使用查数据与干预后审查进行多状态癌症进展建模方法的比较
Eddymurphy U Akwiwu1, Veerle M H Coupé1, Johannes Berkhof1
1Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Epidemiology and Data Science, Amsterdam Public Health, Amsterdam, The Netherlands.
概括
这项研究比较了多州癌症模型的查和监测数据. 贝叶斯TSM方法证明了可靠的风险估计,特别是当癌症前体进展的危险性取决于时间时.
科学领域:
- 生物统计学 生物统计学
- 癌症流行病学 癌症流行病学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 精确的癌症查和监测依赖于了解住院时间和从前恶性病变引起的癌症风险.
- 多州癌症模型估计了这些参数,但当治疗前体时,它们的表现不足,防止癌症进展 (审查设置).
- 这项研究探讨了在这种特定的审查场景中多状态方法的性能.
研究的目的:
- 评估各种多状态癌症建模方法在癌症前体检测后进行治疗的环境中的性能.
- 确定哪些方法在不同的危险假设下提供无偏见的风险估计 (时间独立与时间依赖).
主要方法:
- 我们比较了6个R软件包实现的多状态模型 (msm,cthmm,smms,BayesTSM,hmm).
- 评估的模型具有时间独立的危险和危险取决于从状态进入或过程开始以来的时间.
- 通过模拟和应用对结直肠癌监测数据 (健康,非晚期腺瘤,晚期瘤状态) 评估模型性能.
主要成果:
- 所有的方法都在模拟中表现得很好,具有时间独立的危险.
- 只有smms和BayesTSM在危险取决于进入状态后的时间时,才产生了公正的风险估计.
- 在结直肠癌数据应用中,只有msm,hmm和BayesTSM趋同;BayesTSM和hmm显示了类似的非先进腺瘤风险估计,而先进瘤风险估计有所不同.
结论:
- 危害的时间依赖性显著影响多种癌症模型的性能,特别是对于不可观察的前体到癌症的过渡.
- 贝叶斯TSM提供了可靠和公正的风险估计,特别是在现实的场景中,自进入状态以来的危险取决于时间.
- 选择多状态建模方法至关重要,以避免在癌症查和监测分析中偏见的参数估计.
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