THE INDIVIDUAL OVER TIME - TIME-SERIES APPLICATIONS IN HEALTH-CARE RESEARCH
THE INDIVIDUAL OVER TIME - TIME-SERIES APPLICATIONS IN HEALTH-CARE RESEARCH
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DOI：
10.1016/0895-4356(90)90005-a
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发表时间：
1990-01-01
影响因子：
7.2
通讯作者：
SCHMIDT, DD
中科院分区：
文献类型：
--
作者：
CRABTREE, BF;RAY, SC;SCHMIDT, DD
本文对单主题数据时间序列ARIMA建模方法进行了综述和简要的理论介绍。时间序列是一种统计技术，当数据以几乎相等的时间间隔重复测量时可能是合适的，在慢性疾病如糖尿病，高血压和单纯疱疹的研究中具有潜在的研究应用。包括干预模型和多变量模型，并举例说明时间序列技术在慢性病研究中的实用性。时间序列建模的受试者与糖尿病之前和之后被放置在氯磺丙脲的方案被用来证明干预分析的潜力。多变量时间序列技术说明了运动和血糖之间的关系建模，并通过建模的心理困扰和细胞免疫系统的淋巴细胞亚群之间的关系。
This paper presents a summary and a brief theoretical introduction to time series ARIMA modeling of single subject data. Time series, a statistical technique that may be appropriate when data are measured repeatedly and at nearly equal intervals of time, has potential research applications in the study of chronic diseases such as diabetes, hypertension, and herpes simplex. Both intervention models and multivariate models are covered, with examples illustrating the utility of time series techniques in chronic disease research. Time series modeling of a subject with diabetes before and after being placed on a regimen of chlorpropamide is used to demonstrate the potential of intervention analysis. Multivariate time series techniques are illustrated by modeling the relationship between exercise and blood glucose, and by modelling the relationship between psychosocial distress and lymphocyte subsets of the cellular immune system.