PMID- 24382319
OWN - NLM
STAT- MEDLINE
DCOM- 20140414
LR  - 20220204
IS  - 1365-3156 (Electronic)
IS  - 1360-2276 (Linking)
VI  - 19
IP  - 3
DP  - 2014 Mar
TI  - Estimation after classification using lot quality assurance sampling: corrections 
      for curtailed sampling with application to evaluating polio vaccination 
      campaigns.
PG  - 321-330
LID - 10.1111/tmi.12247 [doi]
AB  - OBJECTIVES: To assess the bias incurred when curtailment of Lot Quality Assurance 
      Sampling (LQAS) is ignored, to present unbiased estimators, to consider the 
      impact of cluster sampling by simulation and to apply our method to published 
      polio immunization data from Nigeria. METHODS: We present estimators of coverage 
      when using two kinds of curtailed LQAS strategies: semicurtailed and curtailed. 
      We study the proposed estimators with independent and clustered data using three 
      field-tested LQAS designs for assessing polio vaccination coverage, with samples 
      of size 60 and decision rules of 9, 21 and 33, and compare them to biased maximum 
      likelihood estimators. Lastly, we present estimates of polio vaccination coverage 
      from previously published data in 20 local government authorities (LGAs) from 
      five Nigerian states. RESULTS: Simulations illustrate substantial bias if one 
      ignores the curtailed sampling design. Proposed estimators show no bias. 
      Clustering does not affect the bias of these estimators. Across simulations, 
      standard errors show signs of inflation as clustering increases. Neither sampling 
      strategy nor LQAS design influences estimates of polio vaccination coverage in 20 
      Nigerian LGAs. When coverage is low, semicurtailed LQAS strategies considerably 
      reduces the sample size required to make a decision. Curtailed LQAS designs 
      further reduce the sample size when coverage is high. CONCLUSIONS: Results 
      presented dispel the misconception that curtailed LQAS data are unsuitable for 
      estimation. These findings augment the utility of LQAS as a tool for monitoring 
      vaccination efforts by demonstrating that unbiased estimation using curtailed 
      designs is not only possible but these designs also reduce the sample size.
CI  - (c) 2014 John Wiley & Sons Ltd.
FAU - Olives, Casey
AU  - Olives C
AD  - Department of Environmental and Occupational Health Sciences, University of 
      Washington, Seattle, WA, USA.
FAU - Valadez, Joseph J
AU  - Valadez JJ
AD  - Department of International Public Health, Liverpool School of Tropical Medicine, 
      Liverpool, UK.
FAU - Pagano, Marcello
AU  - Pagano M
AD  - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
LA  - eng
GR  - R01 AI 97015/AI/NIAID NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20140102
PL  - England
TA  - Trop Med Int Health
JT  - Tropical medicine & international health : TM & IH
JID - 9610576
SB  - IM
MH  - Bias
MH  - Child, Preschool
MH  - Cluster Analysis
MH  - Humans
MH  - Infant
MH  - Local Government
MH  - Lot Quality Assurance Sampling/*methods
MH  - Mass Vaccination/*statistics & numerical data
MH  - Nigeria/epidemiology
MH  - Poliomyelitis/*prevention & control
MH  - Program Evaluation/*methods
MH  - Quality Assurance, Health Care/methods
MH  - Sample Size
OTO - NOTNLM
OT  - Lot Quality Assurance Sampling
OT  - curtailed sampling
OT  - estimation
OT  - polio vaccination
OT  - program evaluation
OT  - semi-curtailed sampling
EDAT- 2014/01/03 06:00
MHDA- 2014/04/15 06:00
CRDT- 2014/01/03 06:00
PHST- 2014/01/03 06:00 [entrez]
PHST- 2014/01/03 06:00 [pubmed]
PHST- 2014/04/15 06:00 [medline]
AID - 10.1111/tmi.12247 [doi]
PST - ppublish
SO  - Trop Med Int Health. 2014 Mar;19(3):321-330. doi: 10.1111/tmi.12247. Epub 2014 
      Jan 2.