Sentence examples for more robust comparative from inspiring English sources

Thus, in our study, more robust comparative data on the use of exenatide or insulin glargine has been provided.

If funding allows we will retrospectively analyse a separate random sample of pre-vaccine CIN3 specimens from young women using the current LCM methodology to provide an even more robust comparative baseline.

The interpretation of data and evidence is more robust if a comparative and longitudinal perspective is possible because it sheds light on the similarities and divergences.

Our method provides a means to construct large phylogenies from primary data, which we hope will facilitate more sophisticated and robust comparative analyses.

To allow for a more robust assessment of the comparative effectiveness of PVI versus LEB, we conducted detailed chart abstraction to obtain key clinical and procedural variables.

30 However, longitudinal studies can examine the effects of illness over time and may provide a more robust picture of the impacts of comparative therapies than can cross-sectional studies.

Despite all these effort in developing more robust and accurate methods to perform comparative genomic studies, several biological phenomena pose difficulties in identifying the real genome dynamic processes in organisms.

However, the average accuracy of CCLS across all feature subsets is much high than the other algorithms, which indicates that the algorithm is more robust to feature subset selection than the comparative methods.

For example, Bath [ 7] found that a global SRH item was more robust in predicting mortality than a self-comparative measure that asked older adults to compare their present health to health five years previously.

In order to generate a more robust evidence-base for Hainan gibbon conservation, we employed a comparative approach to identify drivers of home range size, social group size and mating system across different gibbon species.

In addition, a comparative study demonstrates that the proposed algorithm is more robust against additive noise than traditional algorithms because of its insensitivity to the incorrect polarity estimation and an intrinsic ability for partially canceling noise influence.