{"title":"关于处理条件缺失值的说明","authors":"","doi":"10.1016/j.gloepi.2024.100164","DOIUrl":null,"url":null,"abstract":"<div><div>In medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in regression modeling. Using some examples, we illustrate handling of conditional missing values using simple imputation procedures in etiologic and prediction research.</div></div>","PeriodicalId":36311,"journal":{"name":"Global Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A note on handling conditional missing values\",\"authors\":\"\",\"doi\":\"10.1016/j.gloepi.2024.100164\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in regression modeling. Using some examples, we illustrate handling of conditional missing values using simple imputation procedures in etiologic and prediction research.</div></div>\",\"PeriodicalId\":36311,\"journal\":{\"name\":\"Global Epidemiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Epidemiology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590113324000300\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Epidemiology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590113324000300","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In medical research, some variables are conditionally defined on some levels of another variable, leading to conditional missing data. Imputation of this type of structural missing data is needed given inefficiency of listwise deletion inherent in regression modeling. Using some examples, we illustrate handling of conditional missing values using simple imputation procedures in etiologic and prediction research.
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