{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Cai J"],"funding":["National Institute of Environmental Health Sciences","NIEHS NIH HHS","NIDCD NIH HHS","National Cancer Institute","NCI NIH HHS","National Institutes of Health","NIH HHS"],"pagination":["3739-3751"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11214728"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["79(4)"],"pubmed_abstract":["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"],"journal":["Biometrics"],"pubmed_title":["Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes."],"pmcid":["PMC11214728"],"funding_grant_id":["P30 ES000002","5R01ES026246","R01 ES026246","R01 DC017717","R03 CA252808","R01 ES017017","R21 ES016829","UM1 CA186107"],"pubmed_authors":["Wang M","Spiegelman D","Cai J","Zhang N","Zhou X"],"additional_accession":[]},"is_claimable":false,"name":"Correcting for bias due to mismeasured exposure history in longitudinal studies with continuous outcomes.","description":"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","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Dec","modification":"2025-04-21T22:05:48.665Z","creation":"2025-04-05T18:37:02.173Z"},"accession":"S-EPMC11214728","cross_references":{"pubmed":["37222518"],"doi":["10.1111/biom.13877"]}}