Mediation analysis in longitudinal studies in the presence of measurement error and missing data
2018-05
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Mediation analysis in longitudinal studies in the presence of measurement error and missing data
Authors
Published Date
2018-05
Publisher
Type
Thesis or Dissertation
Abstract
Mediation analysis hypothesizes that an exposure causes a mediator and in turn the mediator causes the outcome, so mediation is inherently longitudinal. Unfortunately, potential mediators may be measured with error and regression estimators obtained by ignoring measurement error can be severely biased. This can induce bias in the estimation of causal direct and indirect effects. In Chapter 2, using regression calibration, we show how to adjust for measurement error in longitudinal studies with repeated measurements of the mediator, and evaluate the effect of ignoring measurement error on direct and indirect effects. Rather than assuming normality for the random effects in the linear mixed effects calibration model, we correct for measurement error in the mediator allowing flexibility in the distribution of subject-specific random effects. On the other hand, longitudinal studies face challenges of missing data resulting from loss to follow-up, death, or withdrawal. In mediation analysis, multiple imputation has been shown to perform well for data missing completely at random (MCAR) and missing at random (MAR) in cross-sectional studies, but it is unclear how it performs in longitudinal studies under misspecification of the imputation model, specifically, where the misspecification ignores clustering by subject. In Chapter 3, we examine the impact of ignoring clustering on mediated effect estimates under MCAR and MAR mechanisms with varying degrees of missingness. In Chapter 4, using data from a randomized controlled trial, we examine the mediation effects on child neurodevelopment of intermittent preventive malaria treatment in pregnant women. Chapter 5 concludes and discusses future work.
Description
University of Minnesota Ph.D. dissertation. May 2018. Major: Biostatistics. Advisor: David Vock. 1 computer file (PDF); v, 107 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Suggested citation
Ssenkusu, John Mbaziira. (2018). Mediation analysis in longitudinal studies in the presence of measurement error and missing data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206371.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.