Accounting for misclassification errors in observational studies using administrative databases

卫生服务行政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提供大量患者现成信息的极好来源，但它们容易出现测量误差。测量误差可能是由于病例或暴露的错误分类造成的，如果在研究设计和/或统计分析中没有正确处理，可能会导致错误的结果。我的研究计划包括三个项目，每个项目都涉及测量误差的一个方面。项目1.管理数据库经常被用来估计疾病的流行程度，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将行政数据库中提供的信息与其他信息来源(如加拿大社区健康调查)相结合的方法，以估计一种疾病的流行率，并考虑到这些数据中继承的错误分类错误。这些信息可以以多种方式组合来定义一个、两个或三个病例定义，这些病例定义可以被假设为条件独立，或者可以对相关函数进行建模，并基于这些假设对结果进行比较。项目2.在从管理数据库研究与药物相关的不良事件时，定义为患者可能处于与药物相关的不良事件风险的时间段的风险时间窗口并不是完全已知的。本项目旨在研究改变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。项目3.数据缺失是医学研究中的一个主要问题，如果处理不当可能会导致偏颇的结果。已经提出了基于观测值的多种补偿技术来估计缺失信息。该项目的目的是评估在调查员只知道死亡日期的情况下，是否可以使用多种归罪技术从行政数据库中推断死亡原因。

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.