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Multinomial Logistic Functions in Markov Chain Models of Sleep Architecture: Internal and External Validation and Covariate Analysis

机译：睡眠体系马尔可夫链模型中的多项逻辑函数：内部和外部验证以及协变量分析

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Mixed-effect Markov chain models have been recently proposed to characterize the time course of transition probabilities between sleep stages in insomniac patients. The most recent one, based on multinomial logistic functions, was used as a base to develop a final model combining the strengths of the existing ones. This final model was validated on placebo data applying also new diagnostic methods and then used for the inclusion of potential age, gender, and BMI effects. Internal validation was performed through simplified posterior predictive check (sPPC), visual predictive check (VPC) for categorical data, and new visual methods based on stochastic simulation and estimation and called visual estimation check (VEC). External validation mainly relied on the evaluation of the objective function value and sPPC. Covariate effects were identified through stepwise covariate modeling within NONMEM VI. New model features were introduced in the model, providing significant sPPC improvements. Outcomes from VPC, VEC, and external validation were generally very good. Age, gender, and BMI were found to be statistically significant covariates, but their inclusion did not improve substantially the model’s predictive performance. In summary, an improved model for sleep internal architecture has been developed and suitably validated in insomniac patients treated with placebo. Thereafter, covariate effects have been included into the final model.Electronic supplementary materialThe online version of this article (doi:10.1208/s12248-011-9287-4) contains supplementary material, which is available to authorized users.
机译：最近已经提出了混合效应马尔可夫链模型来描述失眠患者睡眠阶段之间过渡概率的时间过程。以多项逻辑函数为基础的最新模型被用作开发结合了现有模型优势的最终模型的基础。该最终模型还通过使用新的诊断方法在安慰剂数据上进行了验证，然后用于包含潜在的年龄，性别和BMI效应。内部验证通过简化的后验预测检查（sPPC），视觉预测检查（VPC）进行分类数据，以及基于随机模拟和估计的新视觉方法（称为视觉估计检查（VEC））进行。外部验证主要依靠目标函数值和sPPC的评估。通过NONMEM VI中的逐步协变量建模来确定协变量效应。在模型中引入了新的模型功能，从而对sPPC进行了重大改进。 VPC，VEC和外部验证的结果通常都很好。发现年龄，性别和BMI是统计学上显着的协变量，但将其包括在内并不能显着改善模型的预测性能。总之，已经开发了一种改善的睡眠内部结构模型，并已在安慰剂治疗的失眠患者中得到了适当验证。此后，协变量效应已包含在最终模型中。电子补充材料本文的在线版本（doi：10.1208 / s12248-011-9287-4）包含补充材料，授权用户可以使用。

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