Kreara

LOCF

In clinical trials, data are often collected over a period of time from participating patients. In many situations, however, analyses are only based on data from the last time point (the end of the study) or change from the baseline to the last time point. It is often the case that patients drop out before the completion of the study.

So the question arises on how to perform analysis of the last observations, which are defined as observations from the last time point for patients who completed the study and the last observations prior to the dropout for patients who did not complete the study.

An analysis based only on data from patients who completed the study is called a completers analysis. Although a completers analysis is sufficient in some situations, it is often more desirable to perform an all randomized subjects analysis.

The analysis based on all randomized subjects is usually referred to as an intention-to-treat (ITT) analysis. Regulatory agencies generally consider the ITT analysis as the primary analysis for evaluation of efficacy and safety in clinical trials with informative dropout.

When the dropout is informative, the target populations of a completers analysis and an ITT analysis are different. Suppose that the population of patients under certain treatment is stratified according to the time of the last observations; then the target population of a completers analysis is only the subpopulation of patients who completed the study under different treatments while the target populations of an ITT analysis include all the subpopulations under different treatments.

For the last observation carry-forward (LOCF) analysis based on ITT population, the last observations are carried forward to the last time point for patients who dropped out. The LOCF analysis treats the carried-forward data as observed data at the last time point. Therefore when the dropout is informative, the LOCF analysis may introduce biases to the statistical inference, which has been speculated upon by the Food and Drug Administration (FDA) it is still unknown whether or not the LOCF test is asymptotically correct

Here is a macro that might help you with LOCF