Accounting for misclassification errors in observational studies using administrative databases

使用管理数据库解释观察性研究中的错误分类错误

• 批准号: 238685-2007
• 负责人: Rahme, Elham
• 金额: $ 0.87万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=362297
• 关键词: Accounting; misclassification; errors; observational; studies

Health services administrative databases have been widely used to assess risks associated with diseases and their treatments. Although these administrative databases are excellent sources of ready-to-use information on large number of patients at minimal costs, they are prone to measurement errors. Measurement errors can be due to case or exposure misclassification and can lead to erroneous results if not addressed properly in the study design and/or statistical analyses. My research program includes three projects, each addressing one aspect of measurement error.Project 1. Administrative databases are often used to estimate the prevalence of a disease without accounting for misclassification errors inherit in these databases. I will use a Bayesian approach to develop and assess methods of combining information available from administrative databases as well as other sources of information such as the Canadian Community Health Survey, to estimate the prevalence of a disease accounting for misclassification errors inherit in these data. This information can be combined in several ways to define either one, two or three case-definitions that may be assumed to be either conditionally independent or a correlation function may be modeled and results based on these assumptions compared.Project 2. In studying medication-related adverse events from administrative databases, the risk time-window defined as the period of time during which a patient could be at risk of medication-related adverse events is not exactly known. This project aims to study the effect of varying the risk time window values on the assessment of medication-related adverse events using simulated data.Project 3. Missing data are a major problem in medical research and can lead to biased results if not addressed properly. Multiple imputation techniques have been proposed to estimate the missing information based on observed values. This project aims to assess whether multiple imputation techniques can be used to infer the cause of death from administrative databases when only the date of death is known to the investigator.

卫生服务管理数据库已被广泛用于评估与疾病及其治疗相关的风险。虽然这些管理数据库是以最低成本获得大量患者即时可用信息的绝佳来源，但它们容易出现测量错误。测量错误可能是由于病例或暴露分类错误所致，如果在研究设计和/或统计分析中未正确解决，则可能导致错误结果。我的研究计划包括三个项目，每个项目解决测量误差的一个方面。管理数据库通常用于估计疾病的流行，而不考虑这些数据库中继承的错误分类错误。我将使用贝叶斯方法来开发和评估方法相结合的信息，从行政数据库以及其他来源的信息，如加拿大社区健康调查，估计占这些数据中继承的错误分类错误的疾病的患病率。这些信息可以通过几种方式结合起来，定义一个、两个或三个案例定义，这些案例定义可以被假设为条件独立的，或者可以对相关函数进行建模，并基于这些假设比较结果。在研究管理数据库中的药物相关不良事件时，风险时间窗（定义为患者可能处于药物相关不良事件风险的时间段）并不确切。本项目旨在研究不同的风险时间窗值对使用模拟数据评估药物相关不良事件的影响。缺失数据是医学研究中的一个主要问题，如果处理不当，可能导致有偏见的结果。多重插补技术已被提出来估计基于观测值的缺失信息。本项目旨在评估当研究者仅知道死亡日期时，多重插补技术是否可用于从行政数据库中推断死亡原因。