Software for Fitting Non-Gaussian Random Effects Models

描述(由申请人提供)：我们提出了一种软件实现，用于在广义线性和参数生存模型的背景下，有效地计算多水平混合效应模型中的最大似然估计。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。我们最近建议实施的方法学进步使我们有可能在更广泛的问题中计算一致和渐近无偏的最大估计，我们还建议计算统计数据来验证这些估计。这项建议的“初步结果”部分表明，很容易遇到流行病学数据的情况，在这种情况下，通常的混合效应模型算法失败，即使在样本大的问题中，如果“在簇内”的样本大小。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。我们建议提供这样的警告。大多数混合效应模型软件假定多变量正态随机效应密度。我们建议在随机效果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类似模型拟合能力的软件。

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.