动态风险预测治疗中止使用患者报告的结果数据在NSABP B-35试验的第三阶段NSABP B-35试验
Vinicius F Calsavara1, Norah L Henry2, Ron D Hays3
1Cedars-Sinai Cancer Center, Cedars-Sinai Medical Center, Los Angeles, California.
Cancer prevention research (Philadelphia, Pa.)
|September 27, 2023
概括
部分条件生存模型使用患者报告的结果预测他莫西芬和阿纳斯托治疗中止风险. 这些模型识别高风险患者,用于精确的化疗预防策略.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 临床试验 临床试验
背景情况:
- 预测停止治疗对于在管道癌 in situ (DCIS) 中个性化化疗预防至关重要.
- 患者报告的结果 (PROs) 为动态风险预测提供了有价值的数据.
研究的目的:
- 开发和验证使用PRO数据对他莫西芬和阿纳斯托停药的预测模型.
- 将患者分为高风险和低风险组,以便早期停止治疗.
主要方法:
- 使用了部分有条件的Cox型模型,具有时间依赖的共变量.
- 分析了NSABP B-35临床试验 (患有DCIS的绝经后妇女) 的数据.
- 模型对70%的数据进行了训练,对30%的数据进行了验证,使用Brier分数和AUC评估性能.
主要成果:
- 对于阿纳斯特,基线BMI和诸如失眠和关节疼痛之类的症状预测了停止治疗 (AUC=0.76).
- 对于他莫西芬,基线BMI和诸如认知问题和疼痛等症状预测停止治疗 (AUC=0.78).
- 模型在6个月的时间里分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分层分
结论:
- 部分条件生存模型有效地使用纵向PRO数据预测治疗中止.
- 这些模型可以帮助医疗保健专业人员识别有风险的患者并为干预措施提供信息.
- 开发了一个基于网络的计算器来支持临床决策.
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