Last observation carried forward One method of handling missing data is simply to
impute, or fill in, values based on existing data. A standard method to do this is the Last-Observation-Carried-Forward (LOCF) method. The LOCF method allows for the analysis of the data. However, recent research shows that this method gives a biased estimate of the treatment effect and
underestimates the variability of the estimated result. As an example, assume that there are 8 weekly assessments after the baseline observation. If a patient drops out of the study after the third week, then this value is "carried forward" and assumed to be his or her score for the 5 missing data points. The assumption is that the patients improve gradually from the start of the study until the end, so that carrying forward an intermediate value is a conservative estimate of how well the person would have done had he or she remained in the study. The advantages to the LOCF approach are that: • It minimises the number of the subjects who are eliminated from the analysis, and • It allows the analysis to examine the trends over time, rather than focusing simply on the endpoint. However, the
National Academy of Sciences, in an advisory report to the
Food and Drug Administration on missing data in clinical trials, recommended against the uncritical use of methods like LOCF, stating that "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."
Multiple imputation methods The National Academy of Sciences advisory panel instead recommended methods that provide valid
type I error rates under explicitly stated assumptions taking missing data status into account, and the use of multiple imputation methods based on all the data available in the model. It recommended more widespread use of
Bootstrap and
Generalized estimating equation methods whenever the assumptions underlying them, such as
Missing at Random for
GEE methods, can be justified. It advised collecting auxiliary data believed to be associated with dropouts to provide more robust and reliable models, collecting information about reason for drop-out; and, if possible, following up on drop-outs and obtaining efficacy outcome data. Finally, it recommended sensitivity analyses as part of clinical trial reporting to assess the
sensitivity of the results to the assumptions about the missing data mechanism. Expert statistical and medical judgment must select the method most appropriate to the particularly trial conditions of the available imperfect techniques, depending on the particular trial's goals, endpoints, statistical methods, and context. == Estimands ==