Profile Likelihood Estimation of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data

• Activity Number: 564 - Analysis of Left-Censored Data (E.G., Below Detection): Real-World Problems in Need of Statisticians
• Type: Topic Contributed
• Date/Time: Wednesday, July 31, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Yanming Li* and Brenda W Gillespie and Kerby Shedden and John Gillespie
• Companies: University of Michigan and University of Michigan and University of Michigan and University of Michigan -Dearborn

Abstract

We discuss implementation of a profile likelihood method for estimating a Pearson correlation coefficient from bivariate data with censoring and/or missing values. The method is implemented in an R package “clikcorr” which calculates maximum likelihood estimates of the correlation coefficient when the data are modeled with either a Gaussian or a Student t-distribution, in the presence of left, right, or interval censored and/or missing data. The R package includes functions for estimating the correlation coefficient, calculating its 95% profile-likelihood-based confidence interval and conducting hypothesis tests of whether the true correlation coefficient equals a given value. The package also provides graphs such as scatter plots for censored and/or missing data and profile plots of the log likelihood function. The performance of “clikcorr” in a variety of circumstances is evaluated through extensive simulation studies. We illustrate the package using two dioxin exposure datasets.