An interim analysis is one that's carried out before the conclusion of a clinical trial, using only the data that has been obtained so far. Interim analyses can be blinded or unblinded and can be done for several reasons:
An institutional review board (IRB) may require an early look at the data to ensure that subjects aren't being exposed to an unacceptable level of risk.
You may want to examine data halfway through the trial to see whether the trial can be stopped early for one of the following reasons:
The product is so effective that going to completion isn't necessary to prove significance.
The product is so ineffective that continuing the trial is futile.
You may want to check some of the assumptions that went into the original design and sample-size calculations of the trial (like within-group variability, recruitment rates, base event rates, and so on) to see whether the total sample size should be adjusted upward or downward.
If the interim analysis could possibly lead to early stopping of the trial for proven efficacy, then the issue of multiplicity comes into play, and special methods must be used to control alpha across the interim and final analyses.
These methods often involve some kind of alpha spending strategy. The concepts are subtle, and the calculations can be complicated, but here's a very simple example that illustrates the basic concept.
Suppose your original plan is to test the efficacy endpoint at the end of the trial at the 5 percent alpha level. If you want to design an interim analysis into this trial, you may use this two-part strategy:
Spend one-fifth of the available 5 percent alpha at the interim analysis.
The interim analysis p value must be < 0.01 to stop the trial early and claim efficacy.
Spend the remaining four-fifths of the 5 percent alpha at the end.
The end analysis p value must be < 0.04 to claim efficacy.
This strategy preserves the 5 percent overall alpha level while still giving the drug a chance to prove itself at an early point in the trial.