The Limit of Detection (LoD) is defined as the lowest concentration or amount of material, target or analyte that is consistently detectable (for PCR quantitative studies, in at least 95% of the samples tested)1. In practice, the estimation of the LoD uses a parametric curve fit to a set of panel member (PM1, PM2, PM3, etc.) data where the responses are binary. Typically, the parametric curve fit to the percent detection levels takes on the form of a probit or logistic distribution. The LoD_Est SAS Macro (Canchola & Hemyari, SAS Global Forum 2016), using the SAS PROBIT procedure as the main engine, is used to fit such a parametric curves. The rarely used but preferred method uses the method of maximum likelihood (ML) to estimate the LoD assuming one detectable copy of template. We introduce the LOD_MLE SAS macro that maximizes the log likelihood function and returns the ML estimate (MLE) of the LoD along with its 95% confidence interval (CI). In addition, the macro returns the percent detection table with associated 95% exact (Clopper-Pearson) confidence intervals for the hit rates at each level.