A closer look at the baseline-observation-carried-forward (BOCF)

Abstract

Purpose:The baseline-observation-carried-forward (BOCF) approach is one method to handle missing data from early treatment discontinuation. We examined modifications of this approach, taking into consideration treatment-related and nontreatment-related reasons for discontinuation. Methods: Two duloxetine chronic pain trials (placebo-controlled) were used to examine the impact of different analytical methods on study outcome. Reasons for discontinuation were categorized as treatment-related and nontreatment-related. Missing data in the primary efficacy outcome were handled using five statistical methods: mixed-model repeated measures (MMRM), last-observation-carried-forward (LOCF), BOCF, modified BOCF (mBOCF, discontinuation due to treatment-related reasons, ie, adverse events [AEs] or lack of efficacy), and aeBOCF (discontinuation due to AEs only). Results: Duloxetine was superior to placebo on mean change from baseline in Brief Pain Inventory average pain rating, using MMRM (study 1, P = 0.004; study 2, P  0.001), LOCF (study 1, P = 0.019; study 2, P  0.001), BOCF (study 1, P = 0.019; study 2, P = 0.013), and mBOCF (study 1, P = 0.041; study 2, P = 0.005). Using aeBOCF, duloxetine was superior to placebo in study 2 (P = 0.005) and numerically better in study 1 (P = 0.075). Conclusion: Due to the different assumptions made by various methods regarding accounting for missing data, the analytical methods chosen may influence the interpretation of study results. Consideration should be given to the effect of actual treatment outcomes from patients. Employing different statistical approaches such as sensitivity analyses may help to assess the robustness of the study results and provide a more accurate reflection of the treatment outcome.

Reduced symptom burden with the support of an interactive app during neoadjuvant chemotherapy for breast cancer – A randomized controlled trial

“…Statistical analyses were performed using IBM Statistical Package for Social Sciences version 24, Windows version. An intentionto-treat analysis approach was used for the analyses with missing values being replaced using the baseline observation carried forward method (BOCF) [33,34]. Analyses according to both BOCF and complete cases are presented in Tables 3 and 4.…”

“…However, according to Osoba et al (1998), a mean change of 10 points or more in the EORTC-scales is good and considered as an established level showing a clinical change [49]. Using BOCF for imputation of missing data is a wellknown conservative method, which can lead to under-or overestimating the results [33]. A strength is that the results from the complete cases analyses showed similar results.…”

Telehealth-delivered, Cardioprotective Diet and Exercise Program for Liver Transplant Recipients: A Randomized Feasibility Study

“…Linear regression was performed to identify predictors of change in dietary adherence. All analyses were intention-to-treat, in which missing data were imputed using last observation carried forward 23 or the mean value where in baseline data was missing (9%). Statistical significance was reported where P < 0.05, and all analyses were performed using SPSS (Version 25.0, New York, IBM Corp.).…”

Enhancing resilience in cross-boundary families: A parent–child parallel group intervention

“…Baseline observation carried forward (BOCF) was adopted to handle participant dropout. Based on the assumption that participants might withdraw from the intervention because of a perceived lack of benefit, the BOCF approach uses baseline observation to replace the missing endpoint (Liu-Seifert et al, 2010). BOCF preserves all samples and, to some extent, can avoid the problem of biased parameters and estimates caused by list-wise deletion (Liu-Seifert et al, 2010).…”