The Analysis of Change

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In a randomized trial, mean differences based on changes from baseline can usually be assumed to be addressing exactly the same underlying intervention effects as analyses based on final measurements.

That is to say, the difference in mean final values will on average be the same as the difference in mean change scores. If the use of change scores does increase precision, the studies presenting change scores will appropriately be given higher weights in the analysis than they would have received if final values had been used, as they will have smaller standard deviations.

When combining the data authors must be careful to use the appropriate means and standard deviations either of final measurements or of changes from baseline for each study. Since the mean values and standard deviations for the two types of outcome may differ substantially it may be advisable to place them in separate subgroups to avoid confusion for the reader, but the results of the subgroups can legitimately be pooled together.

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However, final value and change scores should not be combined together as standardized mean differences, since the difference in standard deviation reflects not differences in measurement scale, but differences in the reliability of the measurements. A common practical problem associated with including change-from-baseline measures is that the standard deviation of changes is not reported.

Analysis of change.

Imputations of standard deviations is discussed in Chapter 16 Section Examples come from published data. Statistical methods used in the examples include paired t-tests and analysis of covariance.

The use of difference scores is discussed relative to analysis of covariance. National Center for Biotechnology Information , U.

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