In light of the preceding discussion, it is easy to see that baseline measurements offer two opportunities for reducing person-to-person variation.
First, some components of the baseline measurements such as demographics and risk factors can be used for forming subgroups or strata for analysis.
Second, obtaining a baseline measurement allows us to use each individual as his own control. Without a baseline measurement, we would be forced for our comparisons to depend on the final reading of the primary response variable alone.
Let's suppose this response variable is blood pressure. It might be that an untreated individual has a final diastolic reading of 90mmHg while an individual treated with our new product has a reading of 95 mm. Doesn't look good for our new product. But what if I told you the first individual had a baseline reading of 100 mm Hg, while the second had a baseline of 120mmHg. Comparing the changes that take place as a result of treatment, rather than just the final values, reveals in this hypothetical example that the untreated individual had a change of 10 mm, while the individual treated with our product experienced a far greater drop of 25mmHg.
The initial values of the primary and secondary response variables should always be included in the baseline measurements. Other essential baseline measurements include any demographic, risk factor, or baseline reading (laboratory values, ECG or EEG readings) that can be used to group the subjects of our investigation into strata and reduce the individual-to-individual variation.
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