Statistical Analysis of Longitudinal Medical Cost Data

描述（由申请人提供）：本申请的目的是开发新的统计模型和方法来分析纵向医疗成本数据，例如，每月或随时间收集的其他医疗费用数据。相对于总成本数据分析丢弃大量信息，纵向成本数据的可用性可以使卫生经济学分析更有效，解释更全面，更有见地，更有用。我们首先提出一个纵向的四部分模型，分别用四个联合方程建模：（1）就医概率，（2）住院概率，（3）门诊和（4）住院费用的实际金额。我们的模型同时考虑了每个病人的时间间相关性和四个方程的交叉方程相关性，通过联合广义线性混合模型（GLMM）（1）和（2）中的二元结果，和线性混合模型（3）和（4）中的对数转换成本;为了避免对数转换成本中的重新转换问题，我们接下来提出了一个两部分随机效应模型，第二部分中的Gamma GLMM用于正成本的数量。此外，我们还考虑了一个更灵活的广义Gamma分布的正成本量。 在目标3中，我们引入了纵向医疗费用的两部分随机效应模型，在第二部分中引入了比例风险模型。比例风险模型的一个吸引人的特点是，基线风险是未指定的（非参数），适应的事实，即积极的医疗费用数据往往是高度偏斜和异方差，这不能用任何简单的参数分布。 我们的模型可以用来确定医疗费用的风险因素，并确定最具成本效益的治疗。为了说明这一点，我们将我们提出的新模型应用于来自弗吉尼亚大学临床数据库（CDR）的1，397名慢性心力衰竭患者的每月医疗费用。我们还比较了该模型的结果与现有模型在生成财务预测的一致性和准确性方面的结果。 最后，我们将开发现成的软件，以促进我们的方法在实际数据分析中的应用。

DESCRIPTION (Provided by the Applicant): The objective of this application is to develop new statistical models and methods to analyze longitudinal medical cost data, e.g., monthly or other medical cost data collected over time. Compared to the analysis of total cost data which discards a great amount of information, the availability of longitudinal cost data can make health economic analyses more efficient and interpretation more comprehensive, insightful, and useful. We first propose a longitudinal four-part model, with four joint equations modeling respectively: (1) the probability of seeking medical treatment, (2) the probability of being hospitalized, and the actual amount of (3) outpatient and (4) inpatient costs. Our model simultaneously takes account of the inter-temporal correlation of each patient and the cross-equation correlation of the four equations, by joint generalized linear mixed models (GLMM) for binary outcome in (1) and (2), and linear mixed models for log transformed cost in (3) and (4); To circumvent the re-transformation issue in log transformed cost, we next propose a two part random effects model with a Gamma GLMM in part II for the amount of positive cost. Furthermore, we also consider a more flexible generalized Gamma distribution for the amount of positive cost. In Aim 3 we introduce a two-part random effects model for longitudinal medical costs, with proportional hazards model in the second part. An appealing feature of the proportional hazards model is that the baseline hazard is unspecified (nonparametric), accommodating the fact that positive medical cost data are often highly skewed and heteroscedastic, which can not be described by any simple parametric distribution. Our model can be used to ascertain the risk factors of medical costs and identify the most cost-effective treatment. To illustrate this we apply our proposed new model to monthly medical costs of 1,397 chronic heart failure patients from the clinical data repository (CDR) at the University of Virginia. We also compare the results from this model with those from the existing models in terms of consistency and accuracy for generating financial forecasts. Finally, we will develop ready-to-use software to facilitate the application of our methods in practical data analysis.