PMID- 26125967
OWN - NLM
STAT- MEDLINE
DCOM- 20160422
LR  - 20201217
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 10
IP  - 6
DP  - 2015
TI  - Choosing a Cluster Sampling Design for Lot Quality Assurance Sampling Surveys.
PG  - e0129564
LID - 10.1371/journal.pone.0129564 [doi]
LID - e0129564
AB  - Lot quality assurance sampling (LQAS) surveys are commonly used for monitoring 
      and evaluation in resource-limited settings. Recently several methods have been 
      proposed to combine LQAS with cluster sampling for more timely and cost-effective 
      data collection. For some of these methods, the standard binomial model can be 
      used for constructing decision rules as the clustering can be ignored. For other 
      designs, considered here, clustering is accommodated in the design phase. In this 
      paper, we compare these latter cluster LQAS methodologies and provide 
      recommendations for choosing a cluster LQAS design. We compare technical 
      differences in the three methods and determine situations in which the choice of 
      method results in a substantively different design. We consider two different 
      aspects of the methods: the distributional assumptions and the clustering 
      parameterization. Further, we provide software tools for implementing each method 
      and clarify misconceptions about these designs in the literature. We illustrate 
      the differences in these methods using vaccination and nutrition cluster LQAS 
      surveys as example designs. The cluster methods are not sensitive to the 
      distributional assumptions but can result in substantially different designs 
      (sample sizes) depending on the clustering parameterization. However, none of the 
      clustering parameterizations used in the existing methods appears to be 
      consistent with the observed data, and, consequently, choice between the cluster 
      LQAS methods is not straightforward. Further research should attempt to 
      characterize clustering patterns in specific applications and provide suggestions 
      for best-practice cluster LQAS designs on a setting-specific basis.
FAU - Hund, Lauren
AU  - Hund L
AD  - Department of Family and Community Medicine, University of New Mexico, 
      Albuquerque, NM, USA.
FAU - Bedrick, Edward J
AU  - Bedrick EJ
AD  - Department of Biostatistics and Informatics, University of Colorado, Aurora, CO, 
      USA.
FAU - Pagano, Marcello
AU  - Pagano M
AD  - Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
LA  - eng
GR  - R01 AI097015/AI/NIAID NIH HHS/United States
GR  - T32 ES007142/ES/NIEHS NIH HHS/United States
GR  - R01 AI097015-01A1/AI/NIAID NIH HHS/United States
PT  - Comparative Study
PT  - Evaluation Study
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
DEP - 20150630
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
SB  - IM
MH  - Cluster Analysis
MH  - Global Health/statistics & numerical data
MH  - Health Surveys/methods/statistics & numerical data
MH  - Humans
MH  - Lot Quality Assurance Sampling/*methods/statistics & numerical data
MH  - Models, Statistical
MH  - Sample Size
MH  - Sampling Studies
PMC - PMC4488393
COIS- Competing Interests: The authors have declared that no competing interests exist.
EDAT- 2015/07/01 06:00
MHDA- 2016/04/23 06:00
PMCR- 2015/06/30
CRDT- 2015/07/01 06:00
PHST- 2014/09/08 00:00 [received]
PHST- 2015/05/11 00:00 [accepted]
PHST- 2015/07/01 06:00 [entrez]
PHST- 2015/07/01 06:00 [pubmed]
PHST- 2016/04/23 06:00 [medline]
PHST- 2015/06/30 00:00 [pmc-release]
AID - PONE-D-14-40415 [pii]
AID - 10.1371/journal.pone.0129564 [doi]
PST - epublish
SO  - PLoS One. 2015 Jun 30;10(6):e0129564. doi: 10.1371/journal.pone.0129564. 
      eCollection 2015.