We describe a design for cancer phase I clinical trials that takes into account patients heterogeneity thought to be related to treatment susceptibility. The goal is to estimate the maximum tolerated dose (MTD) given patient’s specific dichotomous covariate value. The design is Bayesian adaptive and is an extension of escalation with overdose control (EWOC). We will assess the performance of this method by comparing the following designs via extensive simulations: (1) design using a covariate; patients are accrued to the trial sequentially and the dose given to a patient depends on his/her baseline covariate value, (2) design ignoring the covariate; patients are accrued to the trial sequentially and the dose given to a patient does not depend on his/her baseline covariate value, and (3) design using separate trials; in each group, patients are accrued to the trial sequentially and EWOC is implemented in each group. These designs are compared with respect to safety of the trial and efficiency of the estimates of the MTDs via extensive simulations. We found that ignoring a significant baseline binary covariate in the model results in a substantial number of patients being overdosed. On the other hand, accounting for a nonsignificant covariate in the model has practically no effect on the safety of the trial and efficiency of the estimates of the MTDs. 1. Introduction The main objective of cancer phase I clinical trials is to determine a maximum tolerated dose (MTD) of a new experimental drug or combination of known drugs for use in a phase II trial. These trials enroll advanced stage cancer patients who have exhausted all standard therapies sequentially in cohorts of size one or more patients and dose level assignment to a given cohort of patients is dependent upon the dose levels and toxicity outcomes of the previously treated cohorts of patients. Adaptive statistical designs for cancer phase I clinical trials have been studied extensively in the last two decades, see for example, O’Quigley et al. [1], Durham and Flournoy [2], Korn et al. [3], Whitehead [4], Babb et al. [5], Gasparini and Eisele [6], Mukhopadhyay [7], and Haines et al. [8]. See also Ting [9] and Chevret [10] for a more comprehensive review of these statistical designs. A key assumption implied by the definition of the phase I target dose (MTD) is that every subgroup of the patient population has the same MTD. That is, it is assumed that the patient population is homogeneous in terms of treatment tolerance and every patient should be treated at the same dose. As a result, no allowance is
References
[1]
J. O'Quigley, M. Pepe, and L. Fisher, “Continual reassessment method: a practical design for phase 1 clinical trials in cancer,” Biometrics, vol. 46, no. 1, pp. 33–48, 1990.
[2]
S. D. Durham and N. Flournoy, Random Walks for Quantile Estimation, Springer, New York, NY, USA, 1994.
[3]
E. L. Korn, D. Midthune, T. T. Chen, L. V. Rubinstein, M. C. Christian, and R. M. Simon, “A comparison of two phase I trial designs,” Statistics in Medicine, vol. 13, no. 18, pp. 1799–1806, 1994.
[4]
J. Whitehead, “Bayesian decision procedures with application to dose-finding studies,” International Journal of Pharmaceutical Medicine, vol. 11, no. 4, pp. 201–208, 1997.
[5]
J. Babb, A. Rogatko, and S. Zacks, “Cancer Phase I clinical Trials: efficient dose escalation with overdose control,” Statistics in Medicine, vol. 17, pp. 1103–1120, 1998.
[6]
M. Gasparini and J. Eisele, “A curve-free method for phase I clinical trials,” Biometrics, vol. 56, no. 2, pp. 609–615, 2000.
[7]
S. Mukhopadhyay, “Bayesian nonparametric inference on the dose level with specified response rate,” Biometrics, vol. 56, no. 1, pp. 220–226, 2000.
[8]
L. M. Haines, I. Perevozskaya, and W. F. Rosenberger, “Bayesian optimal designs for phase I clinical trials,” Biometrics, vol. 59, no. 3, pp. 591–600, 2003.
[9]
N. Ting, Dose Finding in Drug Development, Springer, New York, NY, USA, 1st edition, 2006.
[10]
S. Chevret, Statistical Methods for Dose-Finding Experiments, John Wiley & Sons, Chichester, UK, 2006.
[11]
G. Decoster, G. Stein, and E. E. Holdener, “Original article: responses and toxic deaths in Phase I clinical trials,” Annals of Oncology, vol. 1, no. 3, pp. 175–181, 1990.
[12]
M. J. Ratain, R. Mick, R. L. Schilsky, and M. Siegler, “Statistical and ethical issues in the design and conduct of phase I and II clinical trials of new anticancer agents,” Journal of the National Cancer Institute, vol. 85, no. 20, pp. 1637–1643, 1993.
[13]
M. J. Ratain, R. Mick, L. Janisch et al., “Individualized dosing of amonafide based on a pharmacodynamic model incorporating acetylator phenotype and gender,” Pharmacogenetics, vol. 6, no. 1, pp. 93–101, 1996.
[14]
D. R. Newell, “Pharmacologically based phase I trials in cancer chemotherapy,” Hematology, vol. 8, pp. 257–275, 1994.
[15]
J. O'Quigley, L. Z. Shen, and A. Gamst, “Two-sample continual reassessment method,” Journal of Biopharmaceutical Statistics, vol. 9, no. 1, pp. 17–44, 1999.
[16]
J. O'Quigley and X. Paoletti, “Continual reassessment method for ordered groups,” Biometrics, vol. 59, no. 2, pp. 430–440, 2003.
[17]
J. S. Babb and A. Rogatko, “Patient specific dosing in a cancer phase I clinical trial,” Statistics in Medicine, vol. 20, no. 14, pp. 2079–2090, 2001.
[18]
M. Tighiouart, A. Rogatko, and J. S. Babb, “Flexible Bayesian methods for cancer phase I clinical trials. Dose escalation with overdose control,” Statistics in Medicine, vol. 24, no. 14, pp. 2183–2196, 2005.
