Data Mining for Signal Detection of Targeted Therapy Related Drug Toxicity in Breast Cancer Patients

Application of signal detection methods using claims data can improve post-marketing drug surveillance. The aim of this study is to compare two routinely used approaches, the proportional reporting ratio (PRR) and Gamma Poisson Shrinker (GPS) with a tree-based scan statistic (TBSS). Using data from the Texas Cancer Registry and Surveillance, Epidemiology and End Results linked to Medicare from 2010-2014 we identified 8,949 patients with breast cancer treated with chemotherapy and 2,542 patients treated with trastuzumab in addition to chemotherapy. Inpatient and outpatient visits up to 1 year from start of therapy were used to identify adverse events (AEs). For each method two signaling thresholds were evaluated. Across all methods we found a total of 34 signals associated with use of trastuzumab. Clinical review determined that most identified signals represented known AEs or confounding. GPS on the highest signaling threshold failed to detect a well-established AE when time of follow-up was less than 6 months. Overall there was considerable agreement between methods with GPS being the most conservative. PRR and TBSS may be more appropriate in exploratory drug safety studies using this dataset.

Name thesis:degree_name

Public Health (Masters)

Level thesis:degree_level

Masters

Grantor

The University of Texas Medical Branch at Galveston

Author dc:creator

• Polychronopoulou, Efstathia

• data mining, adverse events, pharmacovigilance

Handle dc:identifier.uri

https://hdl.handle.net/2152.3/11585

OAI identifier oai:identifier

oai:utmb-ir.tdl.org:2152.3/11585