Regression Modeling of Individual-Patient Correlated Discrete Outcomes with Applications to Cancer Pain Ratings.
Regression Modeling of Individual-Patient Correlated Discrete Outcomes with Applications to Cancer Pain Ratings.
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DOI：
10.4236/ojs.2022.124029
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发表时间：
2022-08
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
制定并演示采用离散数值的个体患者纵向结局的概率和离散度的回归建模方法。被认为是三种替代建模的结果概率。多项式概率基于不同结果值的概率的不同截距和斜率。有序概率基于不同结果值的概率的不同截距和相同斜率。删失泊松概率基于不同结果值的概率的相同截距和斜率。参数估计与扩展线性混合模型最大化似然函数的基础上的多元正态密度，占患者内的相关性。提供了用于估计模型参数的梯度向量和Hessian矩阵的公式。似然函数还用于计算替代模型的交叉验证分数，并控制自适应建模过程，以识别概率和分散预测中可能的非线性函数关系。提供了癌症患者在97天的时间段内的每日疼痛评级的示例分析。删失泊松方法更适合于对这些数据以及其他此类数据集进行建模，因为它比其他两种方法在更短的时间内生成具有更少参数的竞争模型。该模型生成的概率在时间上明显是非线性的，而分散度随着时间的推移明显是非恒定的，这表明需要对此类数据进行自适应建模。分析还讨论了这些每日疼痛评级对时间和每日疼痛发作次数的依赖性。概率和离散度随着时间的推移发生不同的变化，对于不同数量的疼痛发作。对个体癌症患者的日常疼痛评级进行自适应建模是识别时间和其他预测因子（如疼痛发作次数）中的非线性关系的有效方法。
To formulate and demonstrate methods for regression modeling of probabilities and dispersions for individual-patient longitudinal outcomes taking on discrete numeric values. Three alternatives for modeling of outcome probabilities are considered. Multinomial probabilities are based on different intercepts and slopes for probabilities of different outcome values. Ordinal probabilities are based on different intercepts and the same slope for probabilities of different outcome values. Censored Poisson probabilities are based on the same intercept and slope for probabilities of different outcome values. Parameters are estimated with extended linear mixed modeling maximizing a likelihood-like function based on the multivariate normal density that accounts for within-patient correlation. Formulas are provided for gradient vectors and Hessian matrices for estimating model parameters. The likelihood-like function is also used to compute cross-validation scores for alternative models and to control an adaptive modeling process for identifying possibly nonlinear functional relationships in predictors for probabilities and dispersions. Example analyses are provided of daily pain ratings for a cancer patient over a period of 97 days. The censored Poisson approach is preferable for modeling these data, and presumably other data sets of this kind, because it generates a competitive model with fewer parameters in less time than the other two approaches. The generated probabilities for this model are distinctly nonlinear in time while the dispersions are distinctly non-constant over time, demonstrating the need for adaptive modeling of such data. The analyses also address the dependence of these daily pain ratings on time and the daily numbers of pain flares. Probabilities and dispersions change differently over time for different numbers of pain flares. Adaptive modeling of daily pain ratings for individual cancer patients is an effective way to identify nonlinear relationships in time as well as in other predictors such as the number of pain flares.