Sampling over time: developing a cost effective and precise exposure assessment program.
Studies requiring ambient exposure assessments invariably ask: How often should measurements be taken? Answer to such questions is dictated by budgetary considerations as well as spatial and temporal variability in the data. For example, do we obtain measurements during all seasons, all months within seasons, weeks within months and days within weeks? On one hand, we can obtain a one-time snapshot sample and regard it as representing the "true" mean exposure. On the other hand, we may obtain a large number of measurements over time and then average these in order to represent this "true" mean exposure. The former estimate is the least expensive but may also be the least precise while the latter, may be very precise but prohibitively costly. In this paper, we demonstrate how a pilot study can be undertaken with a potentially promising and feasible sampling plan for the full-scale study. By applying the statistical methodology of variance component analysis (VCA) to the pilot study data and exploiting mathematical relationship between the variance of the overall mean exposure and posited variance components, we can develop a sampling design with decreased sampling costs and/or increased precision of the mean exposure. Our approach was applied to determine sampling design choices for an on-going study that aimed at assessing ambient particulate matter exposure. We conclude that a pilot study followed by the VCA analysis may often lead to sampling design choices that offer considerable cost savings and, at the same time, promise to provide relatively precise estimates of the mean exposure for the subsequent full-scale study.
Shukla
Rakesh
R
Division of Epidemiology & Biostatistics, Department of Environmental Health, University of Cincinnati, OH, USA. Rakesh.Shukla@uc.edu
Luo
Junxiang
J
Lemasters
Grace K
GK
Grinshpun
Sergey A
SA
Martuzevicius
Dainius
D
D000704:Analysis of Variance; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D004784:Environmental Monitoring / Q000191:economics / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D012107:Research Design; D012494:Sampling Studies; D013997:Time Factors