Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer.
Spatially varying effects of predictors for the survival prediction of nonmetastatic colorectal Cancer.
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非转移性结直肠癌生存预测的预测因子的空间差异效应
DOI：
10.1186/s12885-018-4985-2
复制
发表时间：
2018-11-08
期刊：
影响因子：
3.8
通讯作者：
Li J
中科院分区：
文献类型：
--
作者：
Tian Y;Li J;Zhou T;Tong D;Chi S;Kong X;Ding K;Li J
越来越多的研究已经确定了结直肠癌生存率的空间差异。然而，在结直肠癌生存预测模型研究中，对预测因子的空间变化效应知之甚少，这些研究的重点是估计来自广泛人群的患者的绝对生存风险。本研究旨在证明非转移性结直肠癌患者生存预测因子的空间变化效应。2004年至2013年诊断为非转移性结直肠癌并随访至2013年底的患者来自监测流行病学最终结果登记研究（患者：128061）。使用对数秩检验和限制平均生存时间来评估与广泛使用的临床预测因子相对应的空间聚类之间的生存结局差异：AJCC第7版分期系统确定的分期。异质性检验，这是在荟萃分析中使用，揭示了空间变化的影响，单一的预测。然后，考虑到基于空间聚集数据的标准生存预测模型中的上述预测因素，这些模型的空间变化系数表明，在研究的地理区域内，一些协变量效应可能不是恒定的。然后，建立了两种类型的生存预测模型（统计模型和机器学习模型）;这些模型考虑了预测因素，并能够对来自广泛地理区域的患者进行生存预测。基于单因素和多因素分析，一些预后因素，如“TNM分期”，“肿瘤大小”和“诊断时的年龄”在不同地区之间具有显著的空间差异。当考虑这些空间变化的影响时，与统计模型相比，机器学习模型具有更少的假设约束（例如比例风险假设）和更好的预测性能。在比较这两个模型的一致性指数时，发现机器学习模型（0.898[0.895，0.902]）比统计模型（0.732 [0.726，0.738]）更准确。在此基础上，建议在建立涉及大规模多中心研究数据的生存预测模型时，应考虑预测因子的空间变异效应。不受统计假设要求限制的机器学习模型是有前途的替代模型。本文的在线版本（10.1186/s12885-018-4985-2）包含补充材料，可供授权用户使用。
An increasing number of studies have identified spatial differences in colorectal cancer survival. However, little is known about the spatially varying effects of predictors in survival prediction modeling studies of colorectal cancer that have focused on estimating the absolute survival risk for patients from a wide range of populations. This study aimed to demonstrate the spatially varying effects of predictors of survival for nonmetastatic colorectal cancer patients. Patients diagnosed with nonmetastatic colorectal cancer from 2004 to 2013 who were followed up through the end of 2013 were extracted from the Surveillance Epidemiology End Results registry (Patients: 128061). The log-rank test and the restricted mean survival time were used to evaluate survival outcome differences among spatial clusters corresponding to a widely used clinical predictor: stage determined by AJCC 7th edition staging system. The heterogeneity test, which is used in meta-analyses, revealed the spatially varying effects of single predictors. Then, considering the above predictors in a standard survival prediction model based on spatially clustered data, the spatially varying coefficients of these models revealed that some covariate effects may not be constant across the geographic regions of the study. Then, two types of survival prediction models (a statistical model and a machine learning model) were built; these models considered the predictors and enabled survival prediction for patients from a wide range of geographic regions. Based on univariate and multivariate analysis, some prognostic factors, such as “TNM stage”, “tumor size” and “age at diagnosis,” have significant spatially varying effects among different regions. When considering these spatially varying effects, machine learning models have fewer assumption constraints (such as proportional hazard assumptions) and better predictive performance compared with statistical models. Upon comparing the concordance indexes of these two models, the machine learning model was found to be more accurate (0.898[0.895,0.902]) than the statistical model (0.732 [0.726, 0.738]). Based on this study, it’s recommended that the spatially varying effect of predictors should be considered when building survival prediction models involving large-scale and multicenter research data. Machine learning models that are not limited by the requirement of a statistical hypothesis are promising alternative models. The online version of this article (10.1186/s12885-018-4985-2) contains supplementary material, which is available to authorized users.
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DOI：
10.1016/j.jamcollsurg.2015.12.019
发表时间：
2016-03
影响因子：
5.2
作者：
Gabriel E;Attwood K;Thirunavukarasu P;Al-Sukhni E;Boland P;Nurkin S
通讯作者：
Nurkin S
影响因子：
--
作者：
Senthil, Maheswari;Trisal, Vijay;Lai, Lily L.
通讯作者：
Lai, Lily L.
影响因子：
2
作者：
HARRELL, FE;LEE, KL;ROSATI, RA
通讯作者：
ROSATI, RA
影响因子：
4.9
作者：
Dasgupta P;Cramb SM;Aitken JF;Turrell G;Baade PD
通讯作者：
Baade PD
影响因子：
2.5
作者：
Douaiher, Jeffrey;Ravipati, Advaitaa;Are, Chandrakanth
通讯作者：
Are, Chandrakanth