Impact of Sample Size and Class Imbalance on Performance Metrics of Zero-Inflated Count Models: a Simulation Study Applied to Chagas Disease Data

Count models are used when the response variable represents the number of specific successes. Depending on the application area, this count may have an excess of zeros, which requires a model that addresses this detail. Zero-inflated models are ideal for such situations. This study analyzes data related to Chagas disease to address both statistical and biological questions. To achieve this, a simulation study is conducted to determine whether variations in sample size affect estimates and if the chosen sampling method influences the performance of metrics used to calculate the probability of cell infection. It is found that balancing the sample size across concentrations using stratified sampling yields the best metric performance values. Additionally, it is concluded that increasing the concentration dose helps to reduce the number of parasites in the cells.