Power difference in a χ2 test vs generalized linear mixed model in the presence of missing data – a simulation study
| dc.contributor.author | Miller, Mary L | |
| dc.contributor.author | Roe, Denise J | |
| dc.contributor.author | Hu, Chengcheng | |
| dc.contributor.author | Bell, Melanie L | |
| dc.date.accessioned | 2020-04-28T20:37:50Z | |
| dc.date.available | 2020-04-28T20:37:50Z | |
| dc.date.issued | 2020-03-02 | |
| dc.identifier.citation | Miller, M.L., Roe, D.J., Hu, C. et al. Power difference in a χ2 test vs generalized linear mixed model in the presence of missing data – a simulation study. BMC Med Res Methodol 20, 50 (2020). https://doi.org/10.1186/s12874-020-00936-w | en_US |
| dc.identifier.issn | 1471-2288 | |
| dc.identifier.pmid | 32122312 | |
| dc.identifier.doi | 10.1186/s12874-020-00936-w | |
| dc.identifier.uri | http://hdl.handle.net/10150/641109 | |
| dc.description.abstract | Longitudinal randomized controlled trials (RCTs) often aim to test and measure the effect of treatment between arms at a single time point. A two-sample χ2 test is a common statistical approach when outcome data are binary. However, only complete outcomes are used in the analysis. Missing responses are common in longitudinal RCTs and by only analyzing complete data, power may be reduced and estimates could be biased. Generalized linear mixed models (GLMM) with a random intercept can be used to test and estimate the treatment effect, which may increase power and reduce bias. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | BMC | en_US |
| dc.rights | Copyright © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | binary data | en_US |
| dc.subject | chi-squared test | en_US |
| dc.subject | complete-case | en_US |
| dc.subject | generalized linear mixed model | en_US |
| dc.subject | longitudinal | en_US |
| dc.subject | missing data | en_US |
| dc.subject | power | en_US |
| dc.subject | Relative bias | en_US |
| dc.title | Power difference in a χ2 test vs generalized linear mixed model in the presence of missing data – a simulation study | en_US |
| dc.type | Article | en_US |
| dc.contributor.department | Univ Arizona, Dept Epidemiol & Biostat | en_US |
| dc.identifier.journal | BMC MEDICAL RESEARCH METHODOLOGY | en_US |
| dc.description.note | Open access journal | en_US |
| dc.description.collectioninformation | This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu. | en_US |
| dc.eprint.version | Final published version | en_US |
| dc.source.journaltitle | BMC medical research methodology | |
| dc.source.volume | 20 | |
| dc.source.issue | 1 | |
| dc.source.beginpage | 50 | |
| dc.source.endpage | ||
| refterms.dateFOA | 2020-04-28T20:37:52Z | |
| dc.source.country | England |
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Except where otherwise noted, this item's license is described as Copyright © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.