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

• Published in: Patient preference and adherence Vol. 4; no. default; pp. 11 - 16
• Main Authors: Liu-Seifert, Hong, Zhang, Shuyu, D'Souza, Deborah, Skljarevski, Vladimir
• Format: Journal Article
• Language: English
• Published: New Zealand Taylor & Francis Ltd 01.01.2010; Dove Press; Dove Medical Press
• Subjects: discontinuation; Original Research; Pain; statistical; treatment-related; pain; statistical; discontinuation; treatment-related
• DOI: 10.2147/PPA.S8135
• ISSN: 1177-889X

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. 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). 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). 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.