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Author ORCID Identifier
https://orcid.org/0000-0001-9275-6750
AccessType
Open Access Dissertation
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Public Health
Year Degree Awarded
2022
Month Degree Awarded
February
First Advisor
Ken Kleinman
Second Advisor
Laura Balzer
Third Advisor
Ted Westling
Subject Categories
Biostatistics | Longitudinal Data Analysis and Time Series | Other Statistics and Probability
Abstract
Difference-in-difference cluster randomized trials (CRTs) use baseline and post-test measurements. Standard power equations for these trials assume no loss to follow-up. We present a general equation for calculating treatment effect variance in difference-in-difference CRTs, with special cases assuming loss to follow-up with replacement of lost participants and loss to follow-up with no replacement but retaining the baseline measurements of all participants. Multiple-period CRTs can represent time as continuous using random coefficients (RC) or categorical using repeated measures ANOVA (RM-ANOVA) analytic models. Previous work recommends the use of RC over RM-ANOVA for CRTs with more than two periods because RC exhibited nominal Type I error rates for both time parameterizations while RM-ANOVA exhibited inflated Type I error rates when applied to continuous-time data. In this simulation study, we expand on this work by investigating alternative covariance and random effects structure in analytic models, as well as applying models to cohort data. Results indicate the RC analytic model maintains the nominal Type I error rate in all evaluated settings, while RM-ANOVA with an unstructured covariance does not avoid Type I error rate inflation. Analytic models omitting time-varying group random effects when such variation exists in the data are prone to substantial Type I error inflation unless the residual error variance is high relative to the time by group variance. Publication in journals is ideally due to merit, but an author’s affiliation with a prestigious institution can also be a factor. Using Web of Science article citation information for articles in applied statistics journals from 2006 to 2015, we assess the association between institution affiliation and journal publication using causal inference techniques. Results of a cross-sectional analysis indicate that top journal publication probability for authors at top institutions is 9\% greater, on average, than if, counterfactually, that same author was at a non-top institution. Results are similar stratifying by most categories of perceived race and gender, but authors perceived to be Black or Hispanic showed no benefit of top institution affiliation.
DOI
https://doi.org/10.7275/27141944
Recommended Citation
Moyer, Jonathan, "Impact of Loss to Follow-up and Time Parameterization in Multiple-Period Cluster Randomized Trials and Assessing the Association Between Institution Affiliation and Journal Publication" (2022). Doctoral Dissertations. 2430.
https://doi.org/10.7275/27141944
https://scholarworks.umass.edu/dissertations_2/2430
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Included in
Biostatistics Commons, Longitudinal Data Analysis and Time Series Commons, Other Statistics and Probability Commons