A random effect generalised least squares model for trend estimation was used, employing study characteristics to control for heterogeneity.
To test whether there was a linear dose effect of the variant alleles (log-additive genetic model for trend test), SNPs were coded as 0, 1, and 2 as described above.
The same pattern of associations was observed when cognitive function was analysed as a continuous variable across fourths of cotinine concentration for both basic models (P for trend <0.001) and fully adjusted models (P for trend 0.025).
Variously lagged variables were introduced to regressions sequentially, all models controlling for trend, improving diagnostics, background seasonality, bank holidays and weekends.
We analyzed the data using semiparametric Poisson regression models adjusting for trend, seasonality, influenza epidemics, day of the week, and meteorology.
The DCS model for trends and seasonals, begin{aligned} y_{t}=mu _{tmid t-1}+gamma _{tmid t-1}+v_{t},quad t=1,...,T, end{aligned}has a structure which is similar to that of the innovations form of the Kalman filter for the BSM.
Linear regression models for the trend in the changes of the parameters indicated significant differences among the three systems (P < 0.05) over the time gaps (P < 0.05) for all parameters.
The Rayleigh optical depth calculations are compared with the Elterman's model calculations for trend analysis purpose.
High dietary GL and carbohydrate intake were negatively associated with the prevalence of obesity among men in a multivariate-adjusted logistic regression model (P for trend =.026 and.036, respectively).
