Missing data issues have been discussed and debated for many years. Handling of missing data in clinical trials has been recognized as an important issue not only for statisticians who analyze the data, but also for the clinical study team who conduct the study. While we are still waiting for FDA to issue its guidance on missing data in clinical trials, there are several guidelines published recently.
EMEA just issued its final rule of "Guideline on missing data in confirmatory clinical trials". This guideline provided the guidance on handling the missing data from the perspective of European regulatory authorities. Comparing to the FDA's guidance on non-inferiority and adaptive design, EMEA's missing data guidance is written in plain language and can be easily understood by the non-statisticians.
The recent trend is to discourage the use of LOCF and other single imputation methods (ie, replace the missing value with the last measured value, with averaged value, or with baseline value,...). It is noted that LOCF is mentioned as one of the single imputation methods in EMEA's guideline. The guideline acknowledged that "Only under certain restrictive assumptions does LOCF produce an unbiased estimate of the treatment effect. Moreover, in some situations, LOCF does not produce conservative estimates. However, this approach can still provide a conservative estimate of the treatment effect in some circumstances.". The guideline further elaborated that LOCF may be a good technique for studies (e.g. depression, chronic pain) where the condition is expected to improve spontaneously over time, but may not be conservative for studies (e.g. Alzeimer's disease) where the condition is expected to worsen over time.
In the United States, the Division of Behavioral and Social Sciences and Education under National Research Council of the National Academies have been working on a project "Handling missing data in clinical trials". The working group recently makes its draft report available. The draft report is titled "The prevention and treatment of missing data in clinical trials". I like the word 'prevention' in the title since it is critical to prevent or minimize the occurrence of missing data. Once the missing data has happened, there is no universal method to handle the missing data perfectly. The assumptions of MACR, MAR, and MNAR can never been fully verified.
Academies' report on missing data has a stronger language in discouraging the use of LOCF and other simple imputation approaches. The recommendation #10 stated "Single imputation methods like last observation carried forward and baseline observation carried forward should not be used as the primary approach to the treatment of missing data unless the assumptions that underlie them are scientifically justified."
So far, there is no official guideline from FDA regarding the missing data handling (even though the topic has been the perennial topic in almost all statistics conferences and workshops). Nevertheless, a presentation by Dr. O'Neill to the International Society of Clinical Biostatistics may give some insides.