Use of TreeScan by Non-Sentinel Investigators

The TreeScan™ approach has also been utilized by non-Sentinel investigators. Below is a list of presentations or publications of how others have used TreeScan in academia or industry.

 

Using the Self-Controlled Tree-Temporal Scan Statistic to Assess the Safety of Live Attenuated Herpes Zoster Vaccine
Yih WK, Kulldorff M, Dashevsky I, Maro J.
May 7, 2019

The self-controlled tree-temporal scan statistic allows detection of potential vaccine- or drug-associated adverse events without pre-specifying the specific events or post-exposure risk intervals of concern. It thus opens a promising new avenue for safety studies. The method has been successfully used to evaluate the safety of two vaccines for adolescents and young adults, but its suitability to study vaccines for older adults had not been established. The current study applied the method to assess the safety of live attenuated herpes zoster vaccination during 2011-2017 in U.S. adults ≥ 60 years old, using claims data from Truven Health MarketScan® Research Databases. Counts of International Classification of Diseases diagnosis codes recorded in emergency department or hospital settings were scanned for any statistically unusual clustering within a hierarchical tree structure of diagnoses and within 42 days after vaccination. Among 1.24 million vaccinations, four clusters were found: cellulitis on Days 1-3, non-specific erythematous condition on Days 2-4, "other complications…" on Days 1-3, and non-specific allergy on Days 1-6. These results are consistent with local injection-site reactions and other known, generally mild vaccine-associated adverse events and a favorable safety profile. This method may be useful for assessing the safety of other vaccines for older adults. 

View the article here.

An Implementation and Visualization of the Tree-Based Scan Statistic for Safety Event Monitoring in Longitudinal Electronic Health Data
Schachterle SE, Hurley S, Liu Q, Petronis KR, Bate A.
January 8, 2019

Longitudinal electronic healthcare data hold great potential for drug safety surveillance. The tree-based scan statistic (TBSS), as implemented by the TreeScan® software, allows for hypothesis-free signal detection in longitudinal data by grouping safety events according to branching, hierarchical data coding systems, and then identifying signals of disproportionate recording (SDRs) among the singular events or event groups. The objective of this analysis was to identify and visualize SDRs with the TBSS in historical data from patients using two antifungal drugs, itraconazole or terbinafine. By examining patients who used either itraconazole or terbinafine, we provide a conceptual replication of a previous TBSS analyses by varying methodological choices and using a data source that had not been previously used with the TBSS, i.e., the Optum Clinformatics™ claims database. With this analysis, we aimed to test a parsimonious design that could be the basis of a broadly applicable method for multiple drug and safety event pairs. 

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Tree-Based Scan Statistic - Application in Manufacturing-Related Safety Signal Detection
Mahaux O, Bauchau V, Zeinoun Z, Van Holle L
January 3, 2019

Over the last decades, medicinal regulations have been put into place and have considerably improved manufacturing practices. Nevertheless, safety issues may still arise. Using the simulation described in this manuscript, our aim is to develop adequate detection methods for manufacturing-related safety signals, especially in the context of biological products. Pharmaceutical companies record the entire batch genealogies, from seed batches over intermediates to final product (FP) batches. We constructed a hierarchical tree based on this genealogy information and linked it to the spontaneous safety data available for the FP batch numbers. The tree-based scan statistic (TBSS) was used on simulated data as a proof of concept to locate the source that may have subsequently generated an excess of specific adverse events (AEs) within the manufacturing steps, and to evaluate the method's adjustment for multiple testing.

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Meningococcal Conjugate Vaccine Safety Surveillance in the Vaccine Safety Datalink Using a Tree-Temporal Scan Data Mining Method
Li R, Weintraub E, McNeil MM, Kulldorff M, Lewis EM, Nelson J, Xu S, Qian L, Klein NP, Destefano F.
February 18, 2018

The objective of this study was to conduct a data mining analysis to identify potential adverse events (AEs) following MENACWY-D using the tree-temporal scan statistic in the Vaccine Safety Datalink population and demonstrate the feasibility of this method in a large distributed safety data setting. Traditional pharmacovigilance techniques used in vaccine safety are generally geared to detecting AEs based on pre-defined sets of conditions or diagnoses. Using a newly developed tree-temporal scan statistic data mining method, a pilot study was performed to evaluate the safety profile of the meningococcal conjugate vaccine Menactra® (MenACWY-D), screening thousands of potential AE diagnoses and diagnosis groupings. The study cohort included enrolled participants in the Vaccine Safety Datalink aged 11 to 18 years who had received MenACWY-D vaccination(s) between 2005 and 2014. The tree-temporal scan statistic was employed to identify statistical associations (signals) of AEs following MENACWY-D at a 0.05 level of significance, adjusted for multiple testing. 

View the article here.