Predicting oral anticoagulant response using a pharmacodynamic model

We developed a pharmacokinetic and pharmacodynamic model of warfarin absorption, metabolism, and anticoagulant action appropriate for guiding anticoagulant therapy. The model requires only two independently adjustable parameters to describe warfarin's effect on individual patients. For any given individual, these parameters are rapidly and inexpensively identified using a computer program based on the model. Test data were generated by superimposing Gaussian noise on dose-response curves calculated with the model. Then the computer program was applied to the test data. Future prothrombin complex activities (PCA's) and maintenance doses were predicted accurately early in the course of drug administration. In addition, the program accurately predicted PCA response in two groups of normal volunteers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44006/1/10439_2006_Article_BF02363455.pd

Random effects diagonal metric multidimensional scaling models

By assuming a distribution for the subject weights in a diagonal metric (INDSCAL) multidimensional scaling model, the subject weights become random effects. Including random effects in multidimensional scaling models offers several advantages over traditional diagonal metric models such as those fitted by the INDSCAL, ALSCAL, and other multidimensional scaling programs. Unlike traditional models, the number of parameters does not increase with the number of subjects, and, because the distribution of the subject weights is modeled, the construction of linear models of the subject weights and the testing of those models is immediate. Here we define a random effects diagonal metric multidimensional scaling model, give computational algorithms, describe our experiences with these algorithms, and provide an example illustrating the use of the model and algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45758/1/11336_2005_Article_BF02295730.pd

Statisticians and pharmacokineticists: what they can still learn from each other

Examples are given of how the practice of statistics could be improved if statisticians showed a greater awareness of pharmacokinetic and pharmacodynamic modeling. Some examples are also given where a wider appreciation of statistical theory would improve current approaches to pharmacometrics. Areas in which the two disciplines are in agreement but have failed to have as much influence on others in drug development as they ought are also considered. It is concluded that there would be much benefit in increasing collaboration between these disciplines