title: The effect of simple imputation on inferences about population means when data are missing in biomedical research due to detection limits.
abstract: The sample geometric mean has been widely used in biomedical and psychosocial research to estimate and compare population geometric means. However, due to the detection limit of measurement instruments, the actual value of the measurement is not always observable. A common practice to deal with this problem is to replace missing values by small positive constants and make inferences based on the imputed data. However, no work has been carried out to study the effect of this naïve imputation method on inference. In this report, we show that this simple imputation method may dramatically change the reported outcomes of a study and, thus, make the results uninterpretable, even if the detection limit is very small.
authors.affiliation: Departments of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
authors.forename: Hongyue
authors.initials: H
authors.lastname: Wang
authors.affiliation: Departments of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
authors.forename: Guanqing
authors.initials: G
authors.lastname: Chen
authors.affiliation: Departments of Biostatistics and Computational Biology, University of Rochester, Rochester, NY.
authors.forename: Xiang
authors.initials: X
authors.lastname: Lu
authors.affiliation: Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN.
authors.forename: Hui
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Departments of Biostatistics and Computational Biology, University of Rochester, Rochester, NY; Department of Anesthesiology, University of Rochester, Rochester, NY.
authors.forename: Changyong
authors.initials: C
authors.lastname: Feng
keywords: population geometric mean; sample geometric mean; two-sample test