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Author ORCID Identifier



Open Access Dissertation

Document Type


Degree Name

Doctor of Philosophy (PhD)

Degree Program

Public Health

Year Degree Awarded


Month Degree Awarded


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


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.


Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Available for download on Wednesday, February 01, 2023