What to Do When Your Data Is a Curve (2019-US-45MP-213)
Aug 28, 2019 5:24 AM
B-Spline Model Summaries (Degree=3, Knots=20).jmp
Combined Table (Dextran and Sucrose Excipients).jmp
Parameter Estimates for Non-Linear Model Fits with RH and Temp Rowwise Table.jmp
Sam Gardner, Principal Research Scientist, Elanco Animal Health
In many situations in pharmaceutical product development, the most relevant data is a value that is a function of time (a curve). Traditional approaches to handling curves often ignore the full time dependency of the result and only focus on one aspect of the curve. This often results in a loss of information.
Three analysis approaches that utilize the entire curve will be discussed: 1) When data is aligned in time, multivariate methods can be used to characterize the data; 2) When data can be described by a parameterized model, fitting that model to each curve results in model parameters that become the new data; and, 3) When the data in the curve is not time aligned, or if it is too complex to describe with a parameterized model, then the Functional Data Explorer can be used to fit a smoothing model to each curve, and again the parameters of that smooth curve fit become the new data. DOE can be combined with these methods to understand how the curves depend on the DOE factors.This talk will show how to use JMP to apply each of these approaches, combined with DOE, using real examples from pharmaceutical product development.