Background and Objectives: Longitudinal data are increasingly collected in clinical trials to provide information on treatment action and disease evolution. The trajectory of continuous biomarkers such as target hormone concentrations or viral loads can then be modelled in relationship to the occurrence of events such as recovery or hospitalisation. Other studies may include repeated measurements of discrete pain scores, number of episodes (count) or occurrence of events (survival). Non-linear mixed-effect models (NLMEM) can handle individual differences in trajectories while modelling the underlying population evolution and are the natural choice for their analysis. The saemix package for R is one of the few open-source solutions and the most flexible. In this paper, we extend it to accommodate a variety of models for non-Gaussian data. Methods: The saemix package estimates parameters through the Stochastic Approximation Expectation-Maximisation (SAEM) algorithm. Within the package, non-Gaussian models are specified by their log-likelihood functions, affording maximal control over model formulation. We extend estimation algorithms as well as exploratory and diagnostic plots for non-Gaussian data. Bootstrap approaches were implemented to estimate parameter uncertainty. To evaluate the performance of saemix, we performed a simulation study based on the toenail dataset, containing repeated binary data from a randomised clinical trial. Results: saemix showed good performance to recover the true parameter values in the simulation study, and was stable across different starting values for the parameters. An algorithm jointly searching for covariate and interindividual variability model was also implemented to build the covariate model and applied to categorical and survival-type data.

翻译：背景与目标：临床试验中越来越多地收集纵向数据以提供治疗作用和疾病演变的信息。连续生物标志物（如目标激素浓度或病毒载量）的轨迹可建模为与恢复或住院等事件发生的关系。其他研究可能包含离散疼痛评分、发作次数（计数）或事件发生（生存）的重复测量。非线性混合效应模型（NLMEM）能够在建模潜在群体演变的同时处理个体轨迹差异，是此类分析的自然选择。R语言的saemix包是少数开源解决方案之一且最具灵活性。本文将其扩展以适配多种非高斯数据模型。方法：saemix包通过随机逼近期望最大化（SAEM）算法估计参数。在该包中，非高斯模型通过其对数似然函数指定，从而实现对模型公式的最大控制。我们扩展了针对非高斯数据的估计算法及探索性与诊断图。采用自助法估计参数不确定性。为评估saemix性能，我们基于趾甲数据集（包含随机临床试验的重复二元数据）进行了模拟研究。结果：模拟研究中saemix在恢复真实参数值方面表现良好，且在不同参数初始值下保持稳定。同时实现了联合搜索协变量和个体间变异模型的算法以构建协变量模型，并将其应用于分类和生存型数据。