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As this was a pilot study, the sample size was too small for multivariable model adjustment.
We used directed acyclic graphs (DAGs) to choose potential confounders for model adjustment.
27 When change-point models were used, results strongly depended on model adjustment.
Pearson's linear correlation was estimated between predicted and observed data to evaluate model adjustment.
Results were significant at a p level <0.05 and Hosmer and Lemeshow test was used to check model adjustment.
A 5% significance level was established, and Hosmer and Lemeshow test was used to check model adjustment.
Multiple log-binomial regression models were used, as the outcome was binary; procedure PROC GENMOD was applied for model adjustment.
This list of variables plus study center (12 strata), child's sex, serum levels of triglycerides and total cholesterol were the covariates selected a priori for model adjustment.
Of the trials, 15% (30/199) had published design papers and 70% (21/30) of them provided information on model adjustment.
By applying our model to two stamen microarray data sets, we showed that our model adjustment obtained a significant improvement for the quality of microarray raw data.
When the effects of age and sex on implant survival were analyzed with the Cox model, adjustment was also made for implant groups (Furnes et al. 2001).
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