To test statistical interactions between the genotypes and cigarette smoking in unconditional models, we first used a likelihood-ratio test (LRT) to compare nested models that included terms for all combinations of the genotypes and cigarette smoking status to the models with indicator variables for the main effects only (nominal LRT).
To test the statistical significance of interactions between the environmental exposures and the XRCC1 genotype, we compared the models that included terms for all combinations of the XRCC1 genotype and levels of environmental exposure to the models with indicator variables for the main effects only (nominal LRT).
Step 5: Estimate joint effects of mixtures: We then estimated the joint effects of these C&RT-generated mixtures using the base model with indicators for each mixture.
Characterizing this generic model with indicators of dynamic and steady-state properties (including power spectra and distributions), we reveal the potential activity of any promoter and its influence on gene expression.
We tested the null hypothesis that the odds of breast cancer was constant across ethnic subgroups by comparing a model with indicators for ethnic subgroup to one without, using a likelihood ratio test (LRT).
To estimate the on-trial effects of medication, we fit separate repeated-measures linear models for standard and intensive arms with time-dependent covariates in which the dependent variable is change in weight measured at 4-month intervals through year 2. All models began with indicators for medication use at baseline.
Qualitative matching is incorporated into the model with indicator variables, i.e., some is an indicator variable for respondents in a "somewhat related" job, while close is an indicator variable for respondents in a "closely related" job.
If the continuous model was not significantly different from the model with indicator variables (p ≥0.05), the linearity hypothesis was accepted.
Owing to computational limitations, models of indicator variability (both fixed and random effects) failed to converge, hence we used a three level model with indicator observations nested within practices and practices nested within primary care trusts.
The number of correct turns, scored over five trials, was considered as count data for each time point during the experiment, with a random-intercept multilevel Poisson model with indicator variables fitted for each time point.
