Quantifying and correcting bias due to outcome dependent self-reported weights in longitudinal study of weight loss interventions.

Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE

Original Publication: London : Nature Publishing Group, copyright 2011-

Research Design* ; Weight Loss*; Humans ; Bias ; Longitudinal Studies ; Self Report ; Randomized Controlled Trials as Topic

In response to the escalating global obesity crisis and its associated health and financial burdens, this paper presents a novel methodology for analyzing longitudinal weight loss data and assessing the effectiveness of financial incentives. Drawing from the Keep It Off trial-a three-arm randomized controlled study with 189 participants-we examined the potential impact of financial incentives on weight loss maintenance. Given that some participants choose not to weigh themselves because of small weight change or weight gains, which is a common phenomenon in many weight-loss studies, traditional methods, for example, the Generalized Estimating Equations (GEE) method tends to overestimate the effect size due to the assumption that data are missing completely at random. To address this challenge, we proposed a framework which can identify evidence of missing not at random and conduct bias correction using the estimating equation derived from pairwise composite likelihood. By analyzing the Keep It Off data, we found that the data in this trial are most likely characterized by non-random missingness. Notably, we also found that the enrollment time (i.e., duration time) would be positively associated with the weight loss maintenance after adjusting for the baseline participant characteristics (e.g., age, sex). Moreover, the lottery-based intervention was found to be more effective in weight loss maintenance compared with the direct payment intervention, though the difference was non-statistically significant. This framework's significance extends beyond weight loss research, offering a semi-parametric approach to assess missing data mechanisms and robustly explore associations between exposures (e.g., financial incentives) and key outcomes (e.g., weight loss maintenance). In essence, the proposed methodology provides a powerful toolkit for analyzing real-world longitudinal data, particularly in scenarios with data missing not at random, enriching comprehension of intricate dataset dynamics.
(© 2023. The Author(s).)

Erratum in: Sci Rep. 2023 Dec 18;13(1):22546. (PMID: 38110504)

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R56 AG069880 United States AG NIA NIH HHS; R01 LM014344 United States LM NLM NIH HHS; RF1 AG077820 United States AG NIA NIH HHS; R01 LM013519 United States LM NLM NIH HHS; P30 CA091842 United States CA NCI NIH HHS; R01 AI130460 United States AI NIAID NIH HHS; R21 AI167418 United States AI NIAID NIH HHS; U01 TR003709 United States TR NCATS NIH HHS; ME-2019C3-18315 United States PCORI Patient-Centered Outcomes Research Institute; R01 AG047820 United States AG NIA NIH HHS; R56 AG074604 United States AG NIA NIH HHS; R01 AG073435 United States AG NIA NIH HHS; R01 LM012607 United States LM NLM NIH HHS; R01 GM148494 United States GM NIGMS NIH HHS

Date Created: 20231105 Date Completed: 20231113 Latest Revision: 20240425

20240425

PMC10625563

10.1038/s41598-023-41853-4

37925516