OBJECTIVE: Randomized controlled trials (RCTs) in obesity are plagued by missing data due to participant dropouts. Most methodologists and regulatory bodies agree that the primary analysis of such RCTs should be based on the intent-to-treat (ITT) principle, such that all randomized subjects are included in the analysis, even those who dropped out. Unfortunately, some authors do not include an ITT analysis in their published reports. Here we show that one form of ITT analysis, baseline observation carried forward (BOCF), can be performed utilizing only information available in a published complete-case (CC) analysis, permitting readers, editors, meta-analysts and regulators to easily conduct their own ITT analyses when the original authors do not report one. METHOD: We mathematically derive a simple method for estimating and testing treatment effects using the BOCF to allow a more conservative comparison of treatment effects when there are dropouts in a clinical trial. We provide two examples of this method using available CC analysis data from reported obesity trials to illustrate the application for readers who wish to determine a range of treatment effects based on published summary statistics. CONCLUSION: Commonly used CC analyses may lead to inflated type I error rates and/or treatment effect estimates. The method described herein can be useful for researchers who wish to estimate a conservative range of plausible treatment effects based on limited reported data. Limitations of this method are discussed.
OBJECTIVE: Randomized controlled trials (RCTs) in obesity are plagued by missing data due to participant dropouts. Most methodologists and regulatory bodies agree that the primary analysis of such RCTs should be based on the intent-to-treat (ITT) principle, such that all randomized subjects are included in the analysis, even those who dropped out. Unfortunately, some authors do not include an ITT analysis in their published reports. Here we show that one form of ITT analysis, baseline observation carried forward (BOCF), can be performed utilizing only information available in a published complete-case (CC) analysis, permitting readers, editors, meta-analysts and regulators to easily conduct their own ITT analyses when the original authors do not report one. METHOD: We mathematically derive a simple method for estimating and testing treatment effects using the BOCF to allow a more conservative comparison of treatment effects when there are dropouts in a clinical trial. We provide two examples of this method using available CC analysis data from reported obesity trials to illustrate the application for readers who wish to determine a range of treatment effects based on published summary statistics. CONCLUSION: Commonly used CC analyses may lead to inflated type I error rates and/or treatment effect estimates. The method described herein can be useful for researchers who wish to estimate a conservative range of plausible treatment effects based on limited reported data. Limitations of this method are discussed.
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