What is last observation Carried forward LOCF?

Last Observation Carried Forward (LOCF) is a common statistical approach to the analysis of longitudinal repeated measures data where some follow-up observations may be missing.

What is WOCF?

WOCF (Worst observation carried forward): this approach is the most conservative comparing to LOCF and BOCF. This technique has been used in analgesia drug as well as the trials with laboratory results as endpoint. For example, WOCF technique is mentioned in FDA Summary on Durolane.

How do you report missing data in research?

In their impact report, researchers should report missing data rates by variable, explain the reasons for missing data (to the extent known), and provide a detailed description of how missing data were handled in the analysis, consistent with the original plan.

What is the last observation carried forward?

This is called the Last Observation Carried Forward (LOCF) and is familiar to the Pharmaceutical audience. There are a number of ways to carry forward the last observation using SAS. This paper focuses on a technique that has gained prominence among SAS-L regulars.

What is the last observation carried forward imputation method?

That is the Last Observation Carried Forward (LOCF) imputation method. The assumption for this imputation is the response remains constant at the last observed value. In general, we can use this method when data are in longitudinal structure. For example, repeated measures were taken per subject by time point.

When to use a direct approach to last OBS?

If the purpose of the exercise is to access the last obs “only” then a more direct approach can be used. Editor’s note: this is a very popular question. To help others to find the answer, we have consolidated the most helpful answers into this one reply as an Accepted Solution.

Is it possible to remove the last observation in a program?

No, because if the last observation was REMOVEd as in my example, your program won’t work. Good point. What you lose in readability/maintainability you gain in robustness. have we seen this topic before?