Analysis:
key points
i)
Use of confidence intervals
The
assessment of the difference between the treatment groups is greatly enhanced
by the use of confidence intervals. The
confidence interval for the difference in treatment means contains the mean
differences that are compatible with the data from the trial, where compatible
is interpreted as not being rejected at the 5% level (for a 95% interval) by a
hypothesis test. This is particularly
valuable when the difference does not reach a high level of significance, such
as P < 0.05.
ii)
Uses and abuses of baseline values
Baseline
values, obtained just prior to randomization, can be useful in the analysis of
a trial. Comparing changes from baseline
between the groups can provide a more precise comparison than just using
outcomes, provided that the outcome and baseline have a sufficiently large
correlation. However, comparisons of
P-values between groups can be found in the literature and is a flawed
analysis.
iii)
Use of analysis of covariance
If there
is a baseline imbalance between groups, then it is reasonable to assume that
this will result in an outcome imbalance, even if there is no treatment
effect. Allowing for this in the
analysis requires the analysis of covariance, which will always give a more
precise analysis than either analysing outcomes alone or changes from baseline.