AUTHOR=Kaiser Kathryn Ann , Affuso Olivia , Desmond Renee , Allison David B. 

TITLE=Baseline Participant Characteristics and Risk for Dropout from 10 Obesity Randomized Controlled Trials: A Pooled Analysis of Individual Level Data

JOURNAL=Frontiers in Nutrition

VOLUME=1

YEAR=2014

URL=https://www.frontiersin.org/journals/nutrition/articles/10.3389/fnut.2014.00025

DOI=10.3389/fnut.2014.00025

ISSN=2296-861X

ABSTRACT=<p><bold>Introduction:</bold> Understanding participant demographic characteristics that inform the optimal design of obesity randomized controlled trials (RCTs) have been examined in few studies. The objective of this study was to investigate the association of individual participant characteristics and dropout rates (DORs) in obesity RCTs by pooling data from several publicly available datasets for analyses. We comprehensively characterize DORs and patterns in obesity RCTs at the individual study level, and describe how such rates and patterns vary as a function of individual level characteristics.</p><p><bold>Methods:</bold> We obtained and analyzed nine publicly available, obesity RCT datasets that examined weight loss or weight gain prevention as a primary or secondary endpoint. Four risk factors for dropout were examined by Cox proportional hazards including sex, age, baseline BMI, and race/ethnicity. The individual study data were pooled in the final analyses with a random effect for study, and HR and 95% CIs were computed.</p><p><bold>Results:</bold> Results of the multivariate analysis indicated that the risk of dropout was significantly higher for females compared to males (HR = 1.24, 95% CI = 1.05, 1.46). Hispanics and Non-Hispanic blacks had a significantly higher dropout rate compared to non-Hispanic whites (HR = 1.62, 95% CI = 1.37, 1.91; HR = 1.22, 95% CI = 1.11, 1.35, respectively). There was a significantly increased risk of dropout associated with advancing age (HR = 1.02, 95% CI = 1.01, 1.02) and increasing BMI (HR = 1.03, 95% CI = 1.03, 1.04).</p><p><bold>Conclusion/Significance:</bold> As more studies may focus on special populations, researchers designing obesity RCTs may wish to oversample in certain demographic groups if attempting to match comparison groups based on generalized estimates of expected DORs, or otherwise adjust <italic>a priori</italic> power estimates. Understanding true reasons for dropout may require additional methods of data gathering not generally employed in obesity RCTs, e.g., time on treatment.</p>