Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

IF 0.9 4区 数学 Q3 Mathematics Statistical Applications in Genetics and Molecular Biology Pub Date : 2024-05-13 eCollection Date: 2024-01-01 DOI:10.1515/sagmb-2023-0038
Anand Hari, Edakkalathoor George Jinto, Divya Dennis, Kumarapillai Mohanan Nair Jagathnath Krishna, Preethi S George, Sivasevan Roshni, Aleyamma Mathew
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Abstract

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

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癌症生存数据纵向和时间到事件联合建模中基线危害的选择。
纵向时间到事件分析是一种统计方法,用于分析重复测量协变量的数据。在生存研究中,对于外生的时间依赖性协变量,采用 Cox 比例危险模型或扩展 Cox 模型估算事件风险。然而,这些模型并不适用于内生的时间依赖性协变量,如纵向测量的生物标志物癌胚抗原(CEA)。有人提出了可同时对纵向协变量和时间到事件数据建模的联合模型作为替代方案。本研究强调了选择基线危险度以获得更准确风险估计的重要性。本研究使用结肠癌患者数据来说明和比较四种不同的联合模型,这些模型根据基线危险度的选择而有所不同[片断常数高斯-赫米特(GH)、片断常数伪适应 GH、带 GH 的 Weibull 加速失效时间模型和 B 样条 GH]。我们进行了模拟研究,以评估不同样本量(N = 100、250、500)和剔除比例(20%、50%、70%)下模型的一致性。在结肠癌患者数据中,根据阿凯克信息准则(AIC)和贝叶斯信息准则(BIC),片断恒定伪适应 GH 被认为是拟合效果最好的模型。尽管模型拟合度不同,但四个模型得出的危害值相似。该研究发现,综合分期是结肠癌患者从发病到死亡时间的预后因素,而纵向结果 CEA 则是结肠癌患者总生存期的动态预测因素。模拟研究发现,Piecewise-PH-aGH 是最佳模型,其 AIC 和 BIC 值最小,覆盖概率(CP)最高。而所有模型的偏差和均方误差(RMSE)都表现出了很强的竞争力。然而,在大多数组合中,Piecewise-PH-aGH 的偏差和 RMSE 值最小,计算时间最短,这表明了它的计算效率。本研究首次讨论了基线危害的选择问题。
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来源期刊
CiteScore
1.20
自引率
11.10%
发文量
8
审稿时长
6-12 weeks
期刊介绍: Statistical Applications in Genetics and Molecular Biology seeks to publish significant research on the application of statistical ideas to problems arising from computational biology. The focus of the papers should be on the relevant statistical issues but should contain a succinct description of the relevant biological problem being considered. The range of topics is wide and will include topics such as linkage mapping, association studies, gene finding and sequence alignment, protein structure prediction, design and analysis of microarray data, molecular evolution and phylogenetic trees, DNA topology, and data base search strategies. Both original research and review articles will be warmly received.
期刊最新文献
Empirically adjusted fixed-effects meta-analysis methods in genomic studies. A CNN-CBAM-BIGRU model for protein function prediction. A heavy-tailed model for analyzing miRNA-seq raw read counts. Flexible model-based non-negative matrix factorization with application to mutational signatures. Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.
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