Abstract
In prevention trials, outcomes of interest frequently include data that are best quantified as proportion scores. In some cases, however, proportion scores may violate the statistical assumptions underlying common analytic methods. In this paper, we provide guidelines for analyzing frequency and proportion data as primary outcomes. We describe standard methods including generalized linear regression models to compare mean proportion scores and examine tools for testing normality and other assumptions for each model. Recommendations are made for instances when the assumptions are not met, including transformations for proportion scores that are non-normal. We also discuss more sophisticated analytical tools to model change in proportion scores over time. The guidelines provide ready-to-use analytical strategies for frequency and proportion data that are commonly encountered in prevention science.
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This research was supported by a T32 Fellowship from the National Institute of Mental Health (NIMH) to the third author (T32MH018951) and by a grant from the NIMH to the fourth author (MH093508). The authors would also like to thank Dr. David Kolko for access to the SKIP2 (MH063272) dataset and Charles Bennett for the assistance with the manuscript preparation.
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This research was supported by grants from the National Institute of Mental Health (MH093508; MH063272; MH018951).
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The authors declare that they have no conflict of interest.
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All data collection procedures were carried out with approval from, and in compliance with, the IRB at the University of Pittsburgh.
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Informed consent was obtained from all individual participants included in the study.
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Chen, K., Cheng, Y., Berkout, O. et al. Analyzing Proportion Scores as Outcomes for Prevention Trials: a Statistical Primer. Prev Sci 18, 312–321 (2017). https://doi.org/10.1007/s11121-016-0643-6
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DOI: https://doi.org/10.1007/s11121-016-0643-6