Modeling Semicontinuous Longitudinal Expenditures: A Practical Guide
Modeling Semicontinuous Longitudinal Expenditures: A Practical Guide
复制标题
DOI：
10.1111/1475-6773.12815
复制
发表时间：
2018-08-01
影响因子：
3.4
通讯作者：
Olsen, Maren K.
中科院分区：
文献类型：
--
作者：
Smith, Valerie A.;Maciejewski, Matthew L.;Olsen, Maren K.
目的比较不同的策略分析纵向支出数据的点质量为0美元。我们提供了参数解释，研究问题和模型选择的指导。数据来源，研究设计和数据收集一部分模型，不相关的两部分模型，相关的条件两部分（CTP）模型和相关的边缘化两部分（MTP）模型已被提出的纵向支出，往往表现出很大比例的零和分布的连续，高度右偏的正值。实施和解释这些模型的指导说明了纵向（2000-2003年）专业护理支出的退伍军人与高血压，从退伍军人管理局data.Principal FindingsThe四个策略回答不同的研究问题，是适合不同的数据结构，并提供不同的结果。如果有一个点质量为0美元，那么MTP模型可能是最有用的，如果主要兴趣是在整个人口的平均支出。一个CTP模型可能是最有用的，如果主要利益是在有条件的支出水平，他们正在incurrement.ConclusionsResearchers应考虑的建模策略纵向支出的结果是一致的研究目标和适当的数据在手。
ObjectiveTo compare different strategies for analyzing longitudinal expenditure data that have a point mass at $0. We provide guidance on parameter interpretation, research questions, and model selection.Data Sources, Study Design, and Data CollectionOne-part models, uncorrelated two-part models, correlated conditional two-part (CTP) models, and correlated marginalized two-part (MTP) models have been proposed for longitudinal expenditures that often exhibit a large proportion of zeros and a distribution of continuous, highly right-skewed positive values. Guidance on implementing and interpreting each of these model is illustrated with an example of longitudinal (2000-2003) specialty careexpenditures of veterans with hypertension, drawn from Veterans Administration data.Principal FindingsThe four strategies answer different research questions, are appropriate for different structures of data, and provide different results. If there is a point mass at $0, then the MTP model may be most useful if the primary interest is in mean expenditures of the entire population. A CTP model may be most useful if the primary interest is in the level of expenditures conditional on them being incurred.ConclusionsResearchers should consider which modeling strategy for longitudinal expenditure outcomes is both consistent with research aims and appropriate for the data at hand.