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How does adjusting for baseline affect treatment effect?
The amount of precision gained by adjusting for covariates depends on the strength of the correlation between the covariate (s) and outcome. We have seen that adjusting for a baseline covariate can increase the precision of our treatment effect estimate. But to do this, we have fitted a more complex regression model.
Can you adjust for baseline in a randomized trial?
This means that, in a randomized trial setting, we can advocate adjusting for baseline as our primary analysis method. Of course, it is important that the analysis approach is pre-specified. See the recent paper by Saquib et al on the topic of covariate adjustment and pre-specification in analysis plans for trials.
How to calculate baseline adjustment in mixed model?
I am doing mixed model analysis to evaluate (Y=) fruit intake (continuous variable) between two groups (intervention versus control) over time (baseline, year 1, year 2, year 5, year 7 and year 15). My model look like this: Y = group + time + group*time.
How are baseline covariates used to estimate treatment effect?
If the baseline covariate (s) is moderately correlated with the outcome, differences between the outcome values which can be attributed to differences in the baseline covariate can be removed, leading to a more precise (for linear models) estimate of treatment effect.
Why are post treatment scores and baseline scores the same?
Eventually two methods will estimate the same thing- difference in mean post-treatment scores between two groups, because the baseline mean scores are equal under randomization.
How to model pre and post treatment scores?
Instead, if you really want to model both pre- and post-treatment scores, you can use a constrained repeated measure model (time, time*group) by forcing the intercept (or difference in baseline score between two groups) equal to 0. This constrained repeated measure model performs comparably to ANCOVA model.