Software for Fitting Non-Gaussian Random Effects Models

用于拟合非高斯随机效应模型的软件

• 批准号: 6736080
• 负责人: Edward C Chao
• 金额: $ 9.97万
• 依托单位: INSIGHTFUL CORPORATION
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-04-01 至 2004-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6736080
• 关键词: clinical research; computer program /software; computer system design /evaluation; data collection methodology /evaluation; data quality /integrity; epidemiology; human data; mathematical model; statistics /biometry

DESCRIPTION (provided by applicant): We propose a software implementation of recent methodological advances for efficiently computing Maximum likelihood estimates in multilevel mixed effects models in the context of generalized linear and parametric survival models. Such models are often used in the analysis of longitudinal and cluster sample data arising in Epidemiological and other studies. The recent methodological advances we propose to implement make it possible to compute consistent and asymptotically unbiased maximum estimates in a much wider variety of problems, and we also propose to compute statistics validating these estimates. The "Preliminary Results" section of this proposal shows that it easy to encounter situations with Epidemiological data in which the usual mixed effect model algorithms fail, even in problems with large sample sizes if the 'within cluster' sample sizes are small. These failures are made more troublesome by the fact that the user seldom has any warning that the computational algorithm has failed. We propose to provide such a warning. Most mixed effect model software assumes a multivariate normal random effect density. We propose to allow other densities, including user specified densities, in the random effects model. We also propose to develop software with adaptive MARS like model fitting capabilities.

描述（由申请人提供）：我们提出了一个软件实现的最新方法的进步，有效地计算最大似然估计在广义线性和参数生存模型的背景下，在多级混合效应模型。这种模型经常用于流行病学和其他研究中产生的纵向和集群样本数据的分析。最近的方法进步，我们建议实施，使人们有可能计算一致的和渐近无偏的最大估计在更广泛的各种问题，我们还建议计算验证这些估计的统计。本提案的“初步结果”部分表明，很容易遇到流行病学数据的情况，其中通常的混合效应模型算法失败，即使在大样本量的问题中，如果“集群内”样本量很小。由于用户很少得到计算算法已经失败的任何警告，这些失败变得更加麻烦。我们建议发出这样的警告。大多数混合效应模型软件假定多变量正态随机效应密度。我们建议在随机效应模型中允许其他密度，包括用户指定的密度。我们还建议开发具有自适应MARS模型拟合功能的软件。