Université de Paris, IAME, INSERM , F-75018, Paris, France.
Digital & Data Science platform, Sanofi, Chilly-Mazarin, France.
AAPS J. 2020 Feb 19;22(2):50. doi: 10.1208/s12248-019-0388-9.
Given a joint model and its parameters, Bayesian individual dynamic prediction (IDP) of biomarkers and risk of event can be performed for new patients at different landmark times using observed biomarker values. The aim of the present study was to compare IDP, with uncertainty, using Stan 2.18, Monolix 2018R2 and NONMEM 7.4. Simulations of biomarker and survival were performed using a nonlinear joint model of prostate-specific antigen (PSA) kinetics and survival in metastatic prostate cancer. Several scenarios were evaluated, according to the strength of the association between PSA and survival. For various landmark times, a posteriori distribution of PSA kinetic individual parameters was estimated, given individual observations, with each software. Samples of individual parameters were drawn from the posterior distribution. Bias and imprecision of individual parameters as well as coverage of 95% credibility interval for PSA and risk of death were evaluated. All software performed equally well with small biases on individual parameters. Imprecision on individual parameters was comparable across software and showed marked improvements with increasing landmark time. In terms of coverage, results were also comparable and all software were able to well predict PSA kinetics and survival. As for computing time, Stan was faster than Monolix and NONMEM to obtain individual parameters. Stan 2.18, Monolix 2018R2 and NONMEM 7.4 are able to characterize IDP of biomarkers and risk of event in a nonlinear joint modelling framework with correct uncertainty and hence could be used in the context of individualized medicine.
给定联合模型及其参数，可以使用观察到的生物标志物值在不同的里程碑时间对新患者进行生物标志物和事件风险的贝叶斯个体动态预测（IDP）。本研究的目的是使用 Stan 2.18、Monolix 2018R2 和 NONMEM 7.4 比较具有不确定性的 IDP。使用前列腺特异性抗原（PSA）动力学和转移性前列腺癌生存的非线性联合模型进行了生物标志物和生存的模拟。根据 PSA 与生存之间的关联强度评估了几种情况。对于各种里程碑时间，使用每种软件根据个体观察值估计个体参数的 PSA 动力学后验分布。从后验分布中抽取个体参数的样本。评估了 PSA 动力学个体参数的偏差和不精确性以及 PSA 和死亡风险的 95%可信区间的覆盖率。所有软件的个体参数都具有较小的偏差，性能相当。个体参数的不精确性在软件之间具有可比性，并且随着里程碑时间的增加而显著提高。在覆盖率方面，结果也相当，所有软件都能够很好地预测 PSA 动力学和生存。在计算时间方面，Stan 比 Monolix 和 NONMEM 更快地获得个体参数。Stan 2.18、Monolix 2018R2 和 NONMEM 7.4 能够在非线性联合建模框架中正确地描述 IDP 的不确定性，因此可用于个体化医学领域。
