Comparison and validation of data-mining indices for signal detection: Using the Korean national health insurance claims database

Purpose: To detect the signals of celecoxib compared with other analgesics and anti-inflammatory drugs (AAIDs) by proportional claims ratio (PCR), claims odds ratio (COR), information component (IC), and relative risk (RR) using the Korean claims database. In addition, the concordance of the identified signals by the data-mining indices (DMIs) and the validity of the DMIs were evaluated. Methods: The Korean Health Insurance Review and Assessment Service claims database was used. The study population consisted of elderly ambulatory care patients with osteoarthritis who were prescribed AAIDs in Seoul from 1 January 2005 to 30 September 2005. A short-term serious adverse event (SAE) was defined as a hospital admission within 12weeks from each AAID prescription. Among the screened SAEs, signals were identified by the DMIs. The sensitivity, specificity, and predictability were estimated with reference to known adverse events associated with celecoxib. Results: A total of 135232 elderly patients with osteoarthritis were prescribed AAIDs. There were 309717 drug-SAE pairs and 481 different SAEs. The PCR, COR, IC, and RR detected were as follows: 56 (11.6%), 57 (11.9%), 129 (26.8%), and 123 (25.6%) signals for celecoxib, respectively. The RR detected signals had a relatively high sensitivity (23.4%) compared with the other indices (PCR 9.9%, COR 10.8%, and IC 18.9%). The specificity of RR (73.8%) was higher than that of IC (70.8%). The positive and negative predictive values of the RR were 21.1% and 76.3%, respectively. Conclusion: This study suggested that the RR was the most accurate of the DMIs for detecting signals in the claims database.