PMID- 26715499
OWN - NLM
STAT- MEDLINE
DCOM- 20161213
LR  - 20181113
IS  - 1179-1942 (Electronic)
IS  - 0114-5916 (Linking)
VI  - 39
IP  - 3
DP  - 2016 Mar
TI  - A Method for the Minimization of Competition Bias in Signal Detection from 
      Spontaneous Reporting Databases.
PG  - 251-60
LID - 10.1007/s40264-015-0375-8 [doi]
AB  - INTRODUCTION: The two methods for minimizing competition bias in signal of 
      disproportionate reporting (SDR) detection--masking factor (MF) and masking ratio 
      (MR)--have focused on the strength of disproportionality for identifying 
      competitors and have been tested using competitors at the drug level. OBJECTIVES: 
      The aim of this study was to develop a method that relies on identifying 
      competitors by considering the proportion of reports of adverse events (AEs) that 
      mention the drug class at an adequate level of drug grouping to increase 
      sensitivity (Se) for SDR unmasking, and its comparison with MF and MR. METHODS: 
      Reports in the French spontaneous reporting database between 2000 and 2005 were 
      selected. Five AEs were considered: myocardial infarction, pancreatitis, aplastic 
      anemia, convulsions, and gastrointestinal bleeding; related reports were 
      retrieved using standardized Medical Dictionary for Regulatory Activities 
      (MedDRA((R))) queries. Potential competitors of AEs were identified using the 
      developed method, i.e. Competition Index (ComIn), as well as MF and MR. All three 
      methods were tested according to Anatomical Therapeutic Chemical (ATC) 
      classification levels 2-5. For each AE, SDR detection was performed, first in the 
      complete database, and second after removing reports mentioning competitors; SDRs 
      only detected after the removal were unmasked. All unmasked SDRs were validated 
      using the Summary of Product Characteristics, and constituted the reference 
      dataset used for computing the performance for SDR unmasking (area under the 
      curve [AUC], Se). RESULTS: Performance of the ComIn was highest when considering 
      competitors at ATC level 3 (AUC: 62 %; Se: 52 %); similar results were obtained 
      with MF and MR. CONCLUSION: The ComIn could greatly minimize the competition bias 
      in SDR detection. Further study using a larger dataset is needed.
FAU - Arnaud, Mickael
AU  - Arnaud M
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France. mickael.arnaud@u-bordeaux.fr.
AD  - INSERM U657, Bordeaux, France. mickael.arnaud@u-bordeaux.fr.
FAU - Salvo, Francesco
AU  - Salvo F
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France.
AD  - INSERM U657, Bordeaux, France.
AD  - CHU Bordeaux, Bordeaux, France.
FAU - Ahmed, Ismail
AU  - Ahmed I
AD  - Universite de Versailles St Quentin, Villejuif, France.
AD  - INSERM UMR 1181, Biostatistics, Biomathematics, Pharmacoepidemiology and 
      Infectious Diseases, Villejuif, France.
AD  - Institut Pasteur, Paris, France.
FAU - Robinson, Philip
AU  - Robinson P
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France.
AD  - CIC Bordeaux CIC1401, Bordeaux, France.
FAU - Moore, Nicholas
AU  - Moore N
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France.
AD  - INSERM U657, Bordeaux, France.
AD  - CHU Bordeaux, Bordeaux, France.
AD  - CIC Bordeaux CIC1401, Bordeaux, France.
FAU - Begaud, Bernard
AU  - Begaud B
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France.
AD  - INSERM U657, Bordeaux, France.
AD  - CHU Bordeaux, Bordeaux, France.
FAU - Tubert-Bitter, Pascale
AU  - Tubert-Bitter P
AD  - Universite de Versailles St Quentin, Villejuif, France.
AD  - INSERM UMR 1181, Biostatistics, Biomathematics, Pharmacoepidemiology and 
      Infectious Diseases, Villejuif, France.
AD  - Institut Pasteur, Paris, France.
FAU - Pariente, Antoine
AU  - Pariente A
AD  - Universite de Bordeaux, 146 Rue Leo Saignat, BP 36, 33000, Bordeaux Cedex, 
      France.
AD  - INSERM U657, Bordeaux, France.
AD  - CHU Bordeaux, Bordeaux, France.
AD  - CIC Bordeaux CIC1401, Bordeaux, France.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - New Zealand
TA  - Drug Saf
JT  - Drug safety
JID - 9002928
SB  - IM
MH  - Adverse Drug Reaction Reporting Systems/standards/*statistics & numerical data
MH  - *Bias
MH  - Databases, Factual/standards/*statistics & numerical data
MH  - Drug-Related Side Effects and Adverse Reactions/diagnosis/*epidemiology
MH  - Humans
MH  - Pilot Projects
EDAT- 2015/12/31 06:00
MHDA- 2016/12/15 06:00
CRDT- 2015/12/31 06:00
PHST- 2015/12/31 06:00 [entrez]
PHST- 2015/12/31 06:00 [pubmed]
PHST- 2016/12/15 06:00 [medline]
AID - 10.1007/s40264-015-0375-8 [pii]
AID - 10.1007/s40264-015-0375-8 [doi]
PST - ppublish
SO  - Drug Saf. 2016 Mar;39(3):251-60. doi: 10.1007/s40264-015-0375-8.