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What happens when you use baseline as a covariate?
Note that when the baseline is included as a covariate in the model, the estimated treatment effects are identical for both ‘change from baseline’ and the ‘raw outcome’ analysis. Consequently if the appropriate adjustment is done, then the choice of endpoint becomes solely an issue of interpretability.
What happens when you adjust for baseline values?
However, adjusting for baseline values by including them in our model will increase the precision of our estimated treatment effect, even when there aren’t baseline differences ( Senn, 2019 ).
Is the test of baseline differences a result of chance?
The test of baseline differences is then testing a hypothesis we already know to be true: that there is no association between baseline engagement and treatment assignment. We know that the baseline differences are the result of chance because we did the randomisation! 2 As Bland and Altman (2011) put it (as cited by Harvey, 2018 ):
Do you randomise to remove baseline differences?
We do not randomise to ensure that there are no baseline differences between our groups, we randomise to remove any relationship between baseline values and treatment assignment.
When to adjust for baseline covariates in randomized controlled trials?
This is rather unfortunate, as it means that trials of the same treatments, in the same population, will estimate (in expectation) different treatment effects if in their analyses they adjust for different baseline covariates.
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.
Why is it important to adjust for covariates?
The amount of precision gained by adjusting for covariates depends on the strength of the correlation between the covariate(s) and outcome. Assumptions when adjusting for covariates. We have seen that adjusting for a baseline covariate can increase the precision of our treatment effect estimate.