The statistical technique last observation carried forward is often used in clinical trials. I find it quite troubling.
One example is this recent obesity drug study from The New England Journal of Medicine. Patients in both the study drug group and the placebo group met with doctors monthly for counseling and to have various vital signs measured.
As with many obesity studies, about half of the patients dropped out before the end of the 2-year study. To account for this, the patient's last recorded weight is carried through for the rest of the study to replace any missing values. If a patient last weighed in at 240 pounds at month 3 and then stopping showing up, the statisticians would assume that her weight remained 240 pounds in months 4, 5, 6, and so on. See here for a graphical example, as well as some concerns that this technique may cause.
This methodology paints a much different picture than does the reality that about half of patients will give up on the drug within 2 years. If all of those observations were instead treated as missing (after all, they are missing in reality), that would severely hamper the statistical power of the results. Of course, this is the last thing the researchers want to do.
Additionally, last observation carried forward requires the assumption that the last recorded observation equals any remaining observations. However, it's easy to imagine some patients who don't find the drug effective, experience additional weight gain, become discouraged, and thus stop participating in the study. This weight gain is not only not observed by the study, but is also being entered into the records as not having happened. Because of this, last observation carried forward creates a clear bias in favor of the study drug.