PMID- 23378151
OWN - NLM
STAT- MEDLINE
DCOM- 20130923
LR  - 20211021
IS  - 1464-3685 (Electronic)
IS  - 0300-5771 (Print)
IS  - 0300-5771 (Linking)
VI  - 42
IP  - 1
DP  - 2013 Feb
TI  - Choosing a design to fit the situation: how to improve specificity and positive 
      predictive values using Bayesian lot quality assurance sampling.
PG  - 346-55
LID - 10.1093/ije/dys230 [doi]
AB  - BACKGROUND: Lot Quality Assurance Sampling (LQAS) is a provably useful tool for 
      monitoring health programmes. Although LQAS ensures acceptable Producer and 
      Consumer risks, the literature alleges that the method suffers from poor 
      specificity and positive predictive values (PPVs). We suggest that poor LQAS 
      performance is due, in part, to variation in the true underlying distribution. 
      However, until now the role of the underlying distribution in expected 
      performance has not been adequately examined. METHODS: We present Bayesian-LQAS 
      (B-LQAS), an approach to incorporating prior information into the choice of the 
      LQAS sample size and decision rule, and explore its properties through a 
      numerical study. Additionally, we analyse vaccination coverage data from UNICEF's 
      State of the World's Children in 1968-1989 and 2008 to exemplify the performance 
      of LQAS and B-LQAS. RESULTS: Results of our numerical study show that the choice 
      of LQAS sample size and decision rule is sensitive to the distribution of prior 
      information, as well as to individual beliefs about the importance of correct 
      classification. Application of the B-LQAS approach to the UNICEF data improves 
      specificity and PPV in both time periods (1968-1989 and 2008) with minimal 
      reductions in sensitivity and negative predictive value. CONCLUSIONS: LQAS is 
      shown to be a robust tool that is not necessarily prone to poor specificity and 
      PPV as previously alleged. In situations where prior or historical data are 
      available, B-LQAS can lead to improvements in expected performance.
FAU - Olives, Casey
AU  - Olives C
AD  - Department of Biostatistics, University of Washington, Seattle, WA, USA. 
      colives@uw.edu
FAU - Pagano, Marcello
AU  - Pagano M
LA  - eng
PT  - Journal Article
DEP - 20130201
PL  - England
TA  - Int J Epidemiol
JT  - International journal of epidemiology
JID - 7802871
RN  - 0 (Measles Vaccine)
SB  - IM
MH  - *Bayes Theorem
MH  - Child
MH  - Humans
MH  - Immunization/*statistics & numerical data
MH  - *Lot Quality Assurance Sampling
MH  - Measles Vaccine/administration & dosage
MH  - Population Surveillance
MH  - Predictive Value of Tests
MH  - Program Evaluation/methods
MH  - Quality Assurance, Health Care/methods
MH  - Research Design
MH  - Sensitivity and Specificity
MH  - United Nations
PMC - PMC3600627
EDAT- 2013/02/05 06:00
MHDA- 2013/09/24 06:00
PMCR- 2014/02/01
CRDT- 2013/02/05 06:00
PHST- 2013/02/05 06:00 [entrez]
PHST- 2013/02/05 06:00 [pubmed]
PHST- 2013/09/24 06:00 [medline]
PHST- 2014/02/01 00:00 [pmc-release]
AID - dys230 [pii]
AID - 10.1093/ije/dys230 [doi]
PST - ppublish
SO  - Int J Epidemiol. 2013 Feb;42(1):346-55. doi: 10.1093/ije/dys230. Epub 2013 Feb 1.