Journal of Data Science

Abstract: HIV (Human Immunodeficiency Virus) researchers are often con cerned with the correlation between HIV viral load measurements and CD4+ lymphocyte counts. Due to the lower limits of detection (LOD) of the avail able assays, HIV viral load measurements are subject to left-censoring. Mo tivated by these considerations, the maximum likelihood (ML) method under normality assumptions was recently proposed for estimating the correlation between two continuous variables that are subject to left-censoring. In this paper, we propose a generalized estimating equations (GEE) approach as an alternative to estimate such a correlation coefficient. We investigate the robustness to the normality assumption of the ML and the GEE approaches via simulations. An actual HIV data example is used for illustration.

Abstract: In many clinical trials, information is collected on both the frequency of event occurrence and the severity of each event. For example, in evaluating a new anti-epileptic medication both the total number of seizures a patient has during the study period as well as the severity (e.g., mild, severe) of each seizure could be measured. In order to arrive at a full picture of drug or treatment performance, one needs to jointly model the number of events and their correlated ordinal severity measures. A separate analysis is not recommended as it is inefficient and can lead to what we define as “zero length bias” in estimates of treatment effect on severity. This paper proposes a general, likelihood based, marginal regression model for jointly modeling the number of events and their correlated ordinal severity measures. We describe parameter estimation issues and derive the Fisher information matrix for the joint model in order to obtain the asymptotic covariance matrix of the parameter estimates. A limited simulation study is conducted to examine the asymptotic properties of the maximum likelihood estimators. Using this joint model, we propose tests that incorporate information from both the number of events and their correlated ordinal severity measures. The methodology is illustrated with two examples from clinical trials: the first concerning a new drug treatment for epilepsy; the second evaluating the effect of a cholesterol lowering medication on coronary artery disease.

Abstract: Existing indices of observer agreement for continuous data, such as the intraclass correlation coefficient or the concordance correlation coefficient, measure the total observer-related variability, which includes the variabilities between and within observers. This work introduces a new index that measures the interobserver variability, which is defined in terms of the distances among the ‘true values’ assigned by different observers on the same subject. The new coefficient of interobserver variability (CIV ) is defined as the ratio of the interobserver and the total observer variability. We show how to estimate the CIV and how to use bootstrap and ANOVAbased methods for inference. We also develop a coefficient of excess observer variability, which compares the total observer variability to the expected total observer variability when there are no differences among the observers. This coefficient is a simple function of the CIV . In addition, we show how the value of the CIV , estimated from an agreement study, can be used in the design of measurements studies. We illustrate the new concepts and methods by two examples, where (1) two radiologists used calcium scores to evaluate the severity of coronary artery arteriosclerosis, and (2) two methods were used to measure knee joint angle.