Using generalized regression to analyze designed experiments with detection limited responses


Fangyi Luo
Group Scientist

Christopher Gotwalt
Chief Data Scientist

Beatrice Blum
Senior Scientist

Most measurement systems have detection limits above or below which one cannot accurately measure the quantity of interest. Although detection-limited responses are common in many application areas, such as the pharma, chemical, and consumer products industries, they are often ignored in the analysis. Ignoring detection limits biases in the results and even drastically lowers the power to detect active effects. Fortunately, the Custom Designer and Generalized Regression in JMP® make incorporating detection limits easy and automatic. In this presentation, we will use simulated versions of real designed experiments to show how to get the analysis right in JMP® Pro 17 and the pitfalls that will occur if detection limits are ignored in the analysis. We will also show how simple graphical tools can identify parts of the design region that could be problematic or even make it impossible to estimate certain model terms or interactions. Our examples will include an experiment designed to maximize the yield of a chemical product where the response is a reduction in the number of microorganisms in microbial susceptibility testing of consumer cleaning products.