Statistical Analysis of Longitudinal Medical Cost Data

描述(由申请人提供)：本申请的目标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，随着时间的推移收集的每月或其他医疗成本数据。与丢弃大量信息的总成本数据分析相比，纵向成本数据的可用性可以使卫生经济分析更有效，解释更全面、更有洞察力和更有用。我们首先提出了一个纵向四部分模型，分别用四个联立方程组进行建模：(1)就医概率，(2)住院概率，(3)门诊和(4)住院费用的实际数额。我们的模型同时考虑了每个患者的时间相关性和四个方程的交叉方程相关性，通过(1)和(2)中的二元结果的联合广义线性混合模型(GLMM)和(3)和(4)中的对数转换成本的线性混合模型；为了规避对数转换成本中的再转换问题，我们在第二部分提出了一个具有Gamma GLMM的两部分随机效应模型。此外，我们还考虑了一种更灵活的广义伽玛分布。 在目标3中，我们介绍了纵向医疗费用的两部分随机效应模型，其中比例风险模型在第二部分。比例风险模型的一个吸引人的特征是基线风险是未指定的(非参数)，适应了这样一个事实，即正向医疗费用数据通常是高度倾斜和异方差的，这不能用任何简单的参数分布来描述。 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方法。为了说明这一点，我们将我们提出的新模型应用于弗吉尼亚大学临床数据库(CDR)中1397名慢性心力衰竭患者的每月医疗费用。在生成财务预测的一致性和准确性方面，我们还将该模型的结果与现有模型的结果进行了比较。 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.