Bayesian optimal design provides an effective alternative to capture parameters uncertainty, which has been widely employed in SSADT design by optimizing specified utility objective.
In related research contributions, intervals have been proposed as a means to capture parameter uncertainties.
To capture parameter uncertainty, input variables in our model are probabilistic values.
To capture parameter uncertainty, input variables were modelled as probabilistic values.
The variation in effects, in terms of both reduced fracture risk and utilities, as well as direct health care costs, was included in the probabilistic sensitivity analysis, which was done using statistical distributions to capture parameter uncertainty.
Another alternative, used by one of the authors, is to note than many elements of the computer code required for an analysis are common to most models – capturing parameter estimates, −2×log-likelihood, the eigenvalues, calculating the goodness-of-fit – and having written code for these a variety of models can be investigated with minimal additional effort.
In fact, the rate restrictions are captured by parameters in the model, which allows timed simulations to be performed with varying rate restrictions.
Each dataset is modelled using a Dirichlet-multinomial allocation (DMA) mixture model, with dependencies between these models captured through parameters that describe the agreement among the datasets.
Table 3 indicates that when the simulations modeled a system with gain control, the fitting procedure accurately captured the parameters of the model.
Figure 3 Clean data capturing parameters.
