In a multivariable adjusted model, women with probable migraine had a 1.61-fold increased odds of PTSD (95% CI: 1.30 1.99), and women with migraine had a 2.85-fold increased odds of PTSD (95% CI: 2.18 3.74), compared to women without migraine (Table 2).
The model with 'probable dependence' was not significant (P = 0.056) and was weaker (pseudo R = 0.0668), while the second (with age, domestic violence and morning drinking) was significant.
We fitted modified Poisson regression models, adjusted for sociodemographic variables, with probable depression (PHQ 9 score ≥10 or recent suicidal thoughts) as the outcome variable and the Internalized AIDS–Related Stigma Scale (IARSS) score as the explanatory variable.
The established pathological AD "signature" of biomarkers consisting of predefined cutoff values of t-tau, p-tau181, and Aβ42 levels that are predictive of AD [ 110, 112] is being used in combination with the logistic regression of tau, Aβ, and APOE ε4 alleles model [ 111] to delineate subjects with probable AD.
Docking simulation has been performed to position compound 3e into the EGFR active site to determine the probable binding model, with an estimated binding free energy value of −10.7 kcal/mol.
19 All analyses were carried out with a random effects model given probable clinical heterogeneity in study design between trials regardless of statistical homogeneity.
Finally, a conclusive model map is provided for combining the respective catalytic reaction models with the corresponding probable active sites on the surfaces of graphene-based carbocatalysts.
DAMSUP was used to align all models with the most probable one, and all of the aligned models were averaged in DAMAVER to compute a probability map.
The estimated gamma parameter and proportion of invariant sites suggested two nearly equally probable models: a model with no invariant sites and high rate variation and a model with high proportion of invariant sites (0.97) and no rate variation.
For the purposes of this study, we also developed a new method that enabled the integration of spatial data generated through an atmospheric dispersion model with temporal data concerning time of probable infection.
Akaike weights inform on how much more probable is the model with the lowest AIC, with respect to the other models allowing not only to identify the best model, but also to say something on how far the others are from its performance.
