Knowledge creation

Although both of the previous perspectives may include certain innovative elements, neither of them focuses on the creation and development of knowledge. Therefore, Hakkarainen et al. (2004) introduce the perspective of knowledge creation. Its aim is both to create new knowledge and to develop corresponding social practices. In this process, the existing knowledge is essentially enriched or shaped (as summarised in Table 17.2). 

The knowledge-creation paradigm requires that there are deliberate efforts to extend current... 

Learning = regulation x cooperation Knowledge = information x processing Knowledge creation = knowledge acquisition x participation Pyburn et al. (see Chapter 14) Sriskandarajah et al. (see Chapter 16) Seppanen and Francis (see Chapter 17)... 

Figure C12. Organisational Knowledge Creation Model (Nonaka-Takeuchi)...

Prior to advancing to Phase 2b, all the knowledge hidden in the Phase 1 and Phase 2a data ought to be discovered. Then clinical trial simulation (knowledge creation) should be applied to construct Phase 2b. 

E. I. Ette, H. M. Chu, and C. J. Godfrey, Data supplementation a pharmacokinetic pharmacodynamic knowledge creation approach for characterizing an unexplored region of the response surface. Pharm Res 22 523-531 (2005). 

Pharmacokinetic/Pharmacodynamic Knowledge Creation Toward Characterizing an Unexplored Region of the Response Surface... 

Pharmacokinetic/pharmacodynamic (PK/PD) knowledge creation is the process of building on current understanding of data that is already acquired by generating more data (information) that can be translated into knowledge. It entails the use of (valid) models to synthesize data, estimate inestimable uncertainty, or supplement data for further knowledge acquisition (1, 2). 

Knowledge creation is an emerging, interdisciplinary research field that lives at the intersection of computer science (database, artificial intelligence, graphics, and visualization), statistics, and an application domain such as clinical pharmacology in general, and pharmacometrics in particular. 

In the sections that follow types of PK/PD knowledge creation, general steps in the knowledge creation process, data supplementation and the motivation for it, data supplementation procedure, nonparametric approximate Bayesian data supplementation method, structure-based multiple supplementation with a motivating example, and implications of the use of multiple data supplementation for the characterization of an unexplored region of the response are described. The emphasis in this chapter is on the use of data supplementation to characterize unexplored region of the response surface. A discussion on data synthesis, the qualitative characterization of the response surface, and the estimation of inestimable uncertainty has been elegantly presented by Williams et al. (1) and Ette and Onyiah (5). Therefore, these approaches are discussed in brief in the sections that follow. 

PK/PD knowledge creation from a clinical trial data set is a process that can be formalized into a number of steps. In this section we provide a general framework for the steps needed to be taken in the PK/PD knowledge creation process. These steps could vary depending on the type of knowledge creation approach involved. Subsequently, data supplementation—the PK/PD knowledge creation approach of focus in this chapter—is discussed. 

Briefly, the steps in the PK/PD knowledge creation process are as follows ... 

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