Background: Epidemiology frequently relies on surrogates of long-term exposures, often either individual-level short-term measurements or group-level based on long-term characteristics of subjects and their environment. Whereas individual-level measures are often imprecise due to within-subject variability, group-level measures tend to be inaccurate due to residual between-subject variability within groups. Rather than choose between these error-prone estimates, we borrow strength from each by use of mixed-model prediction and we compare the predictive validity.
Methods: We compared alternative measures of long-term exposure to carbon monoxide (CO) among children in the RESPIRE woodstove randomized control trial during years 2003 and 2004. The main study included 1932 repeated 48-hour-average personal CO measures among 509 children from 0–18 months of age. We used a validation study with additional CO measures among a random subsample of 70 of the children to compare the predictive validity of individual-level estimates (based on observed short-term exposures), group-level estimates (based on stove type and other residential characteristics), and mixed-model predictions that combine these 2 sources of information.
Results: The estimated error variance for mixed-model prediction was 63% lower than the individual-level measure based on the exposure data and 58% lower than the corresponding group-level measure.
Conclusions: When both individual- and group-level estimates are available but imperfect, mixed-model prediction may provide substantially better measures of long-term exposure, potentially increasing the sensitivity of epidemiologic studies to underlying causal relations.
From the aDepartment of Epidemiology, Harvard School of Public Health, Boston, MA; bDepartment of Environmental Health, Harvard School of Public Health, Boston, MA; cDivision of Public Health, University of Liverpool, Liverpool, UK; dCardiovascular Epidemiology Research Unit, Beth Israel Deaconess Medical Center, Boston, MA; eDepartment of Biostatistics, Harvard School of Public Health, Boston, MA; and fEnvironmental Health Sciences Division, School of Public Health, University of California, Berkeley, CA.
Submitted 5 February 2007; accepted 13 May 2008; posted 2 December 2008.
Supported by NIEHS (R01ES010178), World Health Organization, Geneva, and AC Griffin Family Trust. Additional funding was provided by NIEHS (ES-0002), EPA PM Center (R 827353), and NIEHS (T-32 ES07069-25).
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Correspondence: John P. McCracken, Department of Environmental Health, Harvard School of Public Health, Landmark Building, 415 West, 401 Park Drive, Boston MA 02215. E-mail: email@example.com