Association between a national behavioral weight management program and real-world weight change.

OBJECTIVE: In a national cohort of Veterans, weight change was compared between participants in a US Department of Veterans Affairs (VA) behavioral weight management program (MOVE!) and matched non-participants, and between high-intensity and low-intensity participants. METHODS: Retrospective cohort study of Veterans with 1 + MOVE! visits in 2008-2017 were matched to MOVE! non-participants via sequential stratification. Percent weight change up to two years after MOVE! initiation of participants and non-participants was modeled using generalized additive mixed models, and 1-year weight change of high-intensity or low-intensity participants was also compared. RESULTS: MOVE! participants (n = 499,696) and non-participant controls (n = 1,336,172) were well-matched, with an average age of 56 years and average BMI of 35. MOVE! participants lost 1.4 % at 12 months and 1.2 % at 24 months, which was 0.89 % points (95 % CI: 0.83-0.96) more at 12 months than non-participants and 0.55 % points (95 % CI: 0.41-0.68) more at 24 months. 9.1 % of MOVE! participants had high-intensity use in one year, and they had 2.38 % point (95 % CI: 2.25-2.52) greater weight loss than low-intensity participation at 12 months (2.8 % vs 0.4 %). CONCLUSIONS: Participation in VA's system-wide behavioral weight management program (MOVE!) was associated with modest weight loss, suggesting that program modifications are needed to increase Veteran engagement and program effectiveness. Future research should further explore how variations in program delivery and the use of newer anti-obesity medications may impact the program's effectiveness.

TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS.

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin, we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.