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首页> 外文期刊>Journal of the royal statistical society >Joint modelling of a binary and a continuous outcome measured at two cycles to determine the optimal dose
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Joint modelling of a binary and a continuous outcome measured at two cycles to determine the optimal dose

机译：在两个周期中对二进制和连续结果进行联合建模以确定最佳剂量

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摘要

The optimal dose of targeted treatment in oncology may not be the maximal tolerated dose. Evaluating jointly toxicity and efficacy data is then desirable. We propose an adaptive dose finding approach to identify a dose based on repeated binary toxicity and continuous efficacy outcomes from the first two cycles. Probit and linear Gaussian models are used for the toxicity and efficacy at each cycle respectively. The correlation between toxicity and efficacy outcome is modelled via a latent Gaussian variable. Maximum likelihood estimators are used. Two steps in this design are defined: dose escalation with decision rules based only on toxicity observed at the first cycle; the expansion cohort with decision rules based on both repeated toxicity and efficacy outcomes by using the joint model. We perform simulation studies to assess the operating characteristics of our design. The design has good performance for different scenarios. The percentage of correct selection dose varies from 54% to 84%. There is no effect on the estimation parameters with missing data of toxicity or efficacy at cycle 2. The design then has similar performance. Using repeated toxicity and efficacy data in dose finding trials provides more reliable information to estimate the optimal dose for further trials.
机译：肿瘤学中靶向治疗的最佳剂量可能不是最大耐受剂量。因此，需要联合评估毒性和功效数据。我们提出了一种适应性剂量寻找方法，该方法基于重复的二元毒性和前两个周期的连续功效结果来确定剂量。 Probit模型和线性高斯模型分别用于每个循环的毒性和功效。毒性和功效结果之间的相关性通过潜在的高斯变量建模。使用最大似然估计器。该设计中定义了两个步骤：逐步增加剂量，仅基于在第一个周期观察到的毒性做出决策规则；通过使用联合模型，基于重复毒性和功效结果的决策规则，进行扩展队列。我们进行仿真研究，以评估设计的操作特性。该设计在不同情况下均具有良好的性能。正确选择剂量的百分比从54％到84％不等。缺少第2周期的毒性或功效数据，对估计参数没有影响。因此，该设计具有相似的性能。在剂量寻找试验中使用重复的毒性和功效数据可提供更可靠的信息，以估算进一步试验的最佳剂量。

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