TY - JOUR
T1 - Using electronic health care records for drug safety signal detection
T2 - A comparative evaluation of statistical methods
AU - Schuemie, Martijn J.
AU - Coloma, Preciosa M.
AU - Straatman, Huub
AU - Herings, Ron M.C.
AU - Trifirò, Gianluca
AU - Matthews, Justin Neil
AU - Prieto-Merino, David
AU - Molokhia, Mariam
AU - Pedersen, Lars
AU - Gini, Rosa
AU - Innocenti, Francesco
AU - Mazzaglia, Giampiero
AU - Picelli, Gino
AU - Scotti, Lorenza
AU - Van Der Lei, Johan
AU - Sturkenboom, Miriam C.J.M.
PY - 2012/10
Y1 - 2012/10
N2 - BACKGROUND: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). OBJECTIVES: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. RESEARCH DESIGN: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method. MEASURES: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. RESULTS: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. CONCLUSIONS: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.
AB - BACKGROUND: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). OBJECTIVES: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. RESEARCH DESIGN: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method. MEASURES: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. RESULTS: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. CONCLUSIONS: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals.
KW - active surveillance
KW - drug safety
KW - electronic health record
KW - method evaluation
UR - http://www.scopus.com/inward/record.url?scp=84866563491&partnerID=8YFLogxK
U2 - 10.1097/MLR.0b013e31825f63bf
DO - 10.1097/MLR.0b013e31825f63bf
M3 - Article
SN - 0025-7079
VL - 50
SP - 890
EP - 897
JO - Medical Care
JF - Medical Care
IS - 10
ER -