Department of Radiation Medicine and Applied Sciences, University of California, San Diego, La Jolla, California.
Department of Radiation Oncology and the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Int J Radiat Oncol Biol Phys. 2021 May 1;110(1):11-20. doi: 10.1016/j.ijrobp.2020.11.020. Epub 2020 Dec 23.
An overview of common approaches used to assess a dose response for radiation therapy-associated endpoints is presented, using lung toxicity data sets analyzed as a part of the High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic effort as an example. Each component presented (eg, data-driven analysis, dose-response analysis, and calculating uncertainties on model prediction) is addressed using established approaches. Specifically, the maximum likelihood method was used to calculate best parameter values of the commonly used logistic model, the profile-likelihood to calculate confidence intervals on model parameters, and the likelihood ratio to determine whether the observed data fit is statistically significant. The bootstrap method was used to calculate confidence intervals for model predictions. Correlated behavior of model parameters and implication for interpreting dose response are discussed.
本文介绍了评估放射治疗相关终点剂量反应的常用方法概述，使用 High Dose per Fraction, Hypofractionated Treatment Effects in the Clinic 研究中分析的肺毒性数据集作为示例。每个呈现的组件（例如，数据驱动分析、剂量反应分析和计算模型预测的不确定性）都使用既定方法来解决。具体来说，最大似然法用于计算常用逻辑模型的最佳参数值，轮廓似然法用于计算模型参数的置信区间，似然比用于确定观察数据拟合是否具有统计学意义。Bootstrap 方法用于计算模型预测的置信区间。讨论了模型参数的相关性行为及其对解释剂量反应的影响。
