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Shift Models for Dose-Finding in Ordered Groups with Late-Onset Toxicity
Rami Hawila   Nolan A. Wages  

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https://doi.org/10.51387/25-NEJSDS88
Pub. online: 9 July 2025      Type: Methodology Article      Open accessOpen Access
Area: Cancer Research

Accepted
21 May 2025
Published
9 July 2025

Abstract

Historically, the primary objective of Phase I clinical trials has been to pick an optimal dose in terms of patient safety, referred to as the maximum tolerated dose (MTD). Most of these trials recommend a “one-size-fits-all” dose for the patient population being studied, while also solely focusing on short-term adverse events. Often patient heterogeneity exists so that group-specific dose selection is of interest. To address the issue of patient heterogeneity, several dose-finding methods have been proposed, including the shift model framework based on the Continual Reassessment Method (CRM). Additionally, for many cancer therapies, relevant toxicities may be defined by participants experiencing adverse events at any point over a long evaluation window, resulting in pending outcomes when new participants need to be assigned a dose. By leveraging the CRM, the time-to-event continual reassessment method (TITE-CRM) provides a feasible approach for addressing this issue. Motivated by a Phase I trial involving radiotherapy that included two patient groups conducted at the University of Virginia, we have developed a hybrid design that combines elements from the TITE-CRM and the shift model framework. This approach helps address patient heterogeneity and late-onset toxicity simultaneously. We illustrate how to perform a dose-finding trial using the proposed design, and compare its operating characteristics to other suggested methods in the field by conducting a simulation study. The shift model TITE-CRM is shown to be a practical design with good operating characteristics in regard to selecting the correct MTD in each group. An R package is also being developed, allowing investigators to provide group-specific MTD recommendations by applying the proposed design, in addition to providing operating characteristics for custom simulation settings.

Supplementary material

 Supplementary Material
The supplementary material includes additional simulation results, detailing patient allocation, varying sample sizes, target DLT rates, and accrual rates.

References

[1] 
Bekele, B.N., Ji, Y., Shen, Y. and Thall, P. F. Monitoring late-onset toxicities in phase I trials using predicted risks. Biostatistics 9(3) 442–457 (2008).
[2] 
Chapple, A. G. and Thall, P. F. Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination. Pharmaceutical statistics 17(6) 734–749 (2018).
[3] 
Cheung, K. and Duong, M. J. Package ‘dfcrm’ (2013).
[4] 
Cheung, Y. K. Coherence principles in dose-finding studies. Biometrika 92(4) 863–873 (2005). https://doi.org/10.1093/biomet/92.4.863. MR2234191
[5] 
Cheung, Y. K. Dose finding by the continual reassessment method. CRC Press (2011).
[6] 
Cheung, Y. K. and Chappell, R. Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56(4) 1177–1182 (2000). https://doi.org/10.1111/j.0006-341X.2000.01177.x. MR1815616
[7] 
Horton, B. J. and Wages, N. A. In Continual Reassessment Method. Wiley StatsRef: Statistics Reference Online 1–7 (2014).
[8] 
Horton, B. J., O’Quigley, J. and Conaway, M. R. Consequences of performing parallel dose finding trials in heterogeneous groups of patients. JNCI Cancer Spectrum 3(2):pkz013 (2019).
[9] 
Horton, B. J., Wages, N. A. and Conaway, M. R. Shift models for dose-finding in partially ordered groups. Clinical Trials 16(1) 32–40 (2019).
[10] 
Lee, S. M. and Cheung, Y. K. Model calibration in the continual reassessment method. Clinical Trials 6(3) 227–238 (2009).
[11] 
Lee, S. M. and Cheung, Y. K. Calibration of prior variance in the Bayesian continual reassessment method. Statistics in medicine 30(17) 2081–2089 (2011). https://doi.org/10.1002/sim.4139. MR2829158
[12] 
Legedza, A. T. R. and Ibrahim, J. G. Heterogeneity in phase I clinical trials: prior elicitation and computation using the continual reassessment method. Statistics in Medicine 20(6) 867–882 (2001). https://doi.org/10.5351/KJAS.2002.15.2.323. MR1998919
[13] 
Liu, S., Yin, G. and Yuan, Y. Bayesian data augmentation dose finding with continual reassessment method and delayed toxicity. The annals of applied statistics 7(4) 1837 (2013). https://doi.org/10.1214/13-AOAS661. MR3161716
[14] 
McGovern, A., Chapple, A. G. and Ma, C. Two-stage subgroup-specific time-to-event (2s-sub-tite): An adaptive two-stage time-to-toxicity design for subgroup-specific dose finding in phase I oncology trials. Pharmaceutical Statistics 21(6) 1138–1148 (2022).
[15] 
Muller, D. A., Wages, N. A., Wilson, D. D., Dutta, S. W., Alonso, C. E., Handsfield, L. L., Chen, Q., Smith, A. B., Romano, K. D., Janowski, E. M. et al. STAT RAD: prospective dose escalation clinical trial of single fraction scan-plan-QA-treat stereotactic body radiation therapy for painful osseous metastases. Practical Radiation Oncology 10(6) e444–e451 (2020).
[16] 
Neaga, A., Jimbu, L., Mesaros, O., Bota, M., Lazar, D., Cainap, S., Blag, C. and Zdrenghea, M. Why do children with acute lymphoblastic leukemia fare better than adults? Cancers 13(15) 3886 (2021).
[17] 
O’Quigley, J. and Shen, L. Z. Continual reassessment method: a likelihood approach. Biometrics. 673–684 (1996).
[18] 
O’Quigley, J., Pepe, M. and Fisher, L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics. 33–48 (1990). https://doi.org/10.2307/2531628. MR1059105
[19] 
O’Quigley, J., Shen, L. Z. and Gamst, A. Two-sample continual reassessment method. Journal of biopharmaceutical statistics 9(1) 17–44 (1999).
[20] 
O’Quigley, J. Theoretical study of the continual reassessment method. Journal of Statistical Planning and Inference 136(6) 1765–1780 (2006). https://doi.org/10.1016/j.jspi.2005.08.003. MR2255595
[21] 
Salter, A., Morgan, C. and Aban, I. B. Implementation of a two-group likelihood time-to-event continual reassessment method using sas. Computer methods and programs in biomedicine 121(3) 189–196 (2015).
[22] 
Salter, A., O’Quigley, J., Cutter, G. R. and Aban, I. B. Two-group time-to-event continual reassessment method using likelihood estimation. Contemporary clinical trials 45. 340–345 (2015).
[23] 
Thall, P. F., Lee, J. J., Tseng, C.-H. and Estey, E. H. Accrual strategies for phase I trials with delayed patient outcome. Statistics in Medicine 18(10) 1155–1169 (1999).
[24] 
Wages, N. A. and Petroni, G. R. A web tool for designing and conducting phase I trials using the continual reassessment method. BMC cancer 18(1) 1–8 (2018). MR3593209
[25] 
Wages, N. A., Conaway, M. R. and O’Quigley, J. Performance of two-stage continual reassessment method relative to an optimal benchmark. Clinical Trials 10(6) 862–875 (2013).
[26] 
Wages, N. A., Read, P. W. and Petroni, G. R. A phase I/II adaptive design for heterogeneous groups with application to a stereotactic body radiation therapy trial. Pharmaceutical statistics 14(4) 302–310 (2015). https://doi.org/10.1080/10543406.2010.514461. MR2758355
[27] 
Wages, N. A., Braun, T. M. and Conaway, M. R. Isotonic design for phase i cancer clinical trials with late-onset toxicities. Journal of Biopharmaceutical Statistics 33(3) 357–370 (2023).
[28] 
Wheeler, G. M., Sweeting, M. J. and Mander, A. P. Toxicity-dependent feasibility bounds for the escalation with overdose control approach in phase i cancer trials. Statistics in medicine 36(16) 2499–2513 (2017). https://doi.org/10.1002/sim.7280. MR3660146
[29] 
Xue, X., Foster, M. C. and Ivanova, A. Rapid enrollment design for finding the optimal dose in immunotherapy trials with ordered groups. Journal of biopharmaceutical statistics 29(4) 625–634 (2019).
[30] 
Yin, G., Zheng, S. and Xu, J. Fractional dose-finding methods with late-onset toxicity in phase i clinical trials. Journal of Biopharmaceutical Statistics 23(4) 856–870 (2013). https://doi.org/10.1080/10543406.2013.789892. MR3196080
[31] 
Yuan, Y., Lin, R., Li, D., Nie, L. and Warren, K. E. Time-to-event Bayesian optimal interval design to accelerate phase I trials. Clinical Cancer Research 24(20) 4921–4930 (2018).

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© 2025 New England Statistical Society
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Open access article under the CC BY license.

Keywords
Dose finding designs Late onset toxicities Patient heterogeneity Cancer

Funding
The research of Wages is supported by the National Institute of Health grant R01CA247932. The research of Hawila is supported by funding from Massey Comprehensive Cancer Center.

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