A simple antagonism model suggested age-related differences in Tθ and Tω relations would be magnified if antagonistic torque contributions were included.
Including age significantly improves the fit of the model though we appreciate that the 95% Bayesian credible intervals span 0. We have augmented the text in order to reflect this: "Including age improves the fit of the model suggesting age is an important confounder though the Bayesian credible intervals include zero".
None of the parametric models suggests age, residence, province, type of cancer, methods of cancer diagnosis, educational level, occupation, smoking, ethnicity, migration status and drug use as a significant prognostic factors.
The data-derived model suggested that age had an indirect effect on perceived caregiver stress, through hours of care.
The addition of an age interaction term to the model suggested that the age effect was stronger among Ascaris IgE positive children; the effect of being Ascaris IgE positive significantly reduced the odds of being TST positive amongst younger children while this effect weakened with increasing age.
Examination of the path coefficients in this just identified model suggested that younger age (r = −0.21, P < 0.001), using insulin (r = 0.27, P < 0.001), having been diagnosed with diabetes for more years (r = 0.16, P < 0.01), and African American race (r = 0.12, P < 0.01) were associated with higher A1C levels and accounted for 17% of the variability in A1C levels.
Including age improves the fit of the model suggesting that age is an important confounder, although the Bayesian credible intervals include 0. Children >6 years old were 15% (CI, 24 64%) more likely to infect mosquitoes than those of younger ages, whereas no difference in the relationship between gametocyte density and mosquito infection was detected between Burkina Faso and Kenya.
The disappearance of a significant effect of age on ChEI prescribing in the adjusted model suggests that age as such is not an important factor for the decision to prescribe.
This model suggests that age class (P<0.001) and pack size (P = 0.03) have an important influence on PA during winter, but that social status (P = 0.42), study period (P = 0.88), prey size (P = 0.57), and sex (P = 0.27) are not important factors.
This model suggests that age and study may be significant factors in the scale, but none of the factors appear to have a significant contribution to the shape parameter.
