Observation Carried Forward LOCF

Often you need to carry forward data to a specific time point due to holes or sparseness of data. The previous example on determining baseline cholesterol level provides an excellent context for this problem. Assume that you have several cholesterol readings of HDL, LDL, and triglycerides for patients before they take an experimental pill designed to reduce cholesterol levels. For each cholesterol parameter, you want the last observation carried forward so long as the measures occur within a five-day window before the pill is taken. Here are some sample data that illustrate the problem  

Subject Sample Date HDL LDL Triglycerides Dosing Date 

The underlined values in the sample data fit the definition of baseline. You can see how the last non-missing value should be carried forward. The following SAS code selects those proper baseline values. 

Program 4.1 Deriving Last Observation Carried Forward (LOCF) Variables 

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Key Concepts for Creating Analysis Data Sets 84 Defining Variables Once 84 Defining Study Populations 85 Defining Baseline Observations 85 Last Observation Carried Forward (LOCF) 86 Defining Study Day 89 Windowing Data 91 Transposing Data 94... 

Clinical assessments were made by specialized raters not involved in the treatment after 1, 4, 8, 12 and 16 weeks. The study was intended to include 96 patients, 15 of whom withdrew at the very beginning when they heard what treatment they were to be given. Most of those withdrawing were in the psychotherapy group. In addition, patients who failed to show any pronounced improvement in their symptoms within 8 weeks or whose condition even deteriorated were withdrawn from the study. In the case of those patients withdrawing from the study, scores recorded at the time of withdrawal were used for purposes of evaluation this represents a last observation carried forward (LOCF) analysis. Withdrawals from the study occurred rather frequently and showed the following distribution ... 

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. 

An international, multicenter, double-blind trial addressed the acute efficacy and safety of a single-dose range of olanzapine (5 to 20 mg/day) compared with a single-dose range of haloperidol (5 to 20 mg/day) (11.6). A total of 1996 patients with a DSM-lll-R diagnosis of schizophrenia (83.1%), schizophreniform disorder (1.9%), or schizoaffective disorder (15%) participated in this study. The primary overall efficacy analysis (i.e., the difference in baseline to endpoint (last observation carried forward [LOCF]) mean change on the BPRS) found olanzapine to be statistically superior to haloperidol (HPDL) (i.e., -10.98 -7.93 p < 0.015). 

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. 

Last observation carried forward (LOCF) analysis is the standard approach with a... 

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