Understanding participant demographic characteristics that inform the optimal design of obesity randomized controlled trials (RCTs) have been examined in few studies. The objective of a recent study led by Dr. Kathryn Kaiser, instructor in the office of energetics at the University of Alabama at Birmingham—collaborating with Dr. Olivia Affuso, associate professor in UAB’s department of epidemiology; Dr. Renee A. Desmond, associate professor in the division of preventive medicine; and Dr. David B. Allison, distinguished professor and director in the office of energetics—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. The researchers comprehensively characterized DORs and patterns in obesity RCTs at the individual study level and described how such rates and patterns vary as a function of individual-level characteristics.
The team 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: sex, age, baseline body mass index (BMI), and race/ethnicity. The individual study data were pooled in the final analyses using a random effect for study, and Hazard Ratios and 95 percent confidence intervals were computed.
Results of the multivariate analysis indicated that the risk of dropout was significantly higher for females compared with males, and Hispanics and non-Hispanic Blacks had a significantly higher dropout rate compared with non-Hispanic Whites. There was a significantly increased risk of dropout associated with advancing age and increasing BMI.
Dr. Kaiser and her colleagues concluded that, 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 dropout rates or otherwise adjust a priori power estimates. In addition, understanding true reasons for dropout may require additional methods of data gathering not generally employed in obesity RCTs (e.g., time on treatment).
“Baseline Participant Characteristics and Risk for Dropout from Ten Obesity Randomized Controlled Trials: A Pooled Analysis of Individual Level Data” was published online in November in the journal Frontiers in Nutrition.