Last-observation-carried-forward method

In either case, reaching this point indicates that the drug is beneficial or not and is at least a qualitative endpoint. Last observation carried forward (LOCF), a standard method of data analysis, carries the last data point forward week by week. Random regression models can estimate what would happen at a later time point, assuming that patients change in a linear fashion. Improvement, however, often levels off. Thus, creating data points based on questionable assumptions can potentially introduce substantial bias. 

The last of these approaches is called imputation of missing values. As Piantadosi (2005) commented, while this approach sounds a lot like making up data, when done properly it may be the most sensible strategy. While techniques for addressing missing data can be technically difficult, one commonly used, simple imputation method is called last observation carried forward (LOCF). In a study with repeated measurements over time, the most recent observation replaces any subsequent missing observations (Piantadosi, 2005, see also Molenberghs and Kenward, 2007). 

For example, in the analgesic example cited above (25), a comparison was made between an analysis using the last observation carried forward (LOCF) method and the proposed mixed effects maximum likelihood method. Although this was a retrospective analysis, similar contrasts could be included in the trial s simulation to ascertain the most appropriate analytical methodology to include in the study design (protocol). Other analysis factors for consideration include appropriate correction of variability, where such sources may include differences between sites or regional differences. 

First, a number of simulations have shown how miserably last observation (carried forward) approaches perform compare with mixed-model approaches. However, in general one has to be very careful about assuming that simulations tell one what one thinks they do. For examples in other contexts where I have shown that they do not, the reader might be interested to consult Senn (1993, 1994, 1995, 1996, 2007). A key issue in comparing a simpler with a more sophisticated method is whether one has not implicitly assumed information that in practice one would not have. 

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