Between and within patient variability was estimated as part of the model estimation procedure in NONMEM.
In the non-compartmental analysis, the level was estimated based on pre-dose FVIIa coagulant activity levels and subtracted from the post-dose measurements; in the population analysis, the level was included as a subject specific random effect and estimated as part of the model estimation procedure.
The stepwise model estimation procedure is chosen because the total number of potential models is computationally prohibitive.
We parameterized the model using a binomial response distribution, logit inverse link function, and constant subject-specific random effect (detailed information on the model, estimation procedure, and code are available upon request).
As several of these (independent) variables are expected to explain the same part of the variance in the total SSS volume between countries (dependent variable), a stepwise model estimation procedure has been used.
We report 90 and 95% confidence intervals for the odds ratios as well and assess the statistical significance of our results.2 See Appendix C for details on the model estimation procedure.
The model estimation procedure is executed via GAUSS 8.0 [36].Thexplanatoryry variables tested for the cross-sectional binary logit model include age, age square, gender, years of driving experience, driver type, expressway use frequency, travel time saving, cause of expressway delay, and number of traffic lights on the arterial road.
The model estimation procedure for the unit of Figure 4 A is shown in Figure 4 B-a – B-h.
We used a model estimation procedure to identify SP-related signals in FEF during step-ramp tracking.
The model estimation procedure for this unit is shown in Figure 5 B. The fit obtained using this 6-component model had a CD of 0.97.
