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All three variables were included in the LGM models as time invariant covariates one at a time.
In addition to the follow-up time, we used age divided into five year intervals (overall model) and calendar year (overall and age stratified models) as time scales.
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These were included in models as time-dependent variables.
All of these variables were measured at each occasion and are included in the models as time-varying covariates.
Variables measured over time (continuous and categorical) will be entered into survival models as time-dependent covariates as these measurements will be repeatedly obtained from individual participants.
Our estimates were adjusted by age and squared age, which were introduced in the models as time-varying covariates; under these conditions, residual confounding by age is unlikely.
Date, day of the week, season, and 24-hr moving averages for relative humidity and temperature were included in the models as time-varying confounders based on goodness-of-fit as determined using Akaike's information criterion.
Virologic suppression and change in plasma ANG-2 (for the ANG-1 outcome) or change in plasma ANG-1 (for the ANG-2 outcome) were included a priori in both models as time-dependent predictors, to evaluate the relationship with plasma HIV-1 RNA and because the angiopoietins are competitors at the Tie2 receptor, respectively.
Comparison is done for evaluating the forecasting performances of seasonal and global models as well as the time model.
This paper forecasts agricultural commodity weekly prices with ARIMA model as time factor forecasting model.
These additional constraints are date delivery constraints that are modeled as time windows.
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