Design and validation of a data simulation model for longitudinal healthcare data.

Evaluating performance characteristics of analytic methods developed to identify treatment effects in longitudinal healthcare data has been hindered by lack of an objective benchmark to measure performance. Relationships between drugs and subsequent treatment effects are not precisely quantified in real-world data, and simulated data offer potential to augment method development by providing data with known, measurable characteristics. However, the use of simulated data has been limited due to its inability to adequately reflect the complexities inherent in real-world databases that are necessary for effective method development.

Development and evaluation of a common data model enabling active drug safety surveillance using disparate healthcare databases.

Objective: Active drug safety surveillance may be enhanced by analysis of multiple observational healthcare databases, including administrative claims and electronic health records. The objective of this study was to develop and evaluate a common data model (CDM) enabling rapid, comparable, systematic analyses across disparate observational data sources to identify and evaluate the effects of medicines.

Design: The CDM uses a person-centric design, with attributes for demographics, drug exposures, and condition occurrence.

An evaluation of three signal-detection algorithms using a highly inclusive reference event database.

Background: Pharmacovigilance data-mining algorithms (DMAs) are known to generate significant numbers of false-positive signals of disproportionate reporting (SDRs), using various standards to define the terms 'true positive' and 'false positive'.

Objective: To construct a highly inclusive reference event database of reported adverse events for a limited set of drugs, and to utilize that database to evaluate three DMAs for their overall yield of scientifically supported adverse drug effects, with an emphasis on ascertaining false-positive rates as defined by matching to the database, and to assess the overlap among SDRs detected by various DMAs.

Methods: A sample of 35 drugs approved by the US FDA between 2000 and 2004 was selected, including three drugs added to cover therapeutic categories not included in the original sample.

Systematic investigation of time windows for adverse event data mining for recently approved drugs.

The optimum timing of drug safety data mining for a new drug is uncertain. The objective of this study was to compare cumulative data mining versus mining with sliding time windows. Adverse Event Reporting System data (2001-2005) were studied for 27 drugs.

Drug-versus-drug adverse event rate comparisons: a pilot study based on data from the US FDA Adverse Event Reporting System.

Background: A number of published studies compare adverse event rates for drugs on the basis of reports in the US FDA Adverse Event Reporting System (AERS). While the AERS data have the advantage of timely availability and a large capture population, the database is subject to many significant biases, and lacks complete patient information that would allow for correction of those biases. The accuracy of comparative AERS-based data mining has been questioned, but has not been systematically studied.

Influence of the MedDRA hierarchy on pharmacovigilance data mining results.

Purpose: To compare the results of drug safety data mining with three different algorithms, when adverse events are identified using MedDRA Preferred Terms (PT) vs. High Level Terms (HLT) vs. Standardised MedDRA Queries (SMQ).