Statistical Analysis of Longitudinal Medical Cost Data

纵向医疗费用数据统计分析

• 批准号: 7323323
• 负责人: Lei Liu
• 金额: $ 4.44万
• 依托单位: UNIVERSITY OF VIRGINIA
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7323323
• 关键词: Statistical; Analysis; Longitudinal; Medical; Cost

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.

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费用数据，例如随时间推移收集的每月或其他医疗费用数据。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加高效，解释更加全面、富有洞察力和实用性。我们首先提出一个纵向四部分模型，分别用四个联合方程建模：（1）就医概率，（2）住院概率，以及（3）门诊和（4）住院的实际金额成本。我们的模型通过（1）和（2）中二元结果的联合广义线性混合模型（GLMM）以及线性混合模型同时考虑每个患者的时间间相关性和四个方程的交叉方程相关性对于（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，在第二部分中使用 Gamma GLMM 来计算正成本量。此外，我们还考虑对正成本量采用更灵活的广义伽玛分布。 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，第二部分采用比例风险模型。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（非参数的），适应了正医疗成本数据通常高度偏斜和异方差的事实，不能用任何简单的参数分布来描述。 我们的模型可用于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。我们还将该模型的结果与现有模型的结果在生成财务预测的一致性和准确性方面进行比较。 最后，我们将开发即用型软件，以促进我们的方法在实际数据分析中的应用。