from unacceptable toxicity, lack of activity and rapid deterioration of phase I patients. The objective is to determine the lowest dose within a range of highly active doses, under the constraint of not exceeding the maximum tolerated dose. The maximum tolerated dose is associated to some cumulative risk of dose limiting toxicity over a predefined number of treatment cycles. Operating characteristics are explored via simulations in various scenarios.
Le TourneauCLeeJJSiuLL. Dose escalation methods in phase I cancer clinical trials. J Natl Cancer Inst2009; 101: 708–720.
2.
Postel-VinaySGomez-RocaCMolifeLR, et al.Phase I trials of molecularly targeted agents: should we pay more attention to late toxicities?. J Clin Oncol2011; 29: 1728–1735.
3.
ChampiatSLambotteOBarreauE, et al.Management of immune checkpoint blockade dysimmune toxicities: a collaborative position paper. Ann Oncol2016; 27: 559–574.
4.
KummarSGutierrezMDoroshowJH, et al.Drug development in oncology: classical cytotoxics and molecularly targeted agents. Br J Clin Pharmacol2006; 62: 15–26.
5.
O'QuigleyJHughesMDFentonT. Dose-finding designs for HIV studies. Biometrics2001; 57: 1018–1029.
6.
ThallPFCookJD. Dose-finding based on efficacy – toxicity trade-offs. Biometrics2004; 60: 684–693.
7.
ZhangWSargentDJMandrekarS. An adaptive dose-finding design incorporating both toxicity and efficacy. Stat Med2006; 25: 2365–2383.
ZangYLeeJJYuanY. Adaptive designs for identifying optimal biological dose for molecularly targeted agents. Clin Trials2015; 11: 319–327.
11.
RiviereMKYuanYJourdanJH, et al.Phase I/II dose-finding design for molecularly targeted agent: plateau determination using adaptive randomization. Stat Meth Med Res2016, pp. 1–16. http://smm.sagepub.com/cgi/doi/10.1177/0962280216631763 (accessed 16 January 2019).
12.
ZangYLeeJJ. A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials. Stat Med2017; 36: 27–42.
13.
HsiHBartonCActonG, et al.Trends in the characteristics, dose-limiting toxicities and efficacy of phase I oncology trials: The Cancer Research UK experience. Eur J Cancer2016; 66: 9–16. http://doi.org/10.1016/j.ejca.2016.07.004 (accessed 29 January 2019).
14.
BekeleBNShenY. A Bayesian approach to jointly modeling toxicity and biomarker expression in a phase I/II dose-finding trial. Biometrics2005; 61: 344–354.
15.
HirakawaA. An adaptive dose-finding approach for correlated bivariate binary and continuous outcomes in phase I oncology trials. Stat Med2012; 31: 516–532.
16.
YeungWYWhiteheadJReignerB, et al.Bayesian adaptive dose-escalation procedures for binary and continuous responses utilizing a gain function. Pharmaceut Stat2015; 14: 479–487.
17.
YeungWYReignerBBeyerU, et al.Bayesian adaptive dose-escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy. Pharmaceut Stat2017, pp. 1–18. https://doi-wiley-com.web.bisu.edu.cn/10.1002/pst.1818 (accessed 16 January 2019).
18.
YuanYYinG. Bayesian dose finding by jointly modelling toxicity and efficacy as time-to-event outcomes. J Royal Stat Soc2009; 58: 719–736.
19.
KoopmeinersJSModianoJ. A Bayesian adaptive phase I–II clinical trial for evaluating efficacy and toxicity with delayed outcomes. Clin Trials2014; 11: 38–48.
20.
JinIHLiuSThallPF, et al.Using data augmentation to facilitate conduct of phase I–II clinical trials with delayed outcomes. J Am Stat Assoc2014; 109: 525–536.
21.
Guo B and Yuan Y. A Bayesian dose-finding design for phase I/II clinical trials with non-ignorable dropouts. Statistics in Medicine 2016; 34: 1721–1732.
22.
Postel-VinaySColletteLPaolettiX, et al.Towards new methods for the determination of dose limiting toxicities and the assessment of the recommended dose for further studies of molecularly targeted agents – Dose-Limiting Toxicity and Toxicity Assessment Recommendation Group for Early Trials of Targeted therapies, an European Organisation for Research and Treatment of Cancer-led study. Eur J Cancer2014; 50: 2040–2049. http://doi.org/10.1016/j.ejca.2014.04.031 (accessed 9 October 2018).
23.
RustinGJSVergoteIEisenhauerE, et al.Definitions for response and progression in ovarian cancer clinical trials incorporating RECIST 1.1 and CA 125 agreed by the Gynecological Cancer Intergroup (GCIG). Int J Gynecol Cancer2011; 21: 419–423.
24.
ZhouCClampABackenA, et al.Systematic analysis of circulating soluble angiogenesis-associated proteins in ICON7 identifies Tie2 as a biomarker of vascular progression on bevacizumab. Br J Cancer2016; 115: 228–235.
25.
Rizopoulos D. Joint models for longitudinal and time-to-event data with applications in R. Boca Raton, FL: Chapman and Hall/CRC Biostatistics Series, 2012.
26.
O'QuigleyJPepeMFisherL. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics1990; 46: 33–48.
27.
RouanetAHelmerCDartiguesJF, et al.Interpretation of mixed models and marginal models with cohort attrition due to death and drop-out. Stat Meth Med Res2017; 28: 343–356. .
28.
ArnoldBC. Flexible univariate and multivariate models based on hidden truncation. J Stat Plan Inference2009; 139: 3741–3749.
29.
BarrettJDigglePHendersonR, et al.Joint modelling of repeated measurements and time-to-event outcomes: Flexible model specification and exact likelihood inference. J Royal Stat Soc Series B: Stat Methodol2015; 77: 131–148.
30.
O'QuigleyJShenLZ. Continual reassessment method: a likelihood approach. Biometrics1996; 52: 673–684.
31.
SinclairKWhiteheadA. A Bayesian approach to dose-finding studies for cancer therapies: Incorporating later cycles of therapy. Stat Med2014; 33: 2665–2680.
32.
IvanovaAQaqishBFSchellMJ. Continuous toxicity monitoring in phase II trials in oncology. Biometrics2005; 61: 540–545.
33.
McCullochCENeuhausJMOlinRL. Biased and unbiased estimation in longitudinal studies with informative visit processes. Biometrics2016; 72: 1315–1324.
34.
JakiTAndréVSuTl, et al.Designing exploratory cancer trials using change in tumour size as primary endpoint. Statistics in Med2013; 32: 2544–2554.
35.
LinCjWasonJMS. Improving phase II oncology trials using best observed RECIST response as an endpoint by modelling continuous tumour measurements. Stat Med2017; 36: 4616–4626.
36.
WasonJMSJakiT. A review of statistical designs for improving the efficiency of phase II studies in oncology. Stat Meth Med Res2016; 25: 1010–1021. http://doi.org/10.1016/j.ejca.2016.07.004.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
