Cancer relapse in myelodysplastic syndrome (MDS) patients receiving bone marrow transplantation can be predicted with measurable residual disease (MRD), by droplet digital polymerase chain reaction (ddPCR). ddPCR quantifies genomic DNA molecules in an absolute manner using end-point amplification. This work aims to demonstrate that ddPCR assay evaluation can be conducted with fewer healthy donor controls compared to methods for relative quantification. The hypothesis is further studied by applying the total error computed in the ddPCR system as a threshold for background noise in personalized assays. Ten assays for detecting MRD markers were evaluated in an optimized PCR-plate setup for accuracy and reproducibility of background in negative controls. Additionally, data analysis of negative controls collected from patient tests complied to the empirical limit of blank based on false - positive counts, in each assay. The findings indicate that the optimized setup accurately determines background noise, and empirical cutoffs for individualized assays are reliable for performance evaluations. This study supports ddPCR integration into clinical settings for personalized mutation analyses in MDS, providing an optimized setup and alternative metrics of evaluating assay performance in respect to the absolute quantification methodology.