Analyzing Pharmacodynamic Count Data That Rapidly Decrease to Zero.

ABSTRACT: We present a framework for maximum likelihood analysis on count observations that begin high and quickly drop to zero, for example, from hollow fiber drug comparison studies. This simulation study focuses on treating observed counts as Poisson or normally distributed for the purpose of estimating infection rebound after effective treatment. CFU profiles were simulated from inoculation to 96 h post-treatment. The PK-PD link was an Emax inhibitory model. Random parameters were pathogen growth and natural decay rates, drug concentration for half-maximal effect, and drug pathogen kill rate. Other parameters, including PK, were fixed. Parameters were adjusted to attain 67% efficacy at 24 h. Random parameter values were optimized for profiles observed at 24, 48, 72, and 96 h assuming each of fou

SUBMITTER: Yamada WM 

PROVIDER: S-EPMC12823320 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature