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Comparing the Statistical Power of Analysis of Covariance after Multiple Imputation and the Mixed Model in Testing the Treatment Effect for Pre-post Studies with Loss to Follow-up

Xi, Wenna
http://orcid.org/0000-0002-5427-5689
http://rave.ohiolink.edu/etdc/view?acc_num=osu1403557167

Abstract Details

2014, Master of Science, Ohio State University, Biostatistics.
Pre-post studies, where outcomes are measured both before and after an intervention, are common in biomedical research. When the outcomes at both pre- and post-test are completely observed, previous studies have shown that analysis of covariance (ANCOVA) is more powerful than the change score analysis in testing the treatment effect and therefore is usually recommended in analyzing pre-post studies. However, methods for analyzing pre-post studies with missing outcome values have not been compared. The goal of this study was to compare the power of two analysis methods in testing for a treatment effect when post-test values are missing: ANCOVA after multiple imputation (MI) and the mixed model. To do so, we analyzed data from a real study, the BePHIT study, and performed simulation studies. Four analysis methods were used to analyze the BePHIT and simulated data: ANCOVA after MI, ANCOVA using only complete cases (CC), the mixed model using all-available data, and the mixed model using complete cases. Simulation studies were conducted under various sample sizes, missingness rates, and missingness scenarios. In the analysis of the BePHIT data, ANCOVA after MI produced the smallest p-value for the test of a treatment effect. However, in the simulation studies, CC ANCOVA was generally the most powerful method. The simulation studies also showed that the power of ANCOVA after MI dropped the fastest when the percentage of missingness increased and, for most scenarios, was the least powerful method when 50% of the post-test outcomes were missing.
Michael Pennell (Advisor)
Rebecca Andridge (Committee Member)
88 p.
Biostatistics
ANCOVA; multiple imputation; mixed model; statistical power; simulation; pre-post studies; loss to follow-up

Recommended Citations

Citations

  • Xi, W. (2014). Comparing the Statistical Power of Analysis of Covariance after Multiple Imputation and the Mixed Model in Testing the Treatment Effect for Pre-post Studies with Loss to Follow-up [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1403557167

    APA Style (7th edition)

  • Xi, Wenna. Comparing the Statistical Power of Analysis of Covariance after Multiple Imputation and the Mixed Model in Testing the Treatment Effect for Pre-post Studies with Loss to Follow-up. 2014. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1403557167.

    MLA Style (8th edition)

  • Xi, Wenna. "Comparing the Statistical Power of Analysis of Covariance after Multiple Imputation and the Mixed Model in Testing the Treatment Effect for Pre-post Studies with Loss to Follow-up." Master's thesis, Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1403557167

    Chicago Manual of Style (17th edition)

osu1403557167
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© 2014, some rights reserved.Creative Commons License Comparing the Statistical Power of Analysis of Covariance after Multiple Imputation and the Mixed Model in Testing the Treatment Effect for Pre-post Studies with Loss to Follow-up by Wenna Xi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by The Ohio State University and OhioLINK.