Obesity is estimated to affect over 50 million Americans and its resulting morbidities remain significant problems for many individuals. Current treatments are only modestly effective and no treatment works for all individuals. Thus, it is critical to identify and evaluate new strategies for both efficacy and safety. Yet, several important questions about how to best design, interpret, and analyze randomized clinical trials for obesity treatments remain unanswered and in some cases completely unaddressed. Tens of millions of dollars are spent on obesity treatment studies annually by government and industry. Such studies could be better designed and more efficiently executed if they could draw on a well established body of empirical evidence regarding factors such as the effects of using run-in periods, expected drop-out rates, factors affecting drop-out rates, and the relation between population treatment effect and the expected degree of weight loss in a control condition. Information about all of these factors is critical for optimally designing studies, yet no rigorously established comprehensive body of knowledge about these factors exists. Thus, the purpose of the proposed research is to use meta-analytic methodology to develop this knowledge.
Three specific aims are proposed.
Aim 1. Comprehensively investigate of the effect of run-in periods on (a) subject drop-out rates (DORs);and (b) weight loss (WL).
Aim 2. Comprehensively characterize DORs and patterns in obesity RCTs and how such rates and patterns vary as a function of RCT and patient characteristics using meta-regression of published summary statistics AND analysis of multiple raw datasets from large RCTs.
Aim 3. Use a novel hierarchical meta-regression method to estimate the association between the true population (not sample) treatment effect and the true population (not sample) mean weight loss of the control group. This is important because it influences decisions about how vigorous the lifestyle modification interventions provided to control groups in RCTs of pharmaceuticals, surgery, and extra-lifestyle interventions should be. To accomplish these aims, we have assembled an experienced team of obesity researchers and statisticians. This research should give valuable answers and critical information for more evidence-based decisions about the design, analysis, and interpretation of obesity RCTs.
The purpose of the research is to use meta-analytic methodology to develop this knowledge that would inform the design of randomized clinical trials for obesity treatment and prevention. It will entail comprehensively investigating the effect of run-in periods on subject drop-out rates (DORs) and weight loss;determining predictors of DORs, and estimating the association between the true population (not sample) treatment effect and the true population (not sample) mean weight loss of the control group. This research should give valuable answers and critical information for more evidence-based decisions about the design, analysis, and interpretation of obesity RCTs.
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