PDF(Pubmed)
Abstract：
BACKGROUND: This study delves into the complex interplay among prostate-specific antigen, alkaline phosphatase, and the temporal dynamics of tumor shrinkage in prostate cancer. By investigating the longitudinal trajectories and time-to-prostate cancer tumor shrinkage, we aim to untangle the intricate patterns of these biomarkers. This understanding is pivotal for gaining profound insights into the multifaceted aspects of prostate cancer progression. The joint model approach serves as a comprehensive framework, facilitating the elucidation of intricate interactions among these pivotal elements within the context of prostate cancer .
METHODS: A new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model\'s utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients\' baseline characteristics ( Age , body mass index ( BMI ), GleasonScore , Grade , and Drug ) and two longitudinal endogenous covariates ( Platelets and Bilirubin ).
RESULTS: The results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression.
CONCLUSIONS: The analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.
METHODS: A new joint model under a shared parameters strategy is proposed for mixed bivariate longitudinal biomarkers and event time data, for obtaining accurate estimates in the presence of missing covariate data. The primary innovation of our model resides in its effective management of covariates with missing observations. Built upon established frameworks, our joint model extends its capabilities by integrating mixed longitudinal responses and accounting for missingness in covariates, thus confronting this particular challenge. We posit that these enhancements bolster the model\'s utility and dependability in real-world contexts characterized by prevalent missing data. The main objective of this research is to provide a model-based approach to get full information from prostate cancer data collected with patients\' baseline characteristics ( Age , body mass index ( BMI ), GleasonScore , Grade , and Drug ) and two longitudinal endogenous covariates ( Platelets and Bilirubin ).
RESULTS: The results reveal a clear association between prostate-specific antigen and alkaline phosphatase biomarkers in the context of time-to-prostate cancer tumor shrinkage. This underscores the interconnected dynamics of these key indicators in gauging disease progression.
CONCLUSIONS: The analysis of the prostate cancer dataset, incorporating a joint evaluation of mixed longitudinal prostate-specific antigen and alkaline phosphatase biomarkers alongside tumor status, has provided valuable insights into disease progression. The results demonstrate the effectiveness of the proposed joint model, as evidenced by accurate estimates. The shared variables associated with both longitudinal biomarkers and event times consistently deviate from zero, highlighting the robustness and reliability of the model in capturing the complex dynamics of prostate cancer progression. This approach holds promise for enhancing our understanding and predictive capabilities in the clinical assessment of prostate cancer.
摘要：
背景：这项研究探讨了前列腺特异性抗原之间的复杂相互作用，碱性磷酸酶,和前列腺癌中肿瘤缩小的时间动态。通过研究前列腺癌肿瘤的纵向轨迹和时间收缩，我们的目标是解开这些生物标志物的复杂模式。这种理解对于获得对前列腺癌进展的多方面的深刻见解至关重要。联合模型方法是一个全面的框架，有助于阐明前列腺癌背景下这些关键要素之间的复杂相互作用。
方法：针对混合双变量纵向生物标志物和事件时间数据，提出了一种共享参数策略下的新联合模型，在缺失协变量数据的情况下获得准确的估计。我们模型的主要创新在于有效管理缺少观测值的协变量。建立在既定的框架上，我们的联合模型通过整合混合纵向响应和考虑协变量中的错误来扩展其能力，从而面对这一特殊挑战。我们认为，这些增强增强了模型在以普遍缺失数据为特征的现实世界环境中的实用性和可靠性。本研究的主要目的是提供一种基于模型的方法，从收集的前列腺癌数据中获取患者基线特征（年龄，体重指数（BMI），GleasonScore,Grade,和药物）和两个纵向内源性协变量（血小板和胆红素）。
结果：结果显示前列腺特异性抗原和碱性磷酸酶生物标志物在前列腺癌肿瘤缩小时间的背景下存在明显的关联。这强调了这些关键指标在衡量疾病进展方面的相互联系的动态。
结论：前列腺癌数据集的分析，结合混合纵向前列腺特异性抗原和碱性磷酸酶生物标志物与肿瘤状态的联合评估，为疾病进展提供了有价值的见解。结果表明了所提出的联合模型的有效性,准确的估计证明了这一点。与纵向生物标志物和事件时间相关的共享变量始终偏离零，强调了该模型在捕获前列腺癌进展的复杂动力学方面的鲁棒性和可靠性。这种方法有望增强我们对前列腺癌临床评估的理解和预测能力。
方法：针对混合双变量纵向生物标志物和事件时间数据，提出了一种共享参数策略下的新联合模型，在缺失协变量数据的情况下获得准确的估计。我们模型的主要创新在于有效管理缺少观测值的协变量。建立在既定的框架上，我们的联合模型通过整合混合纵向响应和考虑协变量中的错误来扩展其能力，从而面对这一特殊挑战。我们认为，这些增强增强了模型在以普遍缺失数据为特征的现实世界环境中的实用性和可靠性。本研究的主要目的是提供一种基于模型的方法，从收集的前列腺癌数据中获取患者基线特征（年龄，体重指数（BMI），GleasonScore,Grade,和药物）和两个纵向内源性协变量（血小板和胆红素）。
结果：结果显示前列腺特异性抗原和碱性磷酸酶生物标志物在前列腺癌肿瘤缩小时间的背景下存在明显的关联。这强调了这些关键指标在衡量疾病进展方面的相互联系的动态。
结论：前列腺癌数据集的分析，结合混合纵向前列腺特异性抗原和碱性磷酸酶生物标志物与肿瘤状态的联合评估，为疾病进展提供了有价值的见解。结果表明了所提出的联合模型的有效性,准确的估计证明了这一点。与纵向生物标志物和事件时间相关的共享变量始终偏离零，强调了该模型在捕获前列腺癌进展的复杂动力学方面的鲁棒性和可靠性。这种方法有望增强我们对前列腺癌临床评估的理解和预测能力。
