All ETDs from UAB

Advisory Committee Chair

Lloyd J Edwards

Advisory Committee Members

Inmaculada Aban

Timothy M Beasley

Ikjae Lee

Nengjun Yi

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) School of Public Health


The linear mixed model has become a popular technique for the analysis of longitudinal data, but Wald test statistics of fixed effects for these models frequently lack well defined distributions. A common approach to this problem uses the Kenward-Roger adjustment, which attempts to approximate the distribution of the Wald statistic by matching its moments obtained via Taylor expansion to those of an F distribution. However, this approach only matches moments obtained under the null hypothesis of no effect and cannot currently be used to approximate the distribution of the test statistic under some alternative hypothesis. This limitation prevents a straightforward approach to calculating power for the Kenward-Roger adjusted Wald statistic. In chapter 2, we introduce a novel power calculation that extends the original methodology of Kenward and Roger to obtain an approximate noncentral distribution of this adjusted Wald statistic from which power for tests of linear trend can then be calculated. This method is then extended to calculate expected power for designs with anticipated rates of missing follow-up data in Chapter 3, and finally to the calculation of sample size for such designs in Chapter 4. A variety of other techniques are also examined and compared to this method, with the newly developed method consistently outperforming other approaches in the calculation of both power and sample size.

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Public Health Commons



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