Dose-finding design with repeated continuous toxicity and efficacy data

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Introduction Despite the characteristics of molecularly targeted agents (MTA) in oncology, the recommended dose remains typically based on the binary dose-limiting toxicity (DLT) defined as the dose associated with a certain probability of DLT during the first cycle of treatment. This conventional definition may then not be appropriate for MTAs with prolonged administration, for which late and multiple moderate toxicities also deserve attention. Different authors explored more appropriate and richer toxicity endpoints than DLT. The idea of toxicity score accounting for several toxicity types and grades seems interesting for MTA. Methods In this study, we developed a novel adaptive dose-finding design for inclusion of a single continuous biomarker outcome in addition to repeated quasi-continuous toxicity score. A joint multivariate Gaussian model was used for toxicity and efficacy outcomes, considering that toxicity score follows a truncated positive Gaussian distribution. The correlation between toxicity and efficacy outcomes is modelled via a matrix of variance-covariance. Maximum likelihood estimators (MLE) were used to estimate the optimal dose for further trials. This dose was defined as the dose associated with a maximum efficacy and with a toxicity score at each cycle inferior or equal to a given toxicity-score threshold. First, the robustness of the MLE approach was assessed by using different scenarios of toxicity-dose and efficacy-dose relationships. The number of patients was fixed at 10, 20, 30, and 50 in order to study the convergence of MLE. We replicated each scenario 1000 times. Different variance values for toxicity score have been investigated. We used the bias defined by the difference between true and estimated values as metrics of robustness. Secondly, the performance of the dose-finding design is assessed using simulation study. Different scenarios of toxicity-dose and efficacy-dose relationships were used. The performance of the design was measured using the capacity of the design to identify correctly the optimal dose. Results Concerning the robustness of the approach : The bias of all estimators depends on the variance value and the number of patients included. It converges to 0 when the number of subject increases ; for low variance values. The performance of the design presents good results for the overall of scenarios. Conclusion Using repeated toxicity score and efficacy data in dose-finding trials provide information to estimate the optimal dose.