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Systematically Missing Subject-Level Data in Longitudinal Research Synthesis

Kline, David
http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809

Abstract Details

2015, Doctor of Philosophy, Ohio State University, Biostatistics.
When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can occur on either the observation-level (time-varying) or the subject-level (non-time-varying). Traditionally, the focus of missing data methods for longitudinal data has been on missing observation-level variables. In this dissertation, we focus on missing subject-level variables where few methods have been developed or compared. We compare two multiple imputation approaches that have been proposed for missing subject-level data in single longitudinal studies: a joint modeling approach and a sequential conditional modeling approach. Based on analytical and empirical results for the case when all variables are normally distributed, we find the joint modeling approach to be preferable to the sequential conditional approach except when the covariance structure of the repeated outcome for each individual has homogenous variance and exchangeable correlation. Specifically, the regression coefficient estimates from an analysis incorporating imputed values based on the sequential conditional method are attenuated and less efficient than those from the joint method. Based on this preference, we develop a new joint model for multiple imputation of missing subject-level variables that models subject- and observation-level variables with distributions in the exponential family. Our model is built within the generalized linear models framework and uses normally distributed latent variables to account for dependence on both the subject- and observation-levels. When compared via simulation, the performance of our model is similar to or better than existing approaches for imputing missing subject-level variables with normal, Bernoulli, Poisson, and multinomial distributions. We illustrate our method by applying it to combine two longitudinal studies on the psychological and social effects of pediatric traumatic brain injury that have systematically missing subject-level data.
Rebbeca Andridge (Advisor)
Eloise Kaizar (Advisor)
Bo Lu (Committee Member)
172 p.
Biostatistics; Statistics
multiple imputation; research synthesis; longitudinal data; missing data; individual patient data

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Citations

  • Kline, D. (2015). Systematically Missing Subject-Level Data in Longitudinal Research Synthesis [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809

    APA Style (7th edition)

  • Kline, David. Systematically Missing Subject-Level Data in Longitudinal Research Synthesis. 2015. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809.

    MLA Style (8th edition)

  • Kline, David. "Systematically Missing Subject-Level Data in Longitudinal Research Synthesis." Doctoral dissertation, Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440067809

    Chicago Manual of Style (17th edition)

osu1440067809
724
© 2015, all rights reserved.
This open access ETD is published by The Ohio State University and OhioLINK.