Analysis of self-report and biochemically verified tobacco abstinence outcomes with missing data: a sensitivity analysis using two-stage imputation

Analysis of self-report and biochemically verified tobacco abstinence outcomes with missing data: a sensitivity analysis using two-stage imputation

Abstract Background Missing data are common in tobacco studies. It is well known that from the observed data alone, it is impossible to distinguish between missing mechanisms such as missing at random (MAR) and missing not at random (MNAR). In this paper, we propose a sensitivity analysis method to...

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Bibliographic Details
Main Authors: Yiwen Zhang, Xianghua Luo, Chap T. Le, Jasjit S. Ahluwalia, Janet L. Thomas
Format: Article
Language:English
Published: BMC 2018-12-01
Series:BMC Medical Research Methodology
Subjects:
Abstinence outcome
Imputation
Missing data
Sensitivity analysis
Online Access:http://link.springer.com/article/10.1186/s12874-018-0635-2

Internet

http://link.springer.com/article/10.1186/s12874-018-0635-2

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