Adaptive Random Effects/Coefficients Modeling

作     者：George J. Knafl George J. Knafl

作者机构：School of Nursing University of North Carolina at Chapel Hill Chapel Hill USA 

出 版 物：《Open Journal of Statistics》 (统计学期刊（英文）)

年 卷 期：2024年第14卷第2期

页      面：179-206页

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

主　　题：Adaptive Regression Correlated Outcomes Extended Linear Mixed Modeling Fractional Polynomials Likelihood Cross-Validation Random Effects/Coefficients 

摘      要：Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.