{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T12:21:32Z","timestamp":1778070092710,"version":"3.51.4"},"reference-count":30,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T00:00:00Z","timestamp":1750982400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Paediatric Perinatal Epid"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In birth registries, incomplete recording of information leads to missing values. Multiple imputation (MI) by chained equations is a widely used method for analysing datasets with missing data. It is unknown whether using registry records from multiple pregnancies contributed by the same woman could potentially give more accurate values when resolving missing data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To investigate the relative performance of five methods to infer missing data on maternal characteristics using data from a medical birth registry, comparing longitudinal methods and MI with data from previous and future pregnancies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We used data from the Medical Birth Registry of Norway (MBRN), selecting records among mothers with more than one pregnancy between 2004 and 2018. Longitudinal methods used reference pregnancies in three time directions: past, future and closest pregnancy record. MI was conducted with only index pregnancy records (single\u2010pregnancy MI) and with both index and closest reference pregnancy records (multiple\u2010pregnancy MI). Validity was assessed by comparing the actual values with inferred\/imputed values. For continuous variables, we calculated the proportion of inferred values within predefined increments. For binary variables, we calculated five parameters: agreement rate, sensitivity, specificity, positive predictive value and negative predictive value.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We included 578,670 pregnancies among 256,658 women. For continuous variables, the longitudinal methods showed the highest proportion within predefined increments, followed by multiple\u2010pregnancy MI, and single\u2010pregnancy MI showed the lowest value. For binary variables, longitudinal methods generally showed higher values among the five validity parameters than MI. Single\u2010pregnancy MI had substantially lower agreement, while multiple\u2010pregnancy MI performed similarly to longitudinal methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The longitudinal method outperformed MI in inferring missing data on maternal characteristics in a medical birth registry.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/ppe.70011","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T10:24:06Z","timestamp":1751019846000},"page":"264-277","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Longitudinal Methods Versus Multiple Imputation to Infer Missing Maternal Data in Registry\u2010Based Pregnancy Studies"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9086-6999","authenticated-orcid":false,"given":"Takamasa","family":"Sakai","sequence":"first","affiliation":[{"name":"PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences University of Oslo  Oslo Norway"},{"name":"Drug Informatics, Faculty of Pharmacy Meijo University  Nagoya Aichi Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6361-2918","authenticated-orcid":false,"given":"Hedvig","family":"Nordeng","sequence":"additional","affiliation":[{"name":"PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences University of Oslo  Oslo Norway"},{"name":"Department of Child Health and Development Norwegian Institute of Public Health  Oslo Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4853-4434","authenticated-orcid":false,"given":"Marleen M. H. J.","family":"van Gelder","sequence":"additional","affiliation":[{"name":"PharmacoEpidemiology and Drug Safety Research Group, Department of Pharmacy, Faculty of Mathematics and Natural Sciences University of Oslo  Oslo Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007379029182"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.2515\/therapie\/2014009"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.2147\/CLEP.S314959"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/aogs.12302"},{"key":"e_1_2_11_6_1","unstructured":"European Union \u201cAnnex 2. 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