扩展开源saemix包中的代码,以适应纵向和时间到事件数据的联合模型
Alexandra Lavalley-Morelle1, France Mentré2, Emmanuelle Comets3
1Université Paris Cité, INSERM, IAME, F-75018 Paris, France.
Computer methods and programs in biomedicine
|February 29, 2024
概括
这项研究扩展了saemix R包,以适应复杂的参数关节模型,改善纵向和时间到事件数据的估计. 增强的工具为高级统计分析提供了更大的灵活性和用户控制.
科学领域:
- 生物统计学 生物统计学
- 统计建模 统计建模
- 制药指标 (Pharmacometrics) 是一个指标.
背景情况:
- 纵向和时间到事件数据的联合建模越来越重要.
- 现有的软件通常将纵向模型限制在线性形式上,生存模型限制在基本分布上.
- 这限制了复杂生物标志物动态和生存结果的分析,包括竞争风险.
研究的目的:
- 为了扩展saemix R包,用于安装参数关节模型.
- 为了在联合模型中实现纵向结果的非线性混合效应模型.
- 为用户提供对模型功能规格的完全控制.
主要方法:
- 扩展了saemix包,它使用非线性混合效应模型 (NLMEM) 的随机近似期望最大化 (SAEM) 算法.
- 实现了一种新的随机算法,用于计算参数估计的标准误差 (SE).
- 对四个联合模型 (线性/非线性纵向,单一/竞争性风险) 进行了模拟研究,评估参数估计,SE计算和I型错误.
主要成果:
- 在模拟场景中精确估计了参数,偏差和不确定性很低.
- 复杂的联合模型 (与NLMEM) 需要更多的算法链来减少偏差;与早期审查竞争的风险带来了估计挑战.
- 实证SE与计算SE非常相匹配;然而,在复杂模型中随机效应差异的SE显示出更高的不确定性和中度低估. 一种类型I错误被始终控制.
结论:
- 适应的saemix包为复杂的参数联合模型提供了一个灵活的开源工具.
- 这种扩展适用于非线性纵向子模型,克服了标准统计软件的局限性.
- 自由可用的代码和示例有助于用户采用先进的统计分析.
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