PMID- 30470642
OWN - NLM
STAT- MEDLINE
DCOM- 20190606
LR  - 20190606
IS  - 1873-2518 (Electronic)
IS  - 0264-410X (Linking)
VI  - 37
IP  - 1
DP  - 2019 Jan 3
TI  - Tree-based scan statistic - Application in manufacturing-related safety signal 
      detection.
PG  - 49-55
LID - S0264-410X(18)31567-6 [pii]
LID - 10.1016/j.vaccine.2018.11.044 [doi]
AB  - BACKGROUND AND OBJECTIVES: Over the last decades, medicinal regulations have been 
      put into place and have considerably improved manufacturing practices. 
      Nevertheless, safety issues may still arise. Using the simulation described in 
      this manuscript, our aim is to develop adequate detection methods for 
      manufacturing-related safety signals, especially in the context of biological 
      products. METHODS: Pharmaceutical companies record the entire batch genealogies, 
      from seed batches over intermediates to final product (FP) batches. We 
      constructed a hierarchical tree based on this genealogy information and linked it 
      to the spontaneous safety data available for the FP batch numbers. The tree-based 
      scan statistic (TBSS) was used on simulated data as a proof of concept to locate 
      the source that may have subsequently generated an excess of specific adverse 
      events (AEs) within the manufacturing steps, and to evaluate the method's 
      adjustment for multiple testing. All calculations were performed with a 
      customized program in SAS v9.2. RESULTS: The TBSS generated a close to expected 
      number of false positive signals, demonstrating that it adjusted for multiple 
      testing. Overall, the method detected 71% of the simulated signals at the correct 
      production step when a 6-fold increase in reports with AEs of interest (AEOI) was 
      applied, and 31% when a 2-fold increase was applied. The relatively low detection 
      performance may be attributed to the higher granularity associated with the lower 
      levels of the hierarchy, leading to a lack of power and the stringent definition 
      criteria that were applied for a true positive result. CONCLUSION: As a 
      data-mining method for manufacturing-related safety signal detection, the TBSS 
      may provide advantages over other disproportionality analyses (using batch 
      information) but may benefit from complementary methods (not relaying on batch 
      information). While the method warrants further refinement, it may improve safety 
      signal detection and contribute to improvements in the quality of manufacturing 
      processes.
CI  - Copyright (c) 2018 GlaxoSmithKline Biologicals SA. Published by Elsevier Ltd.. All 
      rights reserved.
FAU - Mahaux, Olivia
AU  - Mahaux O
AD  - Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium. Electronic 
      address: Olivia.x.mahaux@gsk.com.
FAU - Bauchau, Vincent
AU  - Bauchau V
AD  - Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium.
FAU - Zeinoun, Ziad
AU  - Zeinoun Z
AD  - Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium.
FAU - Van Holle, Lionel
AU  - Van Holle L
AD  - Vaccine Clinical Safety and Pharmacovigilance, GSK, Waver, Belgium.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20181122
PL  - Netherlands
TA  - Vaccine
JT  - Vaccine
JID - 8406899
RN  - 0 (Vaccines)
SB  - IM
MH  - Data Mining/*methods
MH  - Manufacturing Industry/legislation & jurisprudence
MH  - Monte Carlo Method
MH  - Patient Safety
MH  - Product Surveillance, Postmarketing/*methods
MH  - Software
MH  - Vaccines/*adverse effects/*standards
OTO - NOTNLM
OT  - Data-mining
OT  - Manufacturing
OT  - Safety signal detection
OT  - Tree-based scan statistic
EDAT- 2018/11/25 06:00
MHDA- 2019/06/07 06:00
CRDT- 2018/11/25 06:00
PHST- 2018/08/16 00:00 [received]
PHST- 2018/10/23 00:00 [revised]
PHST- 2018/11/14 00:00 [accepted]
PHST- 2018/11/25 06:00 [pubmed]
PHST- 2019/06/07 06:00 [medline]
PHST- 2018/11/25 06:00 [entrez]
AID - S0264-410X(18)31567-6 [pii]
AID - 10.1016/j.vaccine.2018.11.044 [doi]
PST - ppublish
SO  - Vaccine. 2019 Jan 3;37(1):49-55. doi: 10.1016/j.vaccine.2018.11.044. Epub 2018 
      Nov 22.