Validity of an algorithm to identify cardiovascular deaths from administrative health records: a multi-database population-based cohort study
Cardiovascular death is a common outcome in drug safety studies. The purpose of this study is to assess the validity of an algorithm applied to administrative health records for ascertaining cardiovascular deaths in population-based data.
Multiple imputation for systematically missing confounders within a distributed data drug safety network: A simulation study and real-world example
We conducted a simulation study and a real-world analysis using the UK's Clinical Practice Research Datalink to evaluate multiple imputation for confounders that are systematically missing from a subset of data sites in mock distributed data networks.
Extreme restriction design as a method for reducing confounding by indication in pharmacoepidemiologic research
Confounding by indication is a concern in observational pharmacoepidemiologic studies, including those that use active comparator, new user (ACNU) designs. Here, we present a method of restriction to an indication, which we call "extreme restriction," to reduce confounding in such studies.
Identification of incident pancreatic cancer in Ontario administrative health data: A validation study
The purpose of this study was to validate three approaches for identifying incident cases of pancreatic cancer in Ontario administrative claims data.
Data variability across Canadian administrative health databases: Differences in content, coding, and completeness
In this study, we compare the provincial administrative databases and illustrate the potential impact of database differences on a CNODES study about domperidone and the risk of ventricular tachyarrhythmia and sudden cardiac death.
Can we train machine learning methods to outperform the high-dimensional propensity score algorithm?
This study compares covariate selection strategies for confounding adjustment in secondary database analyses via Plasmode simulation: high-dimensional propensity score, machine learning algorithms: lasso, elastic net, random forest, etc.