Impact of Using Double Positive Samples in Deming Regression

Abstract

In the method comparison approach, two measurement errors are observed. The classical regression approach (linear regression) method cannot be used for the analysis because the method may yield biased and inefficient estimates. In view of that, the Deming regression is preferred over the classical regression. The focus of this work is to assess the impact of censored data on the traditional regression, which deletes the censored observations compared to an adapted version of the Deming regression that takes into account the censored data. The study was done based on simulation studies with NLMIXED being used as a tool to analyse the data. Eight different simulation studies were run in this study. Each of the simulation is made up of 100 datasets with 300 observations. Simulation studies suggest that the traditional Deming regression which deletes censored observations gives biased estimates and a low coverage, whereas the adapted Deming regression that takes censoring into account gives estimates that are close to the true value making them unbiased and gives a high coverage. When the analytical error ratio is misspecified, the estimates are as well not reliable and biased.