An exploration of time-dependent modeling techniques, a more elaborate variable selection procedure, a more sophisticated multiclass discrimination approach, and the incorporation of other types of sensors in our measuring devices for added redundancy, are also envisioned.
With a relatively limited sample size, this will be a feasible approach – and potentially more revealing – than a more omnibus variable selection process.
Another importance of this study lies in the proposition of more through variable selection method in the planning relevant scale.
However, further statistical analysis focused more on variable selection is required to identify potential risk factors.
However, good specificity, moderate prediction accuracy, comparable or higher accuracy as well as more efficient variable selection and data usage in comparison to the logit model, and positive and negative predictive values indicated that the model has potential in predicting mortality and very poor HRQoL in COPD patients.
The larger evolvability of petal size traits of plants which originated from rock faces was possibly due to more spatially variable selection (e.g. Kelly, 1992; McLeod et al., 2012) and restricted gene flow within S. sponhemica populations (Walisch et al., 2014), resulting in small-scale local adaptation (Knight and Miller, 2004; Paccard et al., 2013).
Su et al. [ 49] used a more complicated variable selection approach, but one that may lead to over-fitting and that is not practical for models with large geographic and temporal scopes such as ours, with approximately 125,000-250,000 observandons and run times for one model fit of between 24 and 96 hours.
These results indicate that when BP is used, GE with TBXO is more efficient at variable selection, while GE with normal crossover allows more variation in building architecture and fitting weights.
In summary, the second model can be considered more challenging for variable selection since the relevant predictor variables are correlated and the coefficients are relatively small.
While WGCNA defines patient mortality groups that predict survival independently of other clinical variables, it is interesting to know how this approach compares with a more conventional stepwise variable selection approach [ 37- 39].
