Ag Decision Maker, April 2006, Vol. 10, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2007, Vol. 11, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2008, Vol. 12, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2009, Vol. 13, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2010, Vol. 14, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2011, Vol. 15, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2012, Vol. 16, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2013, Vol. 17, no. 6

A Business Newsletter for Agriculture

Ag Decision Maker, April 2014, Vol. 18, no. 6

A Business Newsletter for Agriculture

Constraining ACT-R models of decision strategies: An experimental paradigm

It has been repeatedly debated which strategies people rely on in inference. These debates have been difficult to resolve, partially because hypotheses about the decision processes assumed by these strategies have typically been formulated qualitatively, making it hard to test precise quantitative predictions about response times and other behavioral data. One way to increase the precision of strategies is to implement them in cognitive architectures such as ACT-R. Often, however, a given strategy can be implemented in several ways, with each implementation yielding different behavioral predictions. We present and report a study with an experimental paradigm that can help to identify the correct implementations of classic compensatory and non-compensatory strategies such as the take-the-best and tallying heuristics, and the weighted-linear model.

Ag Decision Maker, August 2002, Vol. 6, no. 10

A Business Newsletter for Agriculture

Dosage optimization of treatments using population pharmacokinetic modeling and simulation.

Pharmacokinetic variability in drug levels represent for some drugs a major determinant of treatment success, since sub-therapeutic concentrations might lead to toxic reactions, treatment discontinuation or inefficacy. This is true for most antiretroviral drugs, which exhibit high inter-patient variability in their pharmacokinetics that has been partially explained by some genetic and non-genetic factors. The population pharmacokinetic approach represents a very useful tool for the description of the dose-concentration relationship, the quantification of variability in the target population of patients and the identification of influencing factors. It can thus be used to make predictions and dosage adjustment optimization based on Bayesian therapeutic drug monitoring (TDM). This approach has been used to characterize the pharmacokinetics of nevirapine (NVP) in 137 HIV-positive patients followed within the frame of a TDM program. Among tested covariates, body weight, co-administration of a cytochrome (CYP) 3A4 inducer or boosted atazanavir as well as elevated aspartate transaminases showed an effect on NVP elimination. In addition, genetic polymorphism in the CYP2B6 was associated with reduced NVP clearance. Altogether, these factors could explain 26% in NVP variability. Model-based simulations were used to compare the adequacy of different dosage regimens in relation to the therapeutic target associated with treatment efficacy. In conclusion, the population approach is very useful to characterize the pharmacokinetic profile of drugs in a population of interest. The quantification and the identification of the sources of variability is a rational approach to making optimal dosage decision for certain drugs administered chronically.

Ag Decision Maker, August 2003, Vol. 7, no. 10

A Business Newsletter for Agriculture

Ag Decision Maker, August 2004, Vol. 8, no. 10

A Business Newsletter for Agriculture

Ag Decision Maker, August 2005, Vol. 9, no. 9

A Business Newsletter for Agriculture