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Comparison of models for analyzing two-group, cross-sectional data with a Gaussian outcome subject to a detection limit
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Published Date:
May 05 2014
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Source:Stat Methods Med Res. 25(6):2733-2749
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Language:English
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[PDF-974.86 KB]
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Alternative Title:Stat Methods Med Res
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Description:A potential difficulty in the analysis of biomarker data occurs when data are subject to a detection limit. This detection limit is often defined as the point at which the true values cannot be measured reliably. Multiple, regression-type models designed to analyze such data exist. Studies have compared the bias among such models, but few have compared their statistical power. This simulation study provides a comparison of approaches for analyzing two-group, cross-sectional data with a Gaussian-distributed outcome by exploring statistical power and effect size confidence interval coverage of four models able to be implemented in standard software. We found using a Tobit model fit by maximum likelihood provides the best power and coverage. An example using human immunodeficiency virus type 1 ribonucleic acid data is used to illustrate the inferential differences in these models.
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Pubmed ID:24803511
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Pubmed Central ID:PMC6880228
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