Sample size determination for logistic regression on a logit-normal distribution.
Although the sample size for simple logistic regression can be readily determined using currently available methods, the sample size calculation for multiple logistic regression requires some additional information, such as the coefficient of determination ([Formula: see text]) of a covariate of interest with other covariates, which is often unavailable in practice. The response variable of logistic regression follows a logit-normal distribution which can be generated from a logistic transformation of a normal distribution. Using this property of logistic regression, we propose new methods of determining the sample size for simple and multiple logistic regressions using a normal transformation of outcome measures. Simulation studies and a motivating example show several advantages of the proposed methods over the existing methods: (i) no need for [Formula: see text] for multiple logistic regression, (ii) available interim or group-sequential designs, and (iii) much smaller required sample size.
1 Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.|2 Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
Seongho
S
Kim
2 Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
Elisabeth
E
Heath
1 Biostatistics Core, Karmanos Cancer Institute, Wayne State University, Detroit, MI 48201, USA.|2 Department of Oncology, School of Medicine, Wayne State University, Detroit, MI 48201, USA.
Lance
L
Heilbrun
D001741:Black or African American; D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D001706:Biopsy; D018450:Disease Progression; D005060:Europe / Q000208:ethnology; D006801:Humans; D016015:Logistic Models; D008297:Male; D016011:Normal Distribution; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000378:metabolism; D018401:Sample Size
D015415:Biomarkers
Logistic regression; logit-normal distribution; power calculation; sample size determination; transformation