PMID- 23331230
OWN - NLM
STAT- MEDLINE
DCOM- 20131025
LR  - 20131121
IS  - 1520-5711 (Electronic)
IS  - 1054-3406 (Linking)
VI  - 23
IP  - 1
DP  - 2013
TI  - Likelihood ratio test-based method for signal detection in drug classes using 
      FDA's AERS database.
PG  - 178-200
LID - 10.1080/10543406.2013.736810 [doi]
AB  - In 1968 the Food and Drug Administration (FDA) established the Adverse Event 
      Reporting System (AERS) database containing data on adverse events (AEs) reported 
      by patients, health care providers, and other sources through a spontaneous 
      reporting system. FDA uses AERS for monitoring the safety of the drugs on the 
      market after approval. Most statistical methods that are available in the 
      literature to analyze large postmarket drug safety data for identifying 
      drug-event combinations with disproportionately high frequencies are designed to 
      explore signals of a single drug-AE combination, but not signals including a drug 
      class or a group of AEs simultaneously. Those methods are also not designed to 
      control type I error and are subject to high false discovery rates. In this 
      paper, we first briefly review a recently developed method, known as the 
      likelihood ratio test (LRT)-based method, which has been demonstrated to control 
      the family-wise type I error and false discovery rates. By introducing a concept 
      of weight matrix for the drugs (or for AEs), we then extend the LRT method for 
      detecting signals including a class of drugs (or AEs) in addition to detecting 
      signals of single drug (or AE). A simplified Bayesian method is also proposed and 
      compared with LRT method. The proposed methods are applied to study the signal 
      patterns of drug classes, namely, the gadolinium drug class for magnetic 
      resonance imaging (MRI) and statins for hypercholesterolemia, over different time 
      periods, using the datasets with only suspect drugs and with both suspect and 
      concomitant drugs from the AERS database. The signals detected by the statistical 
      methods can be confirmed by signals detected across different databases, existing 
      medical evidence from research or regulatory resources, prospective biological 
      studies, and also through simulation as illustrated in the application.
FAU - Huang, Lan
AU  - Huang L
AD  - Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug 
      Administration, Silver Spring, Maryland 20993, USA. lan.huang@fda.hhs.gov
FAU - Zalkikar, Jyoti
AU  - Zalkikar J
FAU - Tiwari, Ram C
AU  - Tiwari RC
LA  - eng
PT  - Journal Article
PL  - England
TA  - J Biopharm Stat
JT  - Journal of biopharmaceutical statistics
JID - 9200436
RN  - 0 (Pharmaceutical Preparations)
SB  - IM
MH  - Adverse Drug Reaction Reporting Systems/*standards/statistics & numerical data
MH  - Databases, Factual/*standards/statistics & numerical data
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - Humans
MH  - Likelihood Functions
MH  - Pharmaceutical Preparations/*classification
MH  - Statistics as Topic/*methods/*standards
MH  - United States
MH  - United States Food and Drug Administration/*standards/statistics & numerical data
EDAT- 2013/01/22 06:00
MHDA- 2013/10/26 06:00
CRDT- 2013/01/22 06:00
PHST- 2013/01/22 06:00 [entrez]
PHST- 2013/01/22 06:00 [pubmed]
PHST- 2013/10/26 06:00 [medline]
AID - 10.1080/10543406.2013.736810 [doi]
PST - ppublish
SO  - J Biopharm Stat. 2013;23(1):178-200. doi: 10.1080/10543406.2013.736810.