Because of the possibility of analysing sparse and unbalanced datasets, thereby minimising the burden for each child, population PK PD modelling and simulation using non-linear mixed effect modelling has become the preferred tool to develop effective and safe dosing regimens for children.
We also investigated the possibility of using a sparse null space matrix, based on the hypothesis that faster computation can be achieved by minimizing the combinatorial possibilities between the positive and negative elements contained in NS.
However, the possibility of applying greedy solvers, an important category of sparse recovery algorithms, to the PST problem has never been explored.
As already mentioned, it is NP-hard to calculate (delta_{2k}) for a given matrix (Ainmathbb{R}^{mtimes n}) and also to calculate these p. On the other hand, the possibility of recovery of every k-sparse vector by (l_{0} -minimization is just a necessary condition for the existence of such (delta_{2k}), and therefore the results based on (delta_{2k}) lead to limitations of practical application.
In 1978, Zadeh [1] initiated the possibility theory, where the possibility measure is thought to be suitable to deal with the uncertainty, especially under the circumstance of sparse data.
The widespread use of sparse PK sampling and population PK analyses in confirmatory clinical trials have opened the possibility of providing individual steady-state estimates of drug exposure in terms of AUC values, which are suitable for E-R analyses.
It is also possible that the effect of antiangiogenic treatment may vary within tumors, although studies investigating this possibility are sparse.
In this section, we explore the possibility of formulating and solving PST as a sparse regression problem.
Sparse data precluded the possibility of quantitatively modelling the relationships between potential explanatory variables and maternal mortality in Malawi.
Since outcome, i.e., deaths from breast cancer are sparse, there is a possibility of type II error and true associations could have been missed.
