Statistical Innovations in Health Services Research

卫生服务研究的统计创新

• 批准号: 7694979
• 负责人: Joseph C Gardiner
• 金额: $ 28.01万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-05-01 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7694979
• 关键词: Statistical; Innovations; Health; Services; Research

DESCRIPTION (Provided by Applicant): Studies of healthcare utilization from national and state administrative databases have been stymied by a lack of powerful methodological approaches to circumvent deficiency of specificity often common in these databases. New statistical and econometric techniques are needed to uncover hidden structures in healthcare utilization data. This research plan addresses the development and application of innovative regression models for analyzing jointly healthcare utilization and outcomes from clinical and observational studies. All models incorporate observed heterogeneity due to patient and system characteristics. In addition, we explicitly acknowledge unobserved heterogeneity due to omitted variables, endogeneity of explanatory variables, and censoring in outcome measures. Specifically, we develop and test several statistical models to analyze measures of utilization (eg, length of stay, cost) and health outcomes (eg, survival, quality of life). The complexity of the models depends upon the coarseness of the available data (eg, longitudinal, cross-sectional, hierarchical) and richness in observed covariates. Markov models are used with longitudinal data to account for the dynamics of movement of patients between health states (eg, relapse, remission) with covariate effects incorporated in transition intensities through multiplicative intensity and proportional hazards models. Heterogeneity due to unobserved or omitted variables is accommodated through random effects, frailties, and latent class models. We use Coxian phase-type models to elicit hidden Markov structures in cross-sectional data on healthcare utilization. Hierarchical models are applied to accommodate complex sampling designs and clustering (eg, patients within hospitals, hospitals within geographic units). All models will be rigorously tested in simulation and cross-validation studies. Application of our methods will be demonstrated in three studies with healthcare utilization and outcomes. (1) Using the Nationwide Inpatient Sample we jointly estimate total hospital charge and length of stay associated with procedures for two broad disease categories, heart disease and cancer. (2) Using a linked data set of Michigan Medicare, Medicaid and Cancer & Death Certificate Registries, we estimate the cost of treatment and survival in patients with colon, breast, lung and prostate cancer, controlling for observed covariates and unobserved heterogeneity. (3) Using a proprietary longitudinal data set of patient functioning while undergoing cancer treatments, we estimate costs of care, survival and physical function jointly, and assess the impact of changes in physical function on cost and survival. By expanding the repertoire of analytic tools for health services researchers, this project will provide methods for extracting valuable information on healthcare utilization and outcomes from administrative databases that can be used to inform cost-effectiveness analyses and health policy. By expanding the repertoire of analytic tools for health services researchers, this project will provide methods for extracting valuable information on healthcare utilization and outcomes from administrative databases that can be used to inform cost-effectiveness analyses and health policy

描述（由申请人提供）：对国家和州行政数据库的医疗保健利用的研究因缺乏强大的方法论方法来规避这些数据库中常见的特异性缺陷而受到阻碍。需要新的统计和计量经济学技术来揭示医疗保健利用数据中隐藏的结构。该研究计划致力于创新回归模型的开发和应用，以联合分析医疗保健利用以及临床和观察研究的结果。所有模型都包含由于患者和系统特征而观察到的异质性。此外，我们明确承认由于遗漏变量、解释变量的内生性以及结果测量中的审查而导致的未观察到的异质性。具体来说，我们开发并测试了几种统计模型来分析利用率指标（例如，住院时间、费用）和健康结果（例如，生存率、生活质量）。模型的复杂性取决于可用数据的粗糙度（例如，纵向、横截面、分层）和观察到的协变量的丰富度。马尔可夫模型与纵向数据一起使用，以解释患者在健康状态（例如复发、缓解）之间移动的动态，并通过乘法强度和比例风险模型将协变量效应纳入转变强度中。由于未观察到或遗漏的变量而导致的异质性可以通过随机效应、脆弱性和潜在类别模型来调节。我们使用 Coxian 阶段型模型来得出医疗保健利用率横截面数据中的隐藏马尔可夫结构。分层模型用于适应复杂的抽样设计和聚类（例如，医院内的患者、地理单位内的医院）。所有模型都将在模拟和交叉验证研究中经过严格测试。我们的方法的应用将在三项有关医疗保健利用和结果的研究中得到证明。 (1) 使用全国住院患者样本，我们共同估算了与心脏病和癌症两大疾病类别的手术相关的总住院费用和住院时间。 (2) 使用密歇根医疗保险、医疗补助以及癌症和死亡证明登记处的关联数据集，我们估计了结肠癌、乳腺癌、肺癌和前列腺癌患者的治疗和生存成本，并控制了观察到的协变量和未观察到的异质性。 (3) 使用接受癌症治疗时患者功能的专有纵向数据集，我们联合估计护理成本、生存率和身体机能，并评估身体机能变化对成本和生存的影响。通过扩展卫生服务研究人员的分析工具库，该项目将提供从管理数据库中提取有关医疗保健利用和结果的有价值信息的方法，这些信息可用于为成本效益分析和卫生政策提供信息。通过扩展卫生服务研究人员的分析工具库，该项目将提供从管理数据库中提取有关医疗保健利用和结果的有价值信息的方法，这些信息可用于为成本效益分析和卫生政策提供信息

项目成果

Statin therapy in the reduction of cardiovascular events in patients undergoing intermediate-risk noncardiac, nonvascular surgery.

他汀类药物治疗可减少接受中危非心脏、非血管手术的患者的心血管事件。

• DOI: 10.1002/clc.22135
• 发表时间: 2013
• 期刊: Clinical cardiology
• 影响因子: 2.7
• 作者: Raju,ManjunathG;Pachika,Ajay;Punnam,SujeethR;Gardiner,JosephC;Shishehbor,MehdiH;Kapadia,SamirR;Abela,GeorgeS
• 通讯作者: Abela,GeorgeS

Reduction in the intensity rate of appropriate shocks for ventricular arrhythmias with statin therapy.

他汀类药物治疗可降低室性心律失常的适当电击强度。

• DOI: 10.1097/fjc.0b013e3181e74d4f
• 发表时间: 2010
• 期刊: Journal of cardiovascular pharmacology
• 影响因子: 3
• 作者: Beri,Abhimanyu;Contractor,Tahmeed;Gardiner,JosephC;Ardhanari,Sivakumar;Thakur,Ranjan
• 通讯作者: Thakur,Ranjan

Fixed effects, random effects and GEE: What are the differences?

• DOI: 10.1002/sim.3478
• 发表时间: 2009-01-30
• 期刊: STATISTICS IN MEDICINE
• 影响因子: 2
• 作者: Gardiner, Joseph C.;Luo, Zhehui;Roman, Lee Anne
• 通讯作者: Roman, Lee Anne