title: Statistical inference for serial dilution assay data.
abstract: Serial dilution assays are widely employed for estimating substance concentrations and minimum inhibitory concentrations. The Poisson-Bernoulli model for such assays is appropriate for count data but not for continuous measurements that are encountered in applications involving substance concentrations. This paper presents practical inference methods based on a log-normal model and illustrates these methods using a case application involving bacterial toxins.
authors: Channing Laboratory, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts 02115, USA. meiling@channing.harvard.edu
authors: M L
authors: ML
authors: Lee
authors: G A
authors: GA
authors: Whitmore
mesh_terms: D000465:Algorithms; D001427:Bacterial Toxins / Q000096:biosynthesis; D001699:Biometry; D016360:Clostridioides difficile / Q000378:metabolism / Q000472:pathogenicity; D003627:Data Interpretation, Statistical; D003967:Diarrhea / Q000209:etiology; D006801:Humans; D007201:Indicator Dilution Techniques / Q000706:statistics & numerical data*; D008954:Models, Biological; D015233:Models, Statistical
chemical_list: D001427:Bacterial Toxins