title: Log-transformation and its implications for data analysis.
abstract: The log-transformation is widely used in biomedical and psychosocial research to deal with skewed data. This paper highlights serious problems in this classic approach for dealing with skewed data. Despite the common belief that the log transformation can decrease the variability of data and make data conform more closely to the normal distribution, this is usually not the case. Moreover, the results of standard statistical tests performed on log-transformed data are often not relevant for the original, non-transformed data.We demonstrate these problems by presenting examples that use simulated data. We conclude that if used at all, data transformations must be applied very cautiously. We recommend that in most circumstances researchers abandon these traditional methods of dealing with skewed data and, instead, use newer analytic methods that are not dependent on the distribution the data, such as generalized estimating equations (GEE).
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Changyong
authors.initials: C
authors.lastname: Feng
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Hongyue
authors.initials: H
authors.lastname: Wang
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Naiji
authors.initials: N
authors.lastname: Lu
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Tian
authors.initials: T
authors.lastname: Chen
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Hua
authors.initials: H
authors.lastname: He
authors.affiliation: Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Lu
authors.affiliation: Department of Biostatistics and Computational Biology,University of Rochester, Rochester, NY, USA.
authors.forename: Xin M
authors.initials: XM
authors.lastname: Tu
keywords: hypothesis testing; lon-normal distribution; normal distribution; outliners; skewness
references.citation: Stat Med. 2013 Jan 30;32(2):230-9
references.pmid: 22806695