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Second, most model-based designs require the conduct of computer simulations before the trial starts, in which statisticians must calibrate the design parameters to achieve desirable operating characteristics. For example, the CRM requires users to provide a set of prior estimates of the toxicity probabilities for the candidate doses to be used in the trial.
This set should be elicited from the investigators. However, the performance of the CRM under the computer simulations will depend on the agreement of these prior estimates and the true toxicity probabilities specified in the simulations. A poorly elicited set of prior estimates will lead to poor operating characteristics. Therefore, tuning of the prior estimates becomes an important but sometimes challenging process.
Recent attempts [ 8 ] have been made to alleviate this issue, although nontrivial calibration is still required. Ji et al. They proposed using simple Bayesian models to describe the observed toxicity data, and introduced a set of decision rules based on toxicity posterior intervals TPI. Their method is implemented in an Excel spreadsheet, which reduces the burden in making dose assignments during an ongoing trial.
The posterior probabilities of these three intervals are used for making dose-assignment decisions. A default set of values for K1 and K2 was proposed in [ 9 ], but it is unclear how sensitive their method is to changes in these values. We have conducted simulations results not shown and found that different values of K1 and K2 could lead to different results. The implementation of the method is essentially effortless, although the underlying statistical theory is not trivial.
Implementation of the mTPI method does not require tuning of model parameters, and computer simulations can be carried out in an Excel macro. In addition, similar to the TPI design in [ 9 ], the mTPI design is transparent in the sense that physicians can see all the possible dose-finding decisions before the trial starts. Consequently, patients enrolled into the trial can be allocated to appropriate doses without conducting additional computations.
For a given interval on the real line, the UPM is defined as the ratio of the probability of the interval based on a probability measure and the length of the interval.
Therefore, one does not need to calibrate the EI for different trials and physicians. Upon determination of the EI, we compute the mTPI of the three resulting toxicity intervals and choose one of three actions, escalating to the higher dose, staying at the same dose, or de-escalating to the lower dose, depending on which corresponds to the interval with the lowest UPM. Dose-finding method The derivation of the dose-finding rules for the mTPI method involves two steps.
In the first step, we introduce an EI, which leads to three toxicity probability intervals that partition 0, 1. Building upon the EI, we set up a decision-theoretic framework and derive a Bayes rule. We show that the Bayes rule is equivalent to computing the UPM for the toxicity probability intervals. Consider d dose levels of a certain cytotoxic drug in a phase I trial.
The trial thus proceeds with the next cohort.