Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time.
Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time.
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DOI：
10.1093/ije/dyaa030
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发表时间：
2020-08-01
影响因子：
7.7
通讯作者：
Rutherford MJ
中科院分区：
文献类型：
--
作者：
Booth S;Riley RD;Ensor J;Lambert PC;Rutherford MJ
预测模型通常是在涵盖长时间段的研究中开发的。然而，如果最近几年生存率有所改善，那么使用完整的数据集可能会导致生存预测过时。周期分析通过在最近时间窗口的数据子集中开发模型来解决这个问题，但会导致样本量减少。我们提出了一种新的方法，称为时间重新校准，结合联合收割机的周期分析和全队列分析的优点。这种方法在整个数据集中建立一个模型，然后使用周期分析样本重新校准基线生存率。这些方法是利用考克斯比例风险和灵活的参数生存模型，从1996年至2005年的监测，流行病学和最终结果（SEER）计划数据库建立结肠癌的预后模型。对2006年确诊并随访至2015年的新患者进行了模型预测与观察到的生存率估计值的比较。与标准全队列模型相比，周期分析和时间重新校准提供了更及时的生存预测，与后续数据中观察到的生存率更接近。此外，时间重新校准提供了更精确的预测效应的估计。预后模型通常使用全队列分析开发，当近年来生存率提高时，可能会导致过时的长期生存率估计。时间重新校准是解决这一问题的一种简单方法，可以在开发和更新预后模型时使用，以确保生存预测与随后诊断的个体的观察生存率更紧密地校准。
Prognostic models are typically developed in studies covering long time periods. However, if more recent years have seen improvements in survival, then using the full dataset may lead to out-of-date survival predictions. Period analysis addresses this by developing the model in a subset of the data from a recent time window, but results in a reduction of sample size. We propose a new approach, called temporal recalibration, to combine the advantages of period analysis and full cohort analysis. This approach develops a model in the entire dataset and then recalibrates the baseline survival using a period analysis sample. The approaches are demonstrated utilizing a prognostic model in colon cancer built using both Cox proportional hazards and flexible parametric survival models with data from 1996–2005 from the Surveillance, Epidemiology, and End Results (SEER) Program database. Comparison of model predictions with observed survival estimates were made for new patients subsequently diagnosed in 2006 and followed-up until 2015. Period analysis and temporal recalibration provided more up-to-date survival predictions that more closely matched observed survival in subsequent data than the standard full cohort models. In addition, temporal recalibration provided more precise estimates of predictor effects. Prognostic models are typically developed using a full cohort analysis that can result in out-of-date long-term survival estimates when survival has improved in recent years. Temporal recalibration is a simple method to address this, which can be used when developing and updating prognostic models to ensure survival predictions are more closely calibrated with the observed survival of individuals diagnosed subsequently.
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影响因子：
2.3
作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
4.2
作者：
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通讯作者：
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