Maximum likelihood estimation of correction for dilution bias in simple linear regression using replicates from subjects with extreme first measurements.
The least-squares estimator of the slope in a simple linear regression model is biased towards zero when the predictor is measured with random error. A corrected slope may be estimated by adding data from a reliability study, which comprises a subset of subjects from the main study. The precision of this corrected slope depends on the design of the reliability study and estimator choice. Previous work has assumed that the reliability study constitutes a random sample from the main study. A more efficient design is to use subjects with extreme values on their first measurement. Previously, we published a variance formula for the corrected slope, when the correction factor is the slope in the regression of the second measurement on the first. In this paper we show that both designs improve by maximum likelihood estimation (MLE). The precision gain is explained by the inclusion of data from all subjects for estimation of the predictor's variance and by the use of the second measurement for estimation of the covariance between response and predictor. The gain of MLE enhances with stronger true relationship between response and predictor and with lower precision in the predictor measurements. We present a real data example on the relationship between fasting insulin, a surrogate marker, and true insulin sensitivity measured by a gold-standard euglycaemic insulin clamp, and simulations, where the behavior of profile-likelihood-based confidence intervals is examined. MLE was shown to be a robust estimator for non-normal distributions and efficient for small sample situations.
Berglund
Lars
L
Department of Public Health/Geriatrics, Uppsala University, and Uppsala Clinical Research Center, University Hospital, Uppsala, Sweden. lars.berglund@ucr.uu.se
Garmo
Hans
H
Lindbäck
Johan
J
Svärdsudd
Kurt
K
Zethelius
Björn
B
D000704:Analysis of Variance; D001786:Blood Glucose / Q000032:analysis; D002986:Clinical Trials as Topic / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007333:Insulin Resistance; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016014:Linear Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D015983:Selection Bias
D001786:Blood Glucose