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Exact(5)
Two separate datasets were set up to model each outcome, given the different follow-up times.
In the competing risks model, each outcome is treated as an absorbing state, so that transitions do not occur after reaching these outcomes.
Multivariable generalized linear regression models with a log-link function and binomial error using SAS [SAS Institute Inc., Cary, NC, USA] version 8.1, were used to model each outcome.
Mixed-model analysis will be used to model each outcome measure at three (or two) time points and compare the differences across groups over time for non-continuous data.
Three level mixed effects linear regression models were used to model each outcome at the three time points: baseline (age 9/10 years), 1 (age 10/11 years) and 4 years (age 13/14 years).
Similar(55)
We used multiple linear regression models (one model for each outcome) to assess pre-post behavior change comparing the intervention and control communities.
Two multivariable linear regression models (separate models for each outcome) were used to examine the simultaneous association between all predictor variables and outcomes).
Following bivariate analysis, multivariate models included all of the factors within each category (ie, all health factors in one model, all sociodemographic in the second model and all of the workplace factors in the third model for each outcome).
The best fitting model for each outcome variable will be decided by selecting top fitting models based on Akaike's Information Criterion (AIC).
Our analysis shows that such an unigram model, where each outcome is independent of the previous outcome, is not adequate to model the EBUDS corpus.
We constructed the candidate models for each set using standard automated procedures (Step 5), and selected a final candidate model for each outcome based on significance and goodness-of-fit (Step 6).
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