Time Series Modeling of Trends in Medication Prescribing

药物处方趋势的时间序列建模

基本信息

批准号：
6711663
负责人：
RANDALL SCOTT STAFFORD
金额：
$ 32.97万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2002
资助国家：
美国
起止时间：
2002-04-19 至 2006-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6711663
关键词：
behavioral /social science research tag chemotherapy clinical research health economics human data mathematical model model design /development patient care management physicians

项目摘要

DESCRIPTION: Physician decisions regarding medication prescribing have a large impact on health care costs and clinical outcomes. Surprisingly little is known, however, about the relative impact of a range of factors that may influence physician behavior in the prescribing of medications. By extension, the ability to explain and project trends in medication prescribing is currently limited. This study will develop a novel economic model of the simultaneous influence of multiple determinants on trends in medication prescribing. Econometric time-series regression techniques will be used to estimate the relative influence of temporally varying predictor variables and discrete temporal events on observed national trends in the prescribing of pharmaceuticals for hypertension, depression, asthma, and congestive heart failure. Nationally representative quarterly data on 1978 through 2000 trends in drug prescribing for these conditions will come from the National Disease and Therapeutic Index survey available through IMS Health. Quarterly patient sample sizes in 1998 are about 1,700 for depression, 550 for congestive heart failure, 5,100 for hypertension and 2,000 for asthma. Data on potential predictors of trends will be developed from a variety of sources. Drug-specific trend information on pharmaceutical promotion and wholesale drug prices also will be available from IMS Health. Other predictors will include information on drug dosing, other drug characteristics, clinical trial results publication, media reporting on medications, and clinical guideline publication. By constructing a comprehensive model of the temporal determinants of prescribing behavior, this study will assess the relative influence of these factors. Econometric time series regression models built on the theory of demand for differentiated products will be employed to test specific hypotheses regarding the role of these factors. A key hypothesis will be that promotional influences are more potent than scientific influences. In addition, the research will evaluate hypotheses related to the influence of drug prices, the interaction of science and promotion, and the impact of brand name expiration. Results from the analysis will be relevant to efforts to design and implement clinical and health policies to encourage the effective and cost-effective use of medications.

描述：医师关于药物处方的决定对医疗保健费用和临床结果的影响很大。令人惊讶的是很少但是，已经知道有关一系列因素的相对影响在处方药物方面影响医师行为。通过扩展，在处方药中解释和项目趋势的能力是目前有限。这项研究将开发出一种新颖的经济模型多个决定因素对药物趋势的同时影响处方。计量时间序列回归技术将用于估计时间变化的预测变量的相对影响和在开处方中观察到的国家趋势的离散时间事件高血压，抑郁，哮喘和充血性心脏的药物失败。 1978年至2000年趋势的全国代表性季度数据对于这些疾病的药物处方，将来自国家疾病和通过IMS Health提供的治疗指数调查。季度病人 1998年的样本量大约为抑郁症1,700，充血性心脏量为550 失败，高血压为5,100，哮喘为2,000。潜在的数据趋势的预测因素将从各种来源开发。药物特异性趋势信息有关药物促进和批发药物的信息 IMS Health还将提供价格。其他预测因素将包括有关药物给药，其他药物特征，临床试验结果的信息出版，媒体报道药物和临床指南发布。通过构建时间的全面模型处方行为的决定因素，本研究将评估相对这些因素的影响。计量经济时间序列回归模型构建关于对差异化产品的需求理论将采用测试关于这些因素的作用的特定假设。一个关键假设促销影响比科学更有效影响。此外，研究将评估与药物价格的影响，科学与促进的相互作用以及品牌名称到期的影响。分析的结果将是相关的努力设计和实施临床和健康政策以鼓励有效且具有成本效益的药物使用。