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Epidemiologists are often interested in estimating the effect of functions of time‐varying exposure histories in relation to continuous outcomes, for example, cognitive function. However, the individual exposure measurements that constitute the history upon which an exposure history function is constructed are usually mismeasured. To obtain unbiased estimates of the effects for mismeasured functions in longitudinal studies, a method incorporating main and validation studies was developed. Simulation studies under several realistic assumptions were conducted to assess its performance compared to standard analysis, and we found that the proposed method has good performance in terms of finite sample bias reduction and nominal confidence interval coverage. We applied it to a study of long‐term exposure to PM2.5$\text{PM}_{2.5}$, in relation to cognitive decline in the Nurses' Health Study Previously, it was found that the 2‐year decline in the standard measure of cognition was 0.018 (95% CI, −0.034 to −0.001) units worse per 10 μg/m3$\mu \text{g/m}^3$ increase in PM2.5$\text{PM}_{2.5}$ exposure. After correction, the estimated impact of PM2.5$\text{PM}_{2.5}$ on cognitive decline increased to 0.027 (95% CI, −0.059 to 0.005) units lower per 10 μg/m3$\mu \text{g/m}^3$ increase. To put this into perspective, effects of this magnitude are about 2/3 of those found in our data associated with each additional year of aging: 0.044 (95% CI, −0.047 to −0.040) units per 1 year older after applying our correction method. [ABSTRACT FROM AUTHOR]

Participant‐level meta‐analysis across multiple studies increases the sample size for pooled analyses, thereby improving precision in effect estimates and enabling subgroup analyses. For analyses involving biomarker measurements as an exposure of interest, investigators must first calibrate the data to address measurement variability arising from usage of different laboratories and/or assays. In practice, the calibration process involves reassaying a random subset of biospecimens from each study at a central laboratory and fitting models that relate the study‐specific "local" and central laboratory measurements. Previous work in this area treats the calibration process from the perspective of measurement error techniques and imputes the estimated central laboratory value among individuals with only a local laboratory measurement. In this work, we propose a repeated measures method to calibrate biomarker measurements pooled from multiple studies with study‐specific calibration subsets. We account for correlation between measurements made on the same person and between measurements made at the same laboratory. We demonstrate that the repeated measures approach provides valid inference, and compare it to existing calibration approaches grounded in measurement error techniques in an example describing the association between circulating vitamin D and stroke. [ABSTRACT FROM AUTHOR]