Using a multinomial logistic regression, we find that travel patterns are relatively stable along much of the urban-rural continuum, everything else equal.
We collected patient demographics, comorbidities, and postoperative complications from both groups, then compared treatments using a multinomial logistic regression model.
Using a multinomial logistic regression procedure, we failed to establish factors associated with ECMO device colonisation or infection, probably because of the cohort size.
Factors independently associated with early or late in-hospital death were identified using a multinomial logistic regression model using the status alive as the reference category.
This can be handled by the mechanism f ( y | α c, α d, μ, ϕ, c, d ) = α c, y = c α d, y = d ( 1 - α c - α d ) f ( y | μ, ϕ, c, d ), y ∈ ( c, d ), where the vector of probabilities (α c,α c,1 - α c - α d ) should be modelled using a multinomial logistic.
We perform online predictions where we fit the data up to step n, and then predict the next location at step (n+1) using a multinomial logistic regression model; classes are simply the set of all possible places visited so far.
We used a multinomial logistic regression model to calculate the association between the proportion of aPTT and R-K within target range in the two study groups.
To accommodate the structure of the data we used a multinomial logistic regression with partnerships (N = 590) clustered by respondents (N = 148) using survey estimators in STATA [ 37].
We used a multinomial logistic hazard model to model the log odds of death before long term cessation and long term cessation simultaneously.
We used a multinomial logistic regression model for the prediction analysis, and the modeling details follow closely the previous study of eye color (Liu et al. 2009).
We used a multinomial logistic regression model because the outcome variable of interest was four levels: 1) use of CAM only; 2) use of conventional medications only; 3) use of both; and 4) use of neither.
