Sentence examples for modelling analyses were from inspiring English sources
Therefore, in order to address this non-code compliance condition, evacuation and fire modelling analyses were carried out to estimate the Available Safe Egress Time (ASET) and the Required Safe Egress Time (RSET) for people evacuation from the TV Studio.
Specifically, genetic modelling analyses were performed using the OpenMx version 1.0.3 (Boker et al. 2011).
Remarkably, due to data constraints, modelling analyses were conducted using aggregated data over large spatial scales or long time periods [ 34].
Whereas a previous study provided insight into the efficacy of the intervention on PA one year after the intervention started [ 16], for the current study modelling analyses were performed to provide insight into the intervention's health effects, which often become more visible when follow-up time increases, mostly beyond the trial period.
Finally, a limitation rather common to modelling analyses is the fact that QALY results are driven primarily by the cost of the drugs.
Although formal modelling analyses are required to evaluate the cost-effectiveness of screening strategies with vaccination, changes in the length of the screening interval and/or the age at which screening starts can be anticipated.
The model (the CLD) is presented in Appendix 1. "What if" analyses were performed iteratively during the modelling process although the last workshop was dedicated to experimentation.
All statistical analyses were performed using the modelling program Enterprise Guide version 3.0, powered by SAS version 9.1 (SAS Institute Inc., Cary, NC, USA).. Significance was set at P = 0.05.
Analyses were performed using the multilevel modelling package MLwiN (Rasbash et al, 2000).
Statistical analysis All genetic analyses were carried out in Mx [ 17], a structural equation modelling program specifically designed for the genetic analysis of twin and family data.
In other areas of multivariate statistics the role of model-based analyses is also central e.g. missing data modelling using maximum likelihood.
