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

Yiwen Zhang, Xianghua Luo, Chap T. Le, Jasjit S. Ahluwalia, Janet L. Thomas

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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 accommodate different missing mechanisms in cessation outcomes determined by self-report and urine validation results. Methods: We propose a two-stage imputation procedure, allowing survey and urine data to be missing under different mechanisms. The motivating data were from a tobacco cessation trial examining the effects of the extended vs. standard Quit and Win contests and counseling vs. no counseling under a 2-by-2 factorial design. The primary outcome was 6-month biochemically verified tobacco abstinence. Results: Our proposed method covers a wide spectrum of missing scenarios, including the widely adopted "missing = smoking" imputation by assuming a perfect smoking-missing correlation (an extreme case of MNAR), the MAR case by assuming a zero smoking-missing correlation, and many more in between. The analysis of the data example shows that the estimated effects of the studied interventions are sensitive to the different missing assumptions on the survey and urine data. Conclusions: Sensitivity analysis has played a crucial role in assessing the robustness of the findings in clinical trials with missing data. The proposed method provides an effective tool for analyzing missing data introduced at two different stages of outcome assessment, the self-report and validation time. Our methods are applicable to trials studying biochemically verified abstinence from alcohol and other substances.

Original languageEnglish (US)
Article number170
JournalBMC Medical Research Methodology
Volume18
Issue number1
DOIs
StatePublished - Dec 18 2018

Bibliographical note

Funding Information:
This study was supported by the Biostatistics Core of the University of Minnesota Masonic Cancer Center (funded by the National Cancer Institute 5P30CA077598) to CTL and XL, by the National Heart, Lung, and Blood Institute (5R01HL094183) to JLT, JSA, and XL, and by the Clinical and Translational Science Institute of University of Minnesota (National Center for Advancing Translational Sciences UL1TR002494). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and/or in writing the manuscript.

Publisher Copyright:
© 2018 The Author(s).

Keywords

  • Abstinence outcome
  • Imputation
  • Missing data
  • Sensitivity analysis

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