{"title":"缺失数据：问题、概念和方法","authors":"Tra My Pham , Nikolaos Pandis , Ian R White","doi":"10.1053/j.sodo.2024.01.007","DOIUrl":null,"url":null,"abstract":"<div><p>Missing data are a common issue in medical research. We aim to explain in non-technical language the issues and concepts around missing data, as well as discuss common methods for handling missing data. Specifically, our objectives are to answer the following questions: (1) What are missing data and why should we care about them? (2) What are the missingness mechanisms and how do they impact statistical analysis? (3) How can we explore missing values in our datasets? (4) What are ad-hoc methods for dealing with missing values and are they valid? (5) What is multiple imputation? (6) What should we consider when conducting a multiple imputation analysis? (7) Is multiple imputation always needed? (8) How should we report an analysis with missing data? We illustrate discussions with examples from an orthodontic study.</p></div>","PeriodicalId":48688,"journal":{"name":"Seminars in Orthodontics","volume":"30 1","pages":"Pages 37-44"},"PeriodicalIF":2.2000,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1073874624000082/pdfft?md5=fa6de328a3209570f08491efe81cf914&pid=1-s2.0-S1073874624000082-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Missing data: Issues, concepts, methods\",\"authors\":\"Tra My Pham , Nikolaos Pandis , Ian R White\",\"doi\":\"10.1053/j.sodo.2024.01.007\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Missing data are a common issue in medical research. We aim to explain in non-technical language the issues and concepts around missing data, as well as discuss common methods for handling missing data. Specifically, our objectives are to answer the following questions: (1) What are missing data and why should we care about them? (2) What are the missingness mechanisms and how do they impact statistical analysis? (3) How can we explore missing values in our datasets? (4) What are ad-hoc methods for dealing with missing values and are they valid? (5) What is multiple imputation? (6) What should we consider when conducting a multiple imputation analysis? (7) Is multiple imputation always needed? (8) How should we report an analysis with missing data? We illustrate discussions with examples from an orthodontic study.</p></div>\",\"PeriodicalId\":48688,\"journal\":{\"name\":\"Seminars in Orthodontics\",\"volume\":\"30 1\",\"pages\":\"Pages 37-44\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2024-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1073874624000082/pdfft?md5=fa6de328a3209570f08491efe81cf914&pid=1-s2.0-S1073874624000082-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Seminars in Orthodontics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1073874624000082\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seminars in Orthodontics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1073874624000082","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
Missing data are a common issue in medical research. We aim to explain in non-technical language the issues and concepts around missing data, as well as discuss common methods for handling missing data. Specifically, our objectives are to answer the following questions: (1) What are missing data and why should we care about them? (2) What are the missingness mechanisms and how do they impact statistical analysis? (3) How can we explore missing values in our datasets? (4) What are ad-hoc methods for dealing with missing values and are they valid? (5) What is multiple imputation? (6) What should we consider when conducting a multiple imputation analysis? (7) Is multiple imputation always needed? (8) How should we report an analysis with missing data? We illustrate discussions with examples from an orthodontic study.
期刊介绍：
Each issue provides up-to-date, state-of-the-art information on a single topic in orthodontics. Readers are kept abreast of the latest innovations, research findings, clinical applications and clinical methods. Collection of the issues will provide invaluable reference material for present and future review.
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