title: Comparison and validation of data-mining indices for signal detection: using the Korean national health insurance claims database.
abstract: OBJECTIVE
  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 12 weeks 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 135,232 elderly patients with osteoarthritis were prescribed AAIDs. There were 309,717 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.
  
  
  CONCLUSIONS
  This study suggested that the RR was the most accurate of the DMIs for detecting signals in the claims database.
authors: Medical Research Collaborating Center, Seoul National University Hospital/Seoul National University College of Medicine, Seoul, Korea.
authors: Nam-Kyong
authors: NK
authors: Choi
authors: Yoosoo
authors: Y
authors: Chang
authors: Ju-Young
authors: JY
authors: Kim
authors: Yu-Kyong
authors: YK
authors: Choi
authors: Byung-Joo
authors: BJ
authors: Park
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000368:Aged; D000893:Anti-Inflammatory Agents / Q000009:adverse effects* / Q000627:therapeutic use; D000068579:Celecoxib; D052246:Cyclooxygenase 2 Inhibitors / Q000009:adverse effects / Q000627:therapeutic use; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D016017:Odds Ratio; D010003:Osteoarthritis / Q000188:drug therapy; D011237:Predictive Value of Tests; D011720:Pyrazoles / Q000009:adverse effects* / Q000627:therapeutic use; D056910:Republic of Korea; D012306:Risk; D012680:Sensitivity and Specificity; D013449:Sulfonamides / Q000009:adverse effects* / Q000627:therapeutic use
chemical_list: D000893:Anti-Inflammatory Agents; D052246:Cyclooxygenase 2 Inhibitors; D011720:Pyrazoles; D013449:Sulfonamides; D000068579:Celecoxib