Modeling recovery curves with application to prostatectomy
Modeling recovery curves with application to prostatectomy
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DOI：
10.1093/biostatistics/kxy002
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发表时间：
2019-10-01
期刊：
影响因子：
2.1
通讯作者：
Gore, John L.
中科院分区：
文献类型：
--
作者：
Wang, Fulton;Rudin, Cynthia;Gore, John L.
在许多临床环境中，患者的结果采用具有恢复曲线形状的标量时间序列的形式，其特征是由于破坏性事件（例如手术）而急剧下降，随后单调平滑地上升到不超过事件前值的渐近水平。我们提出了一个贝叶斯模型，该模型基于破坏性事件之前的可用信息来预测恢复曲线。我们感兴趣的恢复曲线是前列腺切除术后前列腺癌患者的量化性功能。我们说明了我们的模型作为治疗前医疗决策辅助工具的效用，产生既可解释又准确的个性化预测。我们发现协变量关系，同意并补充了现有的医学文献。
In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.