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Data analysis and multiple imputation for two-level nested designs

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2018, Doctor of Philosophy, Ohio State University, Biostatistics.
This work examined methods to account for the clustering that occurs in nested clinical trial designs, which if ignored when analyzing or imputing data can result in invalid inference and misleading conclusions. We explored two distinct areas of research depending on whether the design is partially nested or fully nested. For partially nested designs, where subjects are nested within clusters in at least one study condition but subjects in another condition remain independent, we conducted a simulation study to identify the best method of analysis for binary outcomes. We compared four logistic regression models: standard logistic regression, logistic regression with generalized estimating equations, and mixed effects logistic regression with either random intercepts (LRI) or random slopes (LRC). For the logistic regression models with random effects, we additionally considered three estimation methods: penalized quasilikelihood, Laplace approximation, or adaptive Gaussian quadrature (AGQ). We showed that for partially nested designs with at least ten clusters and at least ten subjects per cluster, the LRC model estimated by AGQ produced least biased estimates of both the intervention effect and the intracluster correlation coefficient and better maintained the type I error rate. For fully nested designs, we explored methods for handling missing continuous outcomes in cluster randomized trials, the most common fully nested trial design. Random effects regression imputation has been the recommended approach to multiple imputation (MI) in cluster randomized trials, but we proposed three new semiparametric multiple imputation procedures that are more robust to misspecification of the imputation model. The new methods combined two predictive mean matching (PMM) models, one that ignores clustering and one that uses fixed effects for clusters. In the parametric setting, ignoring clustering in the imputation model results in underestimation of the MI variance, while using fixed effects for clusters results in overestimation of the MI variance. Our newly proposed PMM procedures leverage the estimated bias in the MI variance from these two imputation models in one of three ways: weighting the distance metric (PMM-dist), weighting the average of the final imputed values from the two procedures (PMM-avg), or performing a weighted draw from the final imputed values from the two procedures (PMM-draw). We conducted a simulation study to evaluate the performance of our proposed methods relative to established methods for multiple imputation of missing data. In addition to reducing the bias in the MI variance estimator relative to established methods, our newly proposed methods were more robust to model misspecification than even the random effects imputation methods. These results held whether the data were missing completely at random or at random.
Abigail Shoben, PhD (Advisor)
Rebecca Andridge, PhD (Advisor)
Erinn Hade, PhD (Committee Member)
Michael Pennell, PhD (Committee Member)
152 p.

Recommended Citations

Citations

  • Bailey, B. E. (2018). Data analysis and multiple imputation for two-level nested designs [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531822703002162

    APA Style (7th edition)

  • Bailey, Brittney. Data analysis and multiple imputation for two-level nested designs. 2018. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1531822703002162.

    MLA Style (8th edition)

  • Bailey, Brittney. "Data analysis and multiple imputation for two-level nested designs." Doctoral dissertation, Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531822703002162

    Chicago Manual of Style (17th edition)