Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain Medication Usage.
Modeling Individual Patient Count/Rate Data over Time with Applications to Cancer Pain Flares and Cancer Pain Medication Usage.
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DOI：
10.4236/ojs.2021.115038
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发表时间：
2021-10
期刊：
影响因子：
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通讯作者：
Meghani, Salimah H
中科院分区：
文献类型：
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作者：
Knafl, George J;Meghani, Salimah H
本文的目的是调查的方法建模的时间相关性和非恒定的离散度，同时需要合理的时间来搜索这些数据的替代模型，随着时间的推移，个体患者计数/速率数据。本研究提出了两种扩展广义估计方程（GEE）建模方法的公式。这些方法使用基于多元正态密度的似然函数。第一种方法增加了标准的GEE方程，包括用于估计色散参数的方程。第二种方法是基于估计方程的似然函数相对于所有模型参数的偏导数，因此扩展了线性混合建模。三个相关结构被认为是独立的，可交换的，和空间自回归1阶相关。似然函数用于制定似然交叉验证（LCV）分数，用于评估模型。示例分析使用这两种建模方法呈现，其应用于个体癌症患者随时间推移的计数/速率的三个数据集，包括每天的疼痛发作，每天服用的按需止痛药，以及每天每剂量服用的全天候止痛药。使用自适应回归建模方法将均值和离散度建模为时间的可能非线性函数，以通过使用LCV评分进行比较的替代模型进行搜索。这些分析的结果表明，扩展线性混合建模对于随时间推移对个体患者计数/速率数据进行建模是优选的，因为在示例分析中，它生成更好的LCV评分或更简约的模型，并且需要的时间显著更少。
The purpose of this article is to investigate approaches for modeling individual patient count/rate data over time accounting for temporal correlation and non-constant dispersions while requiring reasonable amounts of time to search over alternative models for those data. This research addresses formulations for two approaches for extending generalized estimating equations (GEE) modeling. These approaches use a likelihood-like function based on the multivariate normal density. The first approach augments standard GEE equations to include equations for estimation of dispersion parameters. The second approach is based on estimating equations determined by partial derivatives of the likelihood-like function with respect to all model parameters and so extends linear mixed modeling. Three correlation structures are considered including independent, exchangeable, and spatial autoregressive of order 1 correlations. The likelihood-like function is used to formulate a likelihood-like cross-validation (LCV) score for use in evaluating models. Example analyses are presented using these two modeling approaches applied to three data sets of counts/rates over time for individual cancer patients including pain flares per day, as needed pain medications taken per day, and around the clock pain medications taken per day per dose. Means and dispersions are modeled as possibly nonlinear functions of time using adaptive regression modeling methods to search through alternative models compared using LCV scores. The results of these analyses demonstrate that extended linear mixed modeling is preferable for modeling individual patient count/rate data over time, because in example analyses, it either generates better LCV scores or more parsimonious models and requires substantially less time.