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Janick Weberpals,1 Sudha R Raman,2 Pamela A Shaw,3 Hana Lee,4 Massimiliano Russo,1 Bradley G Hammill,2 Sengwee Toh,5 John G Connolly,5 Kimberly J Dandreo,6 Fang Tian,7 Wei Liu,7 Jie Li,7 José J Hernández-Muñoz,7 Robert J Glynn,1 Rishi J Desai1 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 2Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA; 3Biostatistics Division, Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA; 4Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA; 5Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA; 6Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, MA, USA; 7Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USACorrespondence: Janick Weberpals, Instructor in Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030-R, Boston, MA, 02120, USA, Tel +1 617-278-0932, Fax +1 617-232-8602, Email jweberpals@bwh.harvard.eduObjective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods.Methods: Three empirical sub-cohorts of diabetic SGLT2 or DPP4-inhibitor initiators with complete information on HbA1c, BMI and smoking as confounders of interest (COI) formed the basis of data simulation under a plasmode framework. A true null treatment effect, including the COI in the outcome generation model, and four missingness mechanisms for the COI were simulated: completely at random (MCAR), at random (MAR), and two not at random (MNAR) mechanisms, where missingness was dependent on an unmeasured confounder and on the value of the COI itself. We evaluated the ability of three groups of diagnostics to differentiate between mechanisms: 1)-differences in characteristics between patients with or without the observed COI (using averaged standardized mean differences [ASMD]), 2)-predictive ability of the missingness indicator based on observed covariates, and 3)-association of the missingness indicator with the outcome. We then compared analytic methods including “complete case”, inverse probability weighting, single and multiple imputation in their ability to recover true treatment effects.Results: The diagnostics successfully identified characteristic patterns of simulated missingness mechanisms. For MAR, but not MCAR, the patient characteristics showed substantial differences (median ASMD 0.20 vs 0.05) and consequently, discrimination of the prediction models for missingness was also higher (0.59 vs 0.50). For MNAR, but not MAR or MCAR, missingness was significantly associated with the outcome even in models adjusting for other observed covariates. Comparing analytic methods, multiple imputation using a random forest algorithm resulted in the lowest root-mean-squared-error.Conclusion: Principled diagnostics provided reliable insights into missingness mechanisms. When assumptions allow, multiple imputation with nonparametric models could help reduce bias.Keywords: electronic health records, missing data, diagnostics, imputation, analytics

Underreporting of energy consumption by self-report is well-recognized, but previous studies using recovery biomarkers have not been sufficiently large to establish whether participant characteristics predict misreporting. In 2004-2005, 544 participants in the Women's Health Initiative Dietary Modification Trial completed a doubly labeled water protocol (energy biomarker), 24-hour urine collection (protein biomarker), and self-reports of diet (assessed by food frequency questionnaire (FFQ)), exercise, and lifestyle habits; 111 women repeated all procedures after 6 months. Using linear regression, the authors estimated associations of participant characteristics with misreporting, defined as the extent to which the log ratio (self-reported FFQ/nutritional biomarker) was less than zero. Intervention women in the trial underreported energy intake by 32% (vs. 27% in the comparison arm) and protein intake by 15% (vs. 10%). Younger women had more underreporting of energy (p = 0.02) and protein (p = 0.001), while increasing body mass index predicted increased underreporting of energy and overreporting of percentage of energy derived from protein (p = 0.001 and p = 0.004, respectively). Blacks and Hispanics underreported more than did Caucasians. Correlations of initial measures with repeat measures (n = 111) were 0.72, 0.70, 0.46, and 0.64 for biomarker energy, FFQ energy, biomarker protein, and FFQ protein, respectively. Recovery biomarker data were used in regression equations to calibrate self-reports; the potential application of these equations to disease risk modeling is presented. The authors confirm the existence of systematic bias in dietary self-reports and provide methods of correcting for measurement error. [ABSTRACT FROM AUTHOR]

Background: Physical activity has been associated with a wide range of health benefits including long-term benefits for cognitive and brain health. Whether episodes of everyday physical activity are associated with immediate cognitive benefits remains unknown., Purpose: The purpose of the current study was to examine whether episodes of physical activity, occurring over the course of participants' daily lives, are associated with short-term improvements in cognitive health., Methods: Participants completed a 9-day ecological momentary assessment protocol involving 5 daily assessments of self-reported physical activity and ambulatory cognitive assessments of processing speed and visuospatial working memory. Data were analyzed in a multilevel modeling framework to explore changes in performance on each task associated with physical activity during the period leading up to the assessment as well as individual differences in average frequency of physical activity., Results: Results of MLMs indicated that engaging in physical activity during the period (~3.5 hr) leading up to an assessment was associated with improvements in processing speed equivalent to 4 years of cognitive aging. Such improvements were observed for both light and moderate-to-vigorous physical activity levels. No association was observed for visuospatial working memory accuracy; however, response time during the working memory task reliably mirrored the association observed for processing speed. The short-term benefits were observed, particularly, for individuals with an overall higher frequency of reported physical activity., Conclusions: Our findings suggest that engaging in everyday physical activity of any intensity level may have short-term, acute benefits for cognitive health and point to new potential targets for intervention., Clinical Trial Information: NCT03240406., (© Society of Behavioral Medicine 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background: Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example., Methods: We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP-4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015-2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications., Results: High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies., Conclusions: We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data., (© 2024. The Author(s).)

Background & Aims: Mailed outreach for colorectal cancer (CRC) screening increases uptake but it is unclear how to offer the choice of testing. We evaluated if the active choice between colonoscopy and fecal immunochemical test (FIT), or FIT alone, increased response compared with colonoscopy alone., Methods: This pragmatic, randomized, controlled trial at a community health center included patients between ages 50 and 74 who were not up to date with CRC screening. Patients were randomized 1:1:1 to the following: (1) colonoscopy only, (2) active choice of colonoscopy or FIT, or (3) FIT only. Patients received an outreach letter with instructions for testing (colonoscopy referral and/or an enclosed FIT kit), a reminder letter at 2 months, and another reminder at 3 to 5 months via text message or automated voice recording. The primary outcome was CRC screening completion within 6 months., Results: Among 738 patients in the final analysis, the mean age was 58.7 years (SD, 6.2 y); 48.6% were insured by Medicaid and 24.3% were insured by Medicare; and 71.7% were White, 16.9% were Black, and 7.3% were Hispanic/Latino. At 6 months, 5.6% (95% CI, 2.8-8.5) completed screening in the colonoscopy-only arm, 12.8% (95% CI, 8.6-17.0) in the active-choice arm, and 11.3% (95% CI, 7.4-15.3) in the FIT-only arm. Compared with colonoscopy only, there was a significant increase in screening in active choice (absolute difference, 7.1%; 95% CI, 2.0-12.2; P = .006) and FIT only (absolute difference, 5.7%; 95% CI, 0.8-10.6; P = .02)., Conclusions: Both choice of testing and FIT alone increased response and may align with patient preferences., Trial Registration: clinicaltrials.gov NCT04711473., (Copyright © 2024 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Background: Changes in sleep, physical activity and mental health were observed in older adults during early stages of the COVID-19 pandemic. Here we describe effects of the COVID-19 pandemic on older adult mental health, wellbeing, and lifestyle behaviors and explore predictors of better mid-pandemic mental health and wellbeing., Methods: Participants in the Adult Changes in Thought study completed measures of lifestyle behaviors (e.g., sleep, physical activity) and mental health and wellbeing both pre-pandemic during regular study visits and mid-pandemic via a one-time survey. We used paired t-tests to compare differences in these measures pre- vs. mid-pandemic. Using multivariate linear regression, we further explored demographic, health, and lifestyle predictors of pandemic depressive symptoms, social support, and fatigue. We additionally qualitatively coded free text data from the mid-pandemic survey for related comments., Results: Participants (N = 896) reported significant changes in mental health and lifestyle behaviors at pre-pandemic vs. mid-pandemic measurements (p < 0.0001). Qualitative findings supported these behavioral and wellbeing changes. Being male, never smoking, and lower pre-pandemic computer time and sleep disturbance were significantly associated with lower pandemic depressive symptoms. Being partnered, female, never smoking, and lower pre-pandemic sleep disturbance were associated with higher pandemic social support. Pre-pandemic employment, more walking, less computer time, and less sleep disturbance were associated with less pandemic fatigue. Participant comments supported these quantitative findings, highlighting gender differences in pandemic mental health, changes in computer usage and physical activity during the pandemic, the value of spousal social support, and links between sleep disturbance and mental health and wellbeing. Qualitative findings also revealed additional factors, such as stresses from personal and family health situations and the country's concurrent political environment, that impacted mental health and wellbeing., Conclusions: Several demographic, health, and lifestyle behaviors appeared to buffer the effects of the COVID-19 pandemic and may be key sources of resilience. Interventions and public health measures targeting men and unpartnered individuals could promote social support resilience, and intervening on modifiable behaviors like sleep quality, physical activity and sedentary activities like computer time may promote resilience to fatigue and depressive symptoms during future community stressor events. Further research into these relationships is warranted., (© 2024. The Author(s).)

Background: We examined whether trajectories of cognitive function over 10 years predict later-life physical activity (PA), sedentary time (ST), and sleep., Methods: Participants were from the Adult Changes in Thought (ACT) cohort study. We included 611 ACT participants who wore accelerometers and had 3+ measures of cognition in the 10 years prior to accelerometer wear. The Cognitive Assessment Screening Instrument (CASI) measured cognition and was scored using item-response theory (IRT). activPAL and ActiGraph accelerometers worn over 7 days measured ST and PA outcomes. Self-reported time in bed and sleep quality measured sleep outcomes. Analyses used growth mixture modeling to classify CASI-IRT scores into latent groups and examine associations with PA, ST, and sleep including demographic and health covariates., Results: Participants (Mean age = 80.3 (6.5) years, 90.3% White, 57.1% female, 29.3% had less than 16 years of education) fell into 3 latent trajectory groups: average stable CASI (56.1%), high stable CASI (34.0%), and declining CASI (9.8%). The declining group had 16 minutes less stepping time (95% confidence interval [95% CI]: 0.6, 31.4), 1 517 fewer steps per day (95% CI: 138, 2 896), and 16.3 minutes per day less moderate-to-vigorous PA (95% CI: 1.3, 31.3) compared to the average stable group. There were no associations between CASI trajectory and sedentary or sleep outcomes., Conclusions: Declining cognition predicted lower PA providing some evidence of a reverse relationship between PA and cognition in older adults. However, this conclusion is limited by having outcomes at only one time point, a nonrepresentative sample, self-reported sleep outcomes, and using a global cognition measure., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

NK cells have been shown to exhibit inflammatory and immunoregulatory functions in a variety of healthy and diseased settings. In the context of chronic viral infection and cancer, distinct NK cell populations that inhibit adaptive immune responses have been observed. To understand how these cells arise and further characterize their immunosuppressive role, we examined in vitro conditions that could polarize human NK cells into an inhibitory subset. TGF-β1 has been shown to induce regulatory T cells in vitro and in vivo; we therefore investigated if TGF-β1 could also induce immunosuppressive NK-like cells. First, we found that TGF-β1/IL-15, but not IL-15 alone, induced CD103+CD49a+ NK-like cells from peripheral blood NK cells, which expressed markers previously associated with inhibitory CD56+ innate lymphoid cells, including high expression of GITR and CD101. Moreover, supernatant from ascites collected from patients with ovarian carcinoma also induced CD103+CD49a+ NK-like cells in vitro in a TGF-β-dependent manner. Interestingly, TGF-β1/IL-15-induced CD103+CD56+ NK-like cells suppressed autologous CD4+ T cells in vitro by reducing absolute number, proliferation, and expression of activation marker CD25. Collectively, these findings provide new insight into how NK cells may acquire an inhibitory phenotype in TGF-β1-rich environments., (Copyright © 2024 by The American Association of Immunologists, Inc.)

The innate lymphoid cell (ILC) family is composed of heterogeneous innate effector and helper immune cells that preferentially reside in tissues where they promote tissue homeostasis. In cancer, they have been implicated in driving both pro- and anti-tumor responses. This apparent dichotomy highlights the need to better understand differences in the ILC composition and phenotype within different tumor types that could drive seemingly opposite anti-tumor responses. Here, we characterized the frequency and phenotype of various ILC subsets in melanoma metastases and primary epithelial ovarian tumors. We observed high PD-1 expression on ILC subsets isolated from epithelial ovarian tumor samples, while ILC populations in melanoma samples express higher levels of LAG-3. In addition, we found that the frequency of cytotoxic ILCs and NKp46 + ILC3 in tumors positively correlates with monocytic cells and conventional type 2 dendritic cells, revealing potentially new interconnected immune cell subsets in the tumor microenvironment. Consequently, these observations may have direct relevance to tumor microenvironment composition and how ILC subset may influence anti-tumor immunity., Competing Interests: Dr. Pamela Ohashi is on Scientific Advisory Boards for Providence Therapeutics, Treadwell Therapeutics, Tikva Allocell and Rondo Therapeutics. Inc. Dr. Ohashi holds a sponsored research agreement with Providence Therapeutics. No potential conflict of interest was reported by the other authors., (© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.)

In this paper, we present a practical approach for computing the sandwich variance estimator in 2-stage regression model settings. As a motivating example for 2-stage regression, we consider regression calibration, a popular approach for addressing covariate measurement error. The sandwich variance approach has rarely been applied in regression calibration, despite its requiring less computation time than popular resampling approaches for variance estimation, specifically the bootstrap. This is probably because it requires specialized statistical coding. Here we first outline the steps needed to compute the sandwich variance estimator. We then develop a convenient method of computation in R for sandwich variance estimation, which leverages standard regression model outputs and existing R functions and can be applied in the case of a simple random sample or complex survey design. We use a simulation study to compare the sandwich estimator to a resampling variance approach for both settings. Finally, we further compare these 2 variance estimation approaches in data examples from the Women's Health Initiative (1993-2005) and the Hispanic Community Health Study/Study of Latinos (2008-2011). In our simulations, the sandwich variance estimator typically had good numerical performance, but simple Wald bootstrap confidence intervals were unstable or overcovered in certain settings, particularly when there was high correlation between covariates or large measurement error., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Practical problems with missing data are common, and many methods have been developed concerning the validity and/or efficiency of statistical procedures. On a central focus, there have been longstanding interests on the mechanism governing data missingness, and correctly deciding the appropriate mechanism is crucially relevant for conducting proper practical investigations. In this paper, we present a new hypothesis testing approach for deciding between the conventional notions of missing at random and missing not at random in generalized linear models in the presence of instrumental variables. The foundational idea is to develop appropriate discrepancy measures between estimators whose properties significantly differ only when missing at random does not hold. We show that our testing approach achieves an objective data-oriented choice between missing at random or not. We demonstrate the feasibility, validity, and efficacy of the new test by theoretical analysis, simulation studies, and a real data analysis.

Introduction: Excessive maternal gestational weight gain (GWG) is strongly correlated with childhood obesity, yet how excess maternal weight gain and gestational diabetes mellitus (GDM) interact to affect early childhood obesity is poorly understood. The purpose of this study was to investigate whether overall and trimester-specific maternal GWG and GDM were associated with obesity in offspring by age 6 years., Methods: A cohort of 10,335 maternal-child dyads was established from electronic health records. Maternal weights at conception and delivery were estimated from weight trajectory fits using functional principal components analysis. Kaplan-Meier curves and Cox regression, together with generalized raking, examined time-to-childhood-obesity., Results: Obesity diagnosed prior to age 6 years was estimated at 19.7% (95% CI: 18.3, 21.1). Maternal weight gain during pregnancy was a strong predictor of early childhood obesity (p < 0.0001). The occurrence of early childhood obesity was lower among mothers with GDM compared with those without diabetes (adjusted hazard ratio = 0.58, p = 0.014). There was no interaction between maternal weight gain and GDM (p = 0.55). Higher weight gain during the first trimester was associated with lower risk of early childhood obesity (p = 0.0002) whereas higher weight gain during the second and third trimesters was associated with higher risk (p < 0.0001)., Discussion: Results indicated total and trimester-specific maternal weight gain was a strong predictor of early childhood obesity, though obesity risk by age 6 was lower for children of mothers with GDM. Additional research is needed to elucidate underlying mechanisms directly related to trimester-specific weight gain and GDM that impede or protect against obesity prevalence during early childhood., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objectives: Partially observed confounder data pose a major challenge in statistical analyses aimed to inform causal inference using electronic health records (EHRs). While analytic approaches such as imputation are available, assumptions on underlying missingness patterns and mechanisms must be verified. We aimed to develop a toolkit to streamline missing data diagnostics to guide choice of analytic approaches based on meeting necessary assumptions., Materials and Methods: We developed the smdi (structural missing data investigations) R package based on results of a previous simulation study which considered structural assumptions of common missing data mechanisms in EHR., Results: smdi enables users to run principled missing data investigations on partially observed confounders and implement functions to visualize, describe, and infer potential missingness patterns and mechanisms based on observed data., Conclusions: The smdi R package is freely available on CRAN and can provide valuable insights into underlying missingness patterns and mechanisms and thereby help improve the robustness of real-world evidence studies., Competing Interests: The FDA approved the study protocol, statistical analysis plan and reviewed and approved this manuscript. Coauthors from the FDA participated in the results interpretation and in the preparation and decision to submit the manuscript for publication. The FDA had no role in data collection, management, or analysis. The views expressed are those of the authors and not necessarily those of the US FDA. J.W. reports prior employment by Hoffmann-La Roche and previously held shares in Hoffmann-La Roche. P.A.S. is a named inventor on a patent licensed to Novartis by the University of Pennsylvania for an unrelated project. S.T. serves as a consultant for Pfizer, Inc. and TriNetX, LLC. R.J.G. has received research funding through his employer from Amarin, Kowa, Novartis, and Pfizer. R.J.D. reports serving as Principal Investigator on investigator-initiated grants to the Brigham and Women’s Hospital from Novartis, Vertex, and Bristol-Myers-Squibb on unrelated projects. All remaining authors report no disclosures or conflicts of interest., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Validation studies are often used to obtain more reliable information in settings with error-prone data. Validated data on a subsample of subjects can be used together with error-prone data on all subjects to improve estimation. In practice, more than one round of data validation may be required, and direct application of standard approaches for combining validation data into analyses may lead to inefficient estimators since the information available from intermediate validation steps is only partially considered or even completely ignored. In this paper, we present two novel extensions of multiple imputation and generalized raking estimators that make full use of all available data. We show through simulations that incorporating information from intermediate steps can lead to substantial gains in efficiency. This work is motivated by and illustrated in a study of contraceptive effectiveness among 83 671 women living with HIV, whose data were originally extracted from electronic medical records, of whom 4732 had their charts reviewed, and a subsequent 1210 also had a telephone interview to validate key study variables., (© 2023 John Wiley & Sons Ltd.)

A light-responsive polymer allowing the controlled release of camptothecin and the generation of reactive oxygen species (ROS) is reported. The polymer was prepared by controlled copolymerisation of water-soluble N , N -dimethyl acrylamide with a bromocoumarin methacrylate monomer. The lipophilic chemotherapy agent camptothecin was caged onto the coumarin unit via a photo-cleavable carbonate ester enabling light-triggered cargo release. The polymer showed good biocompatibility in the dark, and high cancer cell killing activity mediated both by the photo-release of camptothecin and ROS generation., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Aircraft noise can disrupt sleep and impair recuperation. The last U.S. investigation into the effects of aircraft noise on sleep dates back more than 20 years. Since then, traffic patterns and the noise levels produced by single aircraft have changed substantially. It is therefore important to acquire current data on sleep disturbance relative to varying degrees of aircraft noise exposure in the U.S. that can be used to check and potentially update the existing noise policy. This manuscript describes the design, procedures, and analytical approaches of the FAA's National Sleep Study. Seventy-seven U.S. airports with relevant nighttime air traffic from 39 states are included in the sampling frame. Based on simulation-based power calculations, the field study aims to recruit 400 participants from four noise strata and record an electrocardiogram (ECG), body movement, and sound pressure levels in the bedroom for five consecutive nights. The primary outcome of the study is an exposure-response function between the instantaneous, maximum A-weighted sound pressure levels (dBA) of individual aircraft measured in the bedroom and awakening probability inferred from changes in heart rate and body movement. Self-reported sleep disturbance due to aircraft noise is the secondary outcome that will be associated with long-term average noise exposure metrics such as the Day-Night Average Sound Level (DNL) and the Nighttime Equivalent Sound Level ( L night ). The effect of aircraft noise on several other physiological and self-report outcomes will also be investigated. This study will provide key insights into the effects of aircraft noise on objectively and subjectively assessed sleep disturbance.

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).)

The conjugation of a fluorophore and a variety of cell-penetrating peptides onto a RAFT agent allowed for the synthesis of polymers of defined sizes with quantifiable cell-uptake. Each peptide-RAFT agent was used to polymerize acrylamide, acrylate, and styrene monomers to form high or low molecular weight polymers (here 50 or 7.5 kDa) with the peptide having no influence on the RAFT agent's control. The incorporation of a single fluorophore per polymer chain allowed cellular analysis of the uptake of the size-specific peptide-polymers via flow cytometry and confocal microscopy. The cell-penetrating peptides had a direct effect on the efficiency of polymer uptake for both high and low molecular weight polymers, demonstrating the versatility of the strategy. These "all-in-one", synthetically accessible RAFT agents allow highly controlled preparation of synthetic peptide-polymer conjugates and subsequent quantification of their delivery into cells.

HIV pre-exposure prophylaxis (PrEP) uptake among cisgender women in the United States is low. Just4Us, a theory-based counseling and navigation intervention, was evaluated in a pilot randomized controlled trial among PrEP-eligible women (n = 83). The comparison arm was a brief information session. Women completed surveys at baseline, post-intervention, and at three months. In this sample, 79% were Black, and 26% were Latina. This report presents results on preliminary efficacy. At 3 months follow-up, 45% made an appointment to see a provider about PrEP; only 13% received a PrEP prescription. There were no differences in PrEP initiation by study arm (9% Info vs. 11% Just4Us). PrEP knowledge was significantly higher in the Just4Us group at post-intervention. Analysis revealed high PrEP interest with many personal and structural barriers along the PrEP continuum. Just4Us is a promising PrEP uptake intervention for cisgender women. Further research is needed to tailor intervention strategies to multilevel barriers.Clinicaltrials.gov registration NCT03699722: A Women-Focused PrEP Intervention (Just4Us)., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Electronic health record (EHR) data are increasingly used for biomedical research, but these data have recognized data quality challenges. Data validation is necessary to use EHR data with confidence, but limited resources typically make complete data validation impossible. Using EHR data, we illustrate prospective, multiwave, two-phase validation sampling to estimate the association between maternal weight gain during pregnancy and the risks of her child developing obesity or asthma. The optimal validation sampling design depends on the unknown efficient influence functions of regression coefficients of interest. In the first wave of our multiwave validation design, we estimate the influence function using the unvalidated (phase 1) data to determine our validation sample; then in subsequent waves, we re-estimate the influence function using validated (phase 2) data and update our sampling. For efficiency, estimation combines obesity and asthma sampling frames while calibrating sampling weights using generalized raking. We validated 996 of 10,335 mother-child EHR dyads in six sampling waves. Estimated associations between childhood obesity/asthma and maternal weight gain, as well as other covariates, are compared to naïve estimates that only use unvalidated data. In some cases, estimates markedly differ, underscoring the importance of efficient validation sampling to obtain accurate estimates incorporating validated data., (© 2022 The International Biometric Society.)

Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider when applying regression calibration, including: 1) how to develop the calibration equation and which covariates to include; 2) valid ways to calculate standard errors of estimated regression coefficients; and 3) problems arising if one of the covariates in the calibration model is a mediator of the relationship between the exposure and outcome. Throughout, we provide illustrative examples using data from the Hispanic Community Health Study/Study of Latinos (United States, 2008-2011) and simulations. We conclude with recommendations for how to perform regression calibration., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2023. This work is written by (a) US Government employee(s) and is in the public domain in the US.)

Measurement error is a major issue in self-reported diet that can distort diet-disease relationships. Use of blood concentration biomarkers has the potential to mitigate the subjective bias inherent in self-reporting. As part of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) baseline visit (2008-2011), self-reported information on diet was collected from all participants (n = 16,415). The HCHS/SOL also included annual telephone follow-up, as well as a second (2014-2017) and third (2020-2023) clinic visit. Blood concentration biomarkers for carotenoids, tocopherols, retinol, vitamin B12, and folate were measured in a subset of participants (n = 476) as part of the Study of Latinos: Nutrition and Physical Activity Assessment Study (SOLNAS) (2010-2012). We examined the relationships among biomarker levels, self-reported intake, Hispanic/Latino background (Central American, Cuban, Dominican, Mexican, Puerto Rican, or South American), and other participant characteristics in this diverse cohort. We built regression calibration-based prediction equations for 10 nutritional biomarkers and used a simulation to study the power of detecting a diet-disease association in a multivariable Cox model using a predicted concentration level. Good statistical power was observed for some nutrients with high prediction model R2 values, but further research is needed to understand how best to realize the potential of these dietary biomarkers. This study provides a comprehensive examination of several nutritional biomarkers within the HCHS/SOL, characterizing their associations with subject characteristics and the influence of the measurement characteristics on the power to detect associations with health outcomes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In large epidemiologic studies, it is typical for an inexpensive, non-invasive procedure to be used to record disease status during regular follow-up visits, with less frequent assessment by a gold standard test. Inexpensive outcome measures like self-reported disease status are practical to obtain, but can be error-prone. Association analysis reliant on error-prone outcomes may lead to biased results; however, restricting analyses to only data from the less frequently observed error-free outcome could be inefficient. We have developed an augmented likelihood that incorporates data from both error-prone outcomes and a gold standard assessment. We conduct a numerical study to show how we can improve statistical efficiency by using the proposed method over standard approaches for interval-censored survival data that do not leverage auxiliary data. We extend this method for the complex survey design setting so that it can be applied in our motivating data example. Our method is applied to data from the Hispanic Community Health Study/Study of Latinos to assess the association between energy and protein intake and the risk of incident diabetes. In our application, we demonstrate how our method can be used in combination with regression calibration to additionally address the covariate measurement error in self-reported diet.

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wu, Rosenberg, Greenwood-Hickman, McCurry, Proust-Lima, Nelson, Crane, LaCroix, Larson and Shaw.)

Background: Molecular stable isotope ratios are a novel type of dietary biomarker with high sensitivity and specificity for certain foods. Among these, fatty acid carbon isotope ratios (CIRs) have strong potential but have not been investigated as dietary biomarkers., Objectives: We evaluated whether fatty acid CIRs and mass proportions were associated with meat, fish, and sugar-sweetened beverage (SSB) intake., Methods: Thirty-two men [aged 46.2 ± 10.5 y; BMI (kg/m2): 27.2 ± 4.0] underwent a 12-wk inpatient dietary intervention at the National Institute of Diabetes and Digestive and Kidney Diseases in Phoenix, Arizona. Men were randomly assigned to 1 of 8 dietary treatments varying the presence/absence of dietary meat, fish, and SSBs in all combinations. Fatty acid CIRs and mass proportions were measured in fasting blood samples and adipose tissue biopsies that were collected pre- and postintervention. Dietary effects were analyzed using multivariable regression and receiver operating characteristic AUCs were calculated using logistic regression., Results: CIRs of the several abundant SFAs, MUFAs and arachidonic acid (20:4n-6) in plasma were strongly associated with meat, as were a subset of these fatty acids in RBCs. Effect sizes in plasma ranged from 1.01‰ to 1.93‰ and were similar but attenuated in RBCs. Mass proportions of those fatty acids were not associated with diet. CIRs of plasma dihomo-γ-linolenic acid (20:3n-6) and adipose palmitic acid (16:0) were weakly associated with SSBs. Mass proportions of plasma odd-chain fatty acids were associated with meat, and mass proportions of plasma EPA and DHA (20:5n-3 and 22:6n-3) were associated with fish., Conclusions: CIRs of plasma and RBC fatty acids show promise as sensitive and specific measures of dietary meat. These provide different information from that provided by fatty acid mass proportions, and are informative where mass proportion is not. This trial is registered at www.clinicaltrials.gov as NCT01237093., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Emerging evidence indicates that healthy dietary patterns are associated with higher cognitive status; however, few clinical trials have explored this association in diverse middle-aged adults before the onset of cognitive decline. We use novel ambulatory methods to assess cognition in natural settings in tandem with diet recording., Aims: We investigate whether the Multicultural Healthy Diet Study to Reduce Cognitive Decline & Alzheimer's Disease Risk, a pilot randomized controlled trial of an anti-inflammatory dietary pattern compared to usual diet, can mitigate cognitive decline and Alzheimer's Disease risk in a diverse population of 40-65 year old adults in Bronx, New York., Methods: Primary cognitive outcomes assessed at nine months are collected in an ecological momentary assessment "measurement burst" design, over the course of participants' daily lives. These ultra-brief, ambulatory cognitive assessments examine processing speed, visuospatial working memory, short-term associative memory binding, long-term associative memory, and working memory capacity. Key secondary outcomes relate to comparing dietary intake between study arms with respect to cognitive outcomes. We assess diet with food records using the National Cancer Institute's Automated Self-Administered 24-h record and serum biomarkers. We further investigate the association of self-reported diet and dietary biomarkers with inflammatory-based biomarkers., Conclusion: This randomized controlled trial of diet and cognition for the first time combines novel measures of ambulatory cognitive assessment with web-based assessment of dietary intake recording. This new approach enabled the study to continue in the midst of the COVID-19 pandemic in remote format., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Inc. All rights reserved.)