Statistical Inference For Minimum Inhibitory Concentration (MIC) Data
The Minimum Inhibitory Concentration (MIC) is the smallest concentration of an anti-microbial agent that inhibits the growth of bacteria. These values are obtained in a highly mechanized fashion, but this procedure only provides interval censored readings, i.e., the MIC is between two concentrations. It is often of interest to use data collected from complex experiments to see how the mean MIC is affected by different factors.
Because MIC value is interval censored, ordinary least squares cannot be used. For models containing only fixed effects, maximum likelihood estimates(MLE) can be obtained using standard software packages. For models containing random effects, MLE methods are infeasible and Bayesian approaches are required. Estimates from the two types of analysis methods are discussed and compared. Model building, selection and diagnostic procedures are presented for selecting the appropriate model. In cases where several models seem to fit the data equally well, model averaging is also performed to get model averaged estimates. Four real data sets are analyzed using the methodology we developed. A simulation study is also performed to investigate the approximate performance of the algorithm used in analysing various model types where the true parameter values are assumed to be unknown.
This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Huanhuan Wu (email@example.com) or her supervisor Carl Schwarz (firstname.lastname@example.org), Department of Statistics and Actuarial Science.