BACKGROUND: The MEGA study is a randomized controlled trial conducted in Japan to evaluate the primary preventive effect of pravastatin against coronary heart disease (CHD), in which 8214 subjects are randomized to diet or diet plus pravastatin. Pravastatin reduces the incidence of CHD (hazard ratio=0.67; 95%CI: 0.49-0.91). In the MEGA study, in addition to the usual loss to follow-up cases, there is another problem of drop-outs due to the refusal of further follow-up at 5 years. PURPOSE: To estimate the treatment effect adjusting for some types of dependent censorings observed in the MEGA study and to assess the sensitivity of standard analysis results for these censoring cases. METHODS: The proposed method is a straightforward extension of the inverse probability of censoring weighted (IPCW) method for settings with more than one reason for censoring, where the propensities for drop-outs are modeled separately for each reason. Simulation studies are also conducted to compare the properties of the IPCW estimate with the standard analysis assuming independent censorings. RESULTS: Simulation studies show that the IPCW estimate can correct for selection bias due to dependent censoring that can be explained by measured factors, while the standard analysis is biased. Applying the proposed method to the MEGA study data, several prognostic factors are associated with the censoring processes, and after adjusting for these dependent censorings, slightly larger treatment effects for pravastatin are observed for both CHD (primary endpoint) and stroke (secondary endpoint) events. LIMITATIONS: The method developed is based on the fundamental assumption of sequentially ignorable censoring. CONCLUSIONS: Our proposed method provides a valuable approach for estimating treatment effect adjusting for several types of dependent censorings. Dependent censorings observed in the MEGA study did not cause a severe selection bias attributable to the covariates and the results from the standard analysis were robust in relation to the censorings.
RCT Entities:
BACKGROUND: The MEGA study is a randomized controlled trial conducted in Japan to evaluate the primary preventive effect of pravastatin against coronary heart disease (CHD), in which 8214 subjects are randomized to diet or diet plus pravastatin. Pravastatin reduces the incidence of CHD (hazard ratio=0.67; 95%CI: 0.49-0.91). In the MEGA study, in addition to the usual loss to follow-up cases, there is another problem of drop-outs due to the refusal of further follow-up at 5 years. PURPOSE: To estimate the treatment effect adjusting for some types of dependent censorings observed in the MEGA study and to assess the sensitivity of standard analysis results for these censoring cases. METHODS: The proposed method is a straightforward extension of the inverse probability of censoring weighted (IPCW) method for settings with more than one reason for censoring, where the propensities for drop-outs are modeled separately for each reason. Simulation studies are also conducted to compare the properties of the IPCW estimate with the standard analysis assuming independent censorings. RESULTS: Simulation studies show that the IPCW estimate can correct for selection bias due to dependent censoring that can be explained by measured factors, while the standard analysis is biased. Applying the proposed method to the MEGA study data, several prognostic factors are associated with the censoring processes, and after adjusting for these dependent censorings, slightly larger treatment effects for pravastatin are observed for both CHD (primary endpoint) and stroke (secondary endpoint) events. LIMITATIONS: The method developed is based on the fundamental assumption of sequentially ignorable censoring. CONCLUSIONS: Our proposed method provides a valuable approach for estimating treatment effect adjusting for several types of dependent censorings. Dependent censorings observed in the MEGA study did not cause a severe selection bias attributable to the covariates and the results from the standard analysis were robust in relation to the censorings.
Authors: Anthony N Muiru; Erin Madden; Ani Chilingirian; Anna D Rubinsky; Rebecca Scherzer; Richard Moore; Celia P Corona Villalobos; Jose Manuel Monroy Trujillo; Chirag R Parikh; Chi-Yuan Hsu; Michael G Shlipak; Michelle M Estrella Journal: HIV Med Date: 2021-12-12 Impact factor: 3.094
Authors: Jonathan Dash; Thomas Verissimo; Anna Faivre; Lena Berchtold; Thierry Berney; Jérôme Pugin; Sophie de Seigneux; David Legouis Journal: J Clin Med Date: 2020-12-10 Impact factor: 4.241