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Extended-care interventions have been demonstrated to improve maintenance of weight loss after the end of initial obesity treatment; however, it is unclear whether these programs are similarly effective for African American versus White participants. The current study examined differences in effectiveness of individual versus group telephone-based extended-care on weight regain, compared to educational control, in 410 African American (n=82) and White (n=328) adults with obesity (mean ± SD age=55.6±10.3years, BMI= 36.4±3.7kg/m(2)). After controlling for initial weight loss, multivariate linear models demonstrated a significant interaction between treatment condition and race, p=.048. Randomization to the individual telephone condition produced the least amount of weight regain in White participants, while the group condition produced the least amount of weight regain in African American participants. Future research should investigate the role of social support in regain for African American versus White participants and examine whether tailoring delivery format by race may improve long-term outcomes.

Background and Objectives:Duchenne muscular dystrophy (DMD) is a progressive muscle degenerative disorder with a well-characterized disease phenotype but considerable interindividual heterogeneity that is not well understood. The aim of the study was to evaluate the effects of dystrophin mutations and genetic modifiers of DMD on rate and age of muscle replacement by fat.Methods:175 corticosteroid treated participants from the ImagingDMD natural history study underwent repeated magnetic resonance spectroscopy (MRS) of the vastus lateralis (VL) and soleus (SOL) to determine muscle fat fraction. MRS was performed annually in the majority of instances; however, some individuals had additional visits at 3 or 6 month intervals. Fat fraction changes over time were modeled using nonlinear mixed effects to estimate disease trajectories based on the age that the VL or SOL reached half-maximum change in fat fraction (mu) and the time required for fat fraction change (sigma). Computed mu and sigma values were evaluated for dystrophin mutations that have demonstrated the ability to lead to a mild phenotype as well as compared between different genetic polymorphism groups.Results:Participants with dystrophin gene deletions amenable to exon 8 skipping (n=4) had minimal increases in SOL fat fraction and had an increase in VL mu value by 4.4 years compared to a reference cohort (p=0.039). Participants with nonsense mutations within exons that may produce milder phenotypes (n=11) also had minimal increases in SOL and VL fat fractions. No differences in estimated fat fraction trajectories were seen for individuals amenable to exon 44 skipping (n=10). Modeling of the SPP1, LTBP4, and THBS1 genetic modifiers did not result in significant differences in muscle fat fraction trajectories between genotype groups (p>0.05); however, trends were noted for the polymorphisms associated with long-range regulation of LTBP4 and THBS1 that deserve further follow-up.Discussion:The results of this study link the historically mild phenotypes seen in individuals amenable to exon 8 skipping and with certain nonsense mutations with alterations in trajectories of lower extremity muscle replacement by fat.

While electronic health records data provide unique opportunities for research, numerous methodological issues must be considered. Among these, selection bias due to incomplete/missing data has received far less attention than other issues. Unfortunately, standard missing data approaches (e.g. inverse-probability weighting and multiple imputation) generally fail to acknowledge the complex interplay of heterogeneous decisions made by patients, providers, and health systems that govern whether specific data elements in the electronic health records are observed. This, in turn, renders the missing-at-random assumption difficult to believe in standard approaches. In the clinical literature, the collection of decisions that gives rise to the observed data is referred to as the data provenance. Building on a recently-proposed framework for modularizing the data provenance, we develop a general and scalable framework for estimation and inference with respect to regression models based on inverse-probability weighting that allows for a hierarchy of missingness mechanisms to better align with the complex nature of electronic health records data. We show that the proposed estimator is consistent and asymptotically Normal, derive the form of the asymptotic variance, and propose two consistent estimators. Simulations show that naïve application of standard methods may yield biased point estimates, that the proposed estimators have good small-sample properties, and that researchers may have to contend with a bias-variance trade-off as they consider how to handle missing data. The proposed methods are motivated by an on-going, electronic health records-based study of bariatric surgery.

Gestational weight gain (GWG) and anthropometric trajectories may affect foetal programming and are potentially modifiable.To assess concomitant patterns of change in weight, circumferences and adiposity across gestation as an integrated prenatal exposure, and determine how they relate to neonatal body composition.Data are from a prospective cohort of singleton pregnancies (n = 2182) enrolled in United States perinatal centres, 2009-2013. Overall and by prepregnancy BMI group (overweight/obesity and healthy weight), joint latent trajectory models were fit with prenatal weight, mid-upper arm circumference (MUAC), triceps (TSF) and subscapular (SSF) skinfolds. Differences in neonatal body composition by trajectory class were assessed via weighted least squares.Six trajectory patterns reflecting co-occurring changes in weight and MUAC, SSF and TSF across pregnancy were identified overall and by body mass index (BMI) group. Among people with a healthy weight BMI, some differences were observed for neonatal subcutaneous adipose tissue, and among individuals with overweight/obesity some differences in neonatal lean mass were found. Neonatal adiposity measures were higher among infants born to individuals with prepregnancy overweight/obesity.Six integrated trajectory patterns of prenatal weight, subcutaneous adipose tissue and circumferences were observed that were minimally associated with neonatal body composition, suggesting a stronger influence of prepregnancy BMI.

OBJECTIVE Emerging data from animal and human pilot studies suggest potential benefits of glucagon-like peptide 1 receptor agonists (GLP-1RA) on lung function. We aimed to assess the association of GLP-1RA and chronic lower respiratory disease (CLRD) exacerbation in a population with comorbid type 2 diabetes (T2D) and CLRD. RESEARCH DESIGN AND METHODS A new-user active-comparator analysis was conducted with use of a national claims database of beneficiaries with employer-sponsored health insurance spanning 2005–2017. We included adults with T2D and CLRD who initiated GLP-1RA or dipeptidyl peptidase 4 inhibitors (DPP-4I) as an add-on therapy to their antidiabetes regimen. The primary outcome was time to first hospital admission for CLRD. The secondary outcome was a count of any CLRD exacerbation associated with an inpatient or outpatient visit. We estimated incidence rates using inverse probability of treatment weighting for each study group and compared via risk ratios. RESULTS The study sample consisted of 4,150 GLP-1RA and 12,540 DPP-4I new users with comorbid T2D and CLRD. The adjusted incidence rate of first CLRD admission during follow-up was 10.7 and 20.3 per 1,000 person-years for GLP-1RA and DPP-4I users, respectively, resulting in an adjusted hazard ratio of 0.52 (95% CI 0.32–0.85). For the secondary outcome, the adjusted incidence rate ratio was 0.70 (95% CI 0.57–0.87). CONCLUSIONS GLP-1RA users had fewer CLRD exacerbations in comparison with DPP-4I users. Considering both plausible mechanistic pathways and this real-world evidence, potential beneficial effects of GLP-1RA may be considered in selection of an antidiabetes treatment regimen. Randomized clinical trials are warranted to confirm our findings.

As the population of the older individuals continues to grow, it is important to study the relationship among the variables measuring financial health and physical health of the older individuals t...

Background Rural Americans have higher prevalence of obesity and type 2 diabetes (T2D) than urban populations and more limited access to behavioral programs to promote healthy lifestyle habits. Descriptive evidence from the Rural Lifestyle Intervention Treatment Effectiveness trial delivered through local cooperative extension service offices in rural areas previously identified that behavioral modification with both nutrition education and coaching resulted in a lower program delivery cost per kilogram of weight loss maintained at 2-years compared with an education-only comparator intervention. Objective This analysis extended earlier Rural Lifestyle Intervention Treatment Effectiveness trial research regarding weight loss outcomes to assess whether nutrition education with behavioral coaching delivered through cooperative extension service offices is cost-effective relative to nutrition education only in reducing T2D cases in rural areas. Design A cost-utility analysis was conducted. Participants/setting Trial participants (n=317) from June 2008 through June 2014 were adults residing in rural Florida counties with a baseline body mass index between 30 and 45, but otherwise identified as healthy. Intervention Trial participants were randomly assigned to low, moderate, or high doses of behavioral coaching with nutrition education (ie, 16, 32, or 48 sessions over 24 months) or a comparator intervention that included 16 sessions of nutrition education without coaching. Participant glycated hemoglobin level was measured at baseline and the end of the trial to assess T2D status. Main outcome measures T2D categories by treatment arm were used to estimate participants’ expected annual health care expenditures and expected health-related utility measured as quality adjusted life years (ie, QALYs) over a 5-year time horizon. Discounted incremental costs and QALYs were used to calculate incremental cost-effectiveness ratios for each behavioral coaching intervention dose relative to the education-only comparator. Statistical analyses performed Using a third-party payer perspective, Markov transition matrices were used to model participant transitions between T2D states. Replications of the individual participant behavior were conducted using Monte Carlo simulation. Results All three doses of the behavioral coaching intervention had lower expected total costs and higher estimated QALYs than the education-only comparator. The moderate dose behavioral coaching intervention was associated with higher estimated QALYs but was costlier than the low dose; the moderate dose was favored over the low dose with willingness to pay thresholds over $107,895/QALY. The low dose behavioral coaching intervention was otherwise favored. Conclusions Because most rural Americans live in counties with cooperative extension service offices, nutrition education with behavioral coaching programs similar to those delivered through this trial may be effective and efficient in preventing or delaying T2D-associated consequences of obesity for rural adults.

BACKGROUND: Upper extremity MRI and proton MR spectroscopy are increasingly considered to be outcome measures in Duchenne muscular dystrophy (DMD) clinical trials. PURPOSE: To demonstrate the feasibility of acquiring upper extremity MRI and proton ((1)H) MR spectroscopy measures of T2 and fat fraction in a large, multicenter cohort (ImagingDMD) of ambulatory and nonambulatory individuals with DMD; compare upper and lower extremity muscles by using MRI and (1)H MR spectroscopy; and correlate upper extremity MRI and (1)H MR spectroscopy measures to function. MATERIALS AND METHODS: In this prospective cross-sectional study, MRI and (1)H MR spectroscopy and functional assessment data were acquired from participants with DMD and unaffected control participants at three centers (from January 28, 2016, to April 24, 2018). T2 maps of the shoulder, upper arm, forearm, thigh, and calf were generated from a spin-echo sequence (repetition time msec/echo time msec, 3000/20–320). Fat fraction maps were generated from chemical shift-encoded imaging (eight echo times). Fat fraction and (1)H(2)O T2 in the deltoid and biceps brachii were measured from single-voxel (1)H MR spectroscopy (9000/11–243). Groups were compared by using Mann-Whitney test, and relationships between MRI and (1)H MR spectroscopy and arm function were assessed by using Spearman correlation. RESULTS: This study evaluated 119 male participants with DMD (mean age, 12 years ± 3 [standard deviation]) and 38 unaffected male control participants (mean age, 12 years ± 3). Deltoid and biceps brachii muscles were different in participants with DMD versus control participants in all age groups by using quantitative T2 MRI (P < .001) and (1)H MR spectroscopy fat fraction (P < .05). The deltoid, biceps brachii, and triceps brachii were affected to the same extent (P > .05) as the soleus and medial gastrocnemius. Negative correlations were observed between arm function and MRI (T2: range among muscles, ρ = −0.53 to −0.73 [P < .01]; fat fraction, ρ = −0.49 to −0.70 [P < .01]) and (1)H MR spectroscopy fat fraction (ρ = −0.64 to −0.71; P < .01). CONCLUSION: This multicenter study demonstrated early and progressive involvement of upper extremity muscles in Duchenne muscular dystrophy (DMD) and showed the feasibility of MRI and (1)H MR spectroscopy to track disease progression over a wide range of ages in participants with DMD. © RSNA, 2020 Online supplemental material is available for this article.

ObjectiveTo quantify disease progression in individuals with Duchenne muscular dystrophy (DMD) using magnetic resonance biomarkers of leg muscles.MethodsMRI and magnetic resonance spectroscopy (MRS) biomarkers were acquired from 104 participants with DMD and 51 healthy controls using a prospective observational study design with patients with DMD followed up yearly for up to 6 years. Fat fractions (FFs) in vastus lateralis and soleus muscles were determined with 1H MRS. MRI quantitative T2 (qT2) values were measured for 3 muscles of the upper leg and 5 muscles of the lower leg. Longitudinal changes in biomarkers were modeled with a cumulative distribution function using a nonlinear mixed-effects approach.ResultsMRS FF and MRI qT2 increased with DMD disease duration, with the progression time constants differing markedly between individuals and across muscles. The average age at half-maximal muscle involvement (μ) occurred 4.8 years earlier in vastus lateralis than soleus, and these measures were strongly associated with loss-of-ambulation age. Corticosteroid treatment was found to delay μ by 2.5 years on average across muscles, although there were marked differences between muscles with more slowly progressing muscles showing larger delay.ConclusionsMRS FF and MRI qT2 provide sensitive noninvasive measures of DMD progression. Modeling changes in these biomarkers across multiple muscles can be used to detect and monitor the therapeutic effects of corticosteroids on disease progression and to provide prognostic information on functional outcomes. This modeling approach provides a method to transform these MRI biomarkers into well-understood metrics, allowing concise summaries of DMD disease progression at individual and population levels.ClinicalTrials.gov identifier:NCT01484678.

Summary Trial-based economic evaluations are typically performed on cross-sectional variables, derived from the responses for only the completers in the study, using methods that ignore the complexities of utility and cost data (e.g. skewness and spikes). We present an alternative and more efficient Bayesian parametric approach to handle missing longitudinal outcomes in economic evaluations, while accounting for the complexities of the data. We specify a flexible parametric model for the observed data and partially identify the distribution of the missing data with partial identifying restrictions and sensitivity parameters. We explore alternative non-ignorable missingness scenarios through different priors for the sensitivity parameters, calibrated on the observed data. Our approach is motivated by, and applied to, data from a trial assessing the cost-effectiveness of a new treatment for intellectual disability and challenging behaviour.

Aberrant liver sirtuin 1 (SIRT1), a mammalian NAD(+)-dependent protein deacetylase, is implicated in the pathogenesis of alcoholic liver disease (ALD). However, the role of intestinal SIRT1 in ALD is presently unknown. This study investigated the involvement of intestine-specific SIRT1 in ethanol-induced liver dysfunction in mice. Ethanol feeding studies were performed on knockout mice with intestinal-specific SIRT1 deletion [SIRT1i knockout (KO)] and flox control [wild-type (WT)] mice with a chronic–plus-binge ethanol feeding protocol. After ethanol administration, hepatic inflammation and liver injury were substantially attenuated in the SIRT1iKO mice compared with the WT mice, suggesting that intestinal SIRT1 played a detrimental role in the ethanol-induced liver injury. Mechanistically, the hepatic protective effect of intestinal SIRT1 deficiency was attributable to ameliorated dysfunctional iron metabolism, increased hepatic glutathione contents, and attenuated lipid peroxidation, along with inhibition of a panel of genes implicated in the ferroptosis process in the livers of ethanol-fed mice. This study demonstrates that ablation of intestinal SIRT1 protected mice from the ethanol-induced inflammation and liver damage. The protective effects of intestinal SIRT1 deficiency are mediated, at least partially, by mitigating hepatic ferroptosis. Targeting intestinal SIRT1 or dampening hepatic ferroptosis signaling may have therapeutic potential for ALD in humans.

Background: Muscles of boys with Duchenne muscular dystrophy (DMD) are progressively replaced by fatty fibrous tissues, and weakness leads to loss of ambulation (LoA). Step activity (SA) monitoring is a quantitative measure of real-world ambulatory function. The relationship between quality of muscle health and SA is unknown in DMD. Objective: To determine SA in steroid treated boys with DMD across various age groups, and to evaluate the association of SA with quality of muscle health and ambulatory function. Methods: Quality of muscle health was measured by magnetic resonance (MR) imaging transverse magnetization relaxation time constant (MRI-T2) and MR spectroscopy fat fraction (MRS-FF). SA was assessed via accelerometry, and functional abilities were assessed through clinical walking tests. Correlations between SA, MR, and functional measures were determined. A threshold value of SA was determined to predict the future LoA. Results: The greatest reduction in SA was observed in the 9–

Background: Joint contractures are common in boys and men with Duchenne muscular dystrophy (DMD), and management of contractures is an important part of care. The optimal methods to prevent and treat contractures are controversial, and the natural history of contracture development is understudied in glucocorticoid treated individuals at joints beyond the ankle. Objective: To describe the development of contractures over time in a large cohort of individuals with DMD in relation to ambulatory ability, functional performance, and muscle quality measured using magnetic resonance imaging (MRI) and spectroscopy (MRS). Methods: In this longitudinal study, range of motion (ROM) was measured annually at the hip, knee, and ankle, and at the elbow, forearm, and wrist at a subset of visits. Ambulatory function (10 meter walk/run and 6 minute walk test) and MR-determined muscle quality (transverse relaxation time (T2) and fat fraction) were measured at each visit. Results: In 178 boys with DMD, contracture prevalence and severity increased with age. Among ambulatory participants, more severe contractures (defined as greater loss of ROM) were significantly associated with worse ambulatory function, and across all participants, more severe contractures significantly associated with higher MRI T2 or MRS FF (ρ: 0.40–0.61 in the lower extremity; 0.20–0.47 in the upper extremity). Agonist/antagonist differences in MRI T2 were not strong predictors of ROM. Conclusions: Contracture severity increases with disease progression (increasing age and muscle involvement and decreasing functional ability), but is only moderately predicted by muscle fatty infiltration and MRI T2, suggesting that other changes in the muscle, tendon, or joint contribute meaningfully to contracture formation in DMD.

Background The lack of dystrophin in cardiomyocytes in Duchenne muscular dystrophy (DMD) is associated with progressive decline in cardiac function eventually leading to death by 20–40 years of age. The aim of this prospective study was to determine rate of progressive decline in left ventricular (LV) function in Duchenne muscular dystrophy (DMD) over 5 years. Methods Short axis cine and grid tagged images of the LV were acquired in individuals with DMD (n = 59; age = 5.3–18.0 years) yearly, and healthy controls at baseline (n = 16, age = 6.0–18.3 years) on a 3 T MRI scanner. Grid-tagged images were analyzed for composite circumferential strain (ℇcc%) and ℇcc% in six mid LV segments. Cine images were analyzed for left ventricular ejection fraction (LVEF), LV mass (LVM), end-diastolic volume (EDV), end-systolic volume (ESV), LV atrioventricular plane displacement (LVAPD), and circumferential uniformity ratio estimate (CURE). LVM, EDV, and ESV were normalized to body surface area for a normalized index of LVM (LVMI), EDV (EDVI) and ESV (ESVI). Results At baseline, LV ℇcc% was significantly worse in DMD compared to controls and five of the six mid LV segments demonstrated abnormal strain in DMD. Longitudinal measurements revealed that ℇcc% consistently declined in individuals with DMD with the inferior segments being more affected. LVEF progressively declined between 3 to 5 years post baseline visit. In a multivariate analysis, the use of cardioprotective drugs trended towards positively impacting cardiac measures while loss of ambulation and baseline age were associated with negative impact. Eight out of 17 cardiac parameters reached a minimal clinically important difference with a threshold of 1/3 standard deviation. Conclusion The study shows a worsening of circumferential strain in dystrophic myocardium. The findings emphasize the significance of early and longitudinal assessment of cardiac function in DMD and identify early biomarkers of cardiac dysfunction to help design clinical trials to mitigate cardiac pathology. This study provides valuable non-invasive and non-contrast based natural history data of cardiac changes which can be used to design clinical trials or interpret the results of current trials aimed at mitigating the effects of decreased cardiac function in DMD.

BACKGROUND: Expiratory muscle weakness and impaired airway clearance are early signs of respiratory dysfunction in Duchenne muscular dystrophy (DMD), a degenerative muscle disorder in which muscle cells are damaged and replaced by fibrofatty tissue. Little is known about expiratory muscle pathology and its relationship to cough and airway clearance capacity; however, the level of muscle replacement by fat can be estimated using MRI and expressed as a fat fraction (FF). RESEARCH QUESTION: How does abdominal expiratory muscle fatty infiltration change over time in DMD and relate to clinical expiratory function? STUDY DESIGN AND METHODS: Individuals with DMD underwent longitudinal MRI of the abdomen to determine FF in the internal oblique, external oblique, and rectus abdominis expiratory muscles. FF data were used to estimate a model of expiratory muscle degeneration by using nonlinear mixed effects and a cumulative distribution function. FVC, maximal inspiratory and expiratory pressures, and peak cough flow were collected as clinical correlates to MRI. RESULTS: Forty individuals with DMD (aged 6-18 years at baseline) participated in up to five visits over 36 months. Modeling estimated the internal oblique progresses most quickly and reached 50% replacement by fat at a mean patient age of 13.0 years (external oblique, 14.0 years; rectus abdominis, 16.2 years). Corticosteroid-untreated individuals (n = 4) reached 50% muscle replacement by fat 3 to 4 years prior to treated individuals. Individuals with mild clinical dystrophic phenotypes (n = 3) reached 50% muscle replacement by fat 4 to 5 years later than corticosteroid-treated individuals. Internal and external oblique FFs near 50% were associated with maximal expiratory pressures < 60 cm H(2)O and peak cough flows < 270 L/min. INTERPRETATION: These data improve understanding of the early phase of respiratory compromise in DMD, which typically presents as airway clearance dysfunction prior to the onset of hypoventilation, and links expiratory muscle fatty infiltration to pulmonary function measures.

We develop a semiparametric Bayesian approach to missing outcome data in longitudinal studies in the presence of auxiliary covariates. We consider a joint model for the full data response, missingness and auxiliary covariates. We include auxiliary covariates to “move” the missingness “closer” to missing at random (MAR). In particular, we specify a semiparametric Bayesian model for the observed data via Gaussian process priors and Bayesian additive regression trees. These model specifications allow us to capture non-linear and non-additive effects, in contrast to existing parametric methods. We then separately specify the conditional distribution of the missing data response given the observed data response, missingness and auxiliary covariates (i.e. the extrapolation distribution) using identifying restrictions. We introduce meaningful sensitivity parameters that allow for a simple sensitivity analysis. Informative priors on those sensitivity parameters can be elicited from subject-matter experts. We use Monte Carlo integration to compute the full data estimands. Performance of our approach is assessed using simulated datasets. Our methodology is motivated by, and applied to, data from a clinical trial on treatments for schizophrenia.

In instrumental variable studies, missing instrument data are very common. For example, in the Wisconsin Longitudinal Study, one can use genotype data as a Mendelian randomization–style instrument, but this information is often missing when subjects do not contribute saliva samples or when the genotyping platform output is ambiguous. Here we review missing at random assumptions one can use to identify instrumental variable causal effects, and discuss various approaches for estimation and inference. We consider likelihood-based methods, regression and weighting estimators, and doubly robust estimators. The likelihood-based methods yield the most precise inference and are optimal under the model assumptions, while the doubly robust estimators can attain the nonparametric efficiency bound while allowing flexible nonparametric estimation of nuisance functions (e.g., instrument propensity scores). The regression and weighting estimators can sometimes be easiest to describe and implement. Our main contribution is an extensive review of this wide array of estimators under varied missing-at-random assumptions, along with discussion of asymptotic properties and inferential tools. We also implement many of the estimators in an analysis of the Wisconsin Longitudinal Study, to study effects of impaired cognitive functioning on depression.

Causal inference with observational longitudinal data and time-varying exposures is often complicated by time-dependent confounding and attrition. The G-computation formula is one approach for estimating a causal effect in this setting. The parametric modelling approach typically used in practice relies on strong modelling assumptions for valid inference and moreover depends on an assumption of missing at random, which is not appropriate when the missingness is missing not at random (MNAR) or due to death. In this work we develop a flexible Bayesian semi-parametric G-computation approach for assessing the causal effect on the subpopulation that would survive irrespective of exposure, in a setting with MNAR dropout. The approach is to specify models for the observed data using Bayesian additive regression trees, and then, use assumptions with embedded sensitivity parameters to identify and estimate the causal effect. The proposed approach is motivated by a longitudinal cohort study on cognition, health and ageing and we apply our approach to study the effect of becoming a widow on memory. We also compare our approach to several standard methods.

Summary Studies of memory trajectories using longitudinal data often result in highly nonrepresentative samples due to selective study enrollment and attrition. An additional bias comes from practice effects that result in improved or maintained performance due to familiarity with test content or context. These challenges may bias study findings and severely distort the ability to generalize to the target population. In this study, we propose an approach for estimating the finite population mean of a longitudinal outcome conditioning on being alive at a specific time point. We develop a flexible Bayesian semiparametric predictive estimator for population inference when longitudinal auxiliary information is known for the target population. We evaluate the sensitivity of the results to untestable assumptions and further compare our approach to other methods used for population inference in a simulation study. The proposed approach is motivated by 15-year longitudinal data from the Betula longitudinal cohort study. We apply our approach to estimate lifespan trajectories in episodic memory, with the aim to generalize findings to a target population.

Study designs where follow-up samples are collected through multiple attempts have been called repeated attempts designs. In this note we explore the monotonicity thought to underlie the models fit for these designs and show it does not always hold.

Importance Lifestyle interventions for obesity produce reductions in body weight that can decrease risk for diabetes and cardiovascular disease but are limited by suboptimal maintenance of lost weight and inadequate dissemination in low-resource communities. Objective To evaluate the effectiveness of extended care programs for obesity management delivered remotely in rural communities through the US Cooperative Extension System. Design, Setting, and Participants This randomized clinical trial was conducted from October 21, 2013, to December 21, 2018, in Cooperative Extension Service offices of 14 counties in Florida. A total of 851 individuals were screened for participation; 220 individuals did not meet eligibility criteria, and 103 individuals declined to participate. Of 528 individuals who initiated a 4-month lifestyle intervention, 445 qualified for randomization. Data were analyzed from August 22 to October 21, 2019. Interventions Participants were randomly assigned to extended care delivered via individual or group telephone counseling or an education control program delivered via email. All participants received 18 modules with posttreatment recommendations for maintaining lost weight. In the telephone-based interventions, health coaches provided participants with 18 individual or group sessions focused on problem solving for obstacles to the maintenance of weight loss. Main Outcomes and Measures The primary outcome was change in body weight from the conclusion of initial intervention (month 4) to final follow-up (month 22). An additional outcome was the proportion of participants achieving at least 10% body weight reduction at follow-up. Results Among 445 participants (mean [SD] age, 55.4 [10.2] years; 368 [82.7%] women; 329 [73.9%] white), 149 participants (33.5%) were randomized to individual telephone counseling, 143 participants (32.1%) were randomized to group telephone counseling, and 153 participants (34.4%) were randomized to the email education control. Mean (SD) baseline weight was 99.9 (14.6) kg, and mean (SD) weight loss after the initial intervention was 8.3 (4.9) kg. Mean weight regains at follow-up were 2.3 (95% credible interval [CrI], 1.2-3.4) kg in the individual telephone counseling group, 2.8 (95% CrI, 1.4-4.2) kg for the group telephone counseling group, and 4.1 (95% CrI, 3.1-5.0) kg for the education control group, with a significantly smaller weight regain observed in the individual telephone counseling group vs control group (posterior probability >.99). A larger proportion of participants in the individual telephone counseling group achieved at least 10% weight reductions (31.5% [95% CrI, 24.1%-40.0%]) than in the control group (19.1% [95% CrI, 14.1%-24.9%]) (posterior probability >.99). Conclusions and Relevance This randomized clinical trial found that providing extended care for obesity management in rural communities via individual telephone counseling decreased weight regain and increased the proportion of participants who sustained clinically meaningful weight losses. Trial Registration ClinicalTrials.gov Identifier:NCT02054624

ObjectiveTo investigate the potential of lower extremity magnetic resonance (MR) biomarkers to serve as endpoints in clinical trials of therapeutics for Duchenne muscular dystrophy (DMD) by characterizing the longitudinal progression of MR biomarkers over 48 months and assessing their relationship to changes in ambulatory clinical function.MethodsOne hundred sixty participants with DMD were enrolled in this longitudinal, natural history study and underwent MR data acquisition of the lower extremity muscles to determine muscle fat fraction (FF) and MRI T2 biomarkers of disease progression. In addition, 4 tests of ambulatory function were performed. Participants returned for follow-up data collection at 12, 24, 36, and 48 months.ResultsLongitudinal analysis of the MR biomarkers revealed that vastus lateralis FF, vastus lateralis MRI T2, and biceps femoris long head MRI T2 biomarkers were the fastest progressing biomarkers over time in this primarily ambulatory cohort. Biomarker values tended to demonstrate a nonlinear, sigmoidal trajectory over time. The lower extremity biomarkers predicted functional performance 12 and 24 months later, and the magnitude of change in an MR biomarker over time was related to the magnitude of change in function. Vastus lateralis FF, soleus FF, vastus lateralis MRI T2, and biceps femoris long head MRI T2 were the strongest predictors of future loss of function, including loss of ambulation.ConclusionsThis study supports the strong relationship between lower extremity MR biomarkers and measures of clinical function, as well as the ability of MR biomarkers, particularly those from proximal muscles, to predict future ambulatory function and important clinical milestones.ClinicalTrials.gov identifierNCT01484678.

Imputation and inference (or analysis) models that cannot be true simultaneously are frequently used in practice when missing outcomes are present. In these situations, the conclusions can be misleading depending on how "different" the implicit inference model, induced by the imputation model, is from the inference model actually used. We introduce model-based compatibility (MBC) and compare two MBC approaches to a non-MBC approach and explore the inferential validity of the latter in a simple case. In addition, we evaluate more complex cases through a series of simulation studies. Overall, we recommend caution when making inferences using a non-MBC analysis and point out when the inferential "cost" is the largest.

We extend the method proposed in a recent work by the Authors for trial-level general surrogate evaluation to allow combinations of biomarkers and provide a procedure for finding the "best" combination of biomarkers based on the absolute prediction error summary of surrogate quality. We use a nonparametric Bayesian model that allows us to select an optimal subset of biomarkers without having to consider a large number of explicit model specifications for that subset. This dramatically reduces the number of model comparisons needed. Given the model's flexibility, complex nonlinear relationships can be fit when enough data are available. We evaluate the operating characteristics of our proposed method in simulations designed to be similar to our motivating example. We use our method to compare and evaluate combinations of biomarkers as trial-level general surrogates for the pentavalent rotavirus vaccine RotaTeq™ (RV5) (Merck & Co, Inc, Kenilworth, New Jersey, USA), finding that the same single biomarker identified in our previously published analysis is likely the optimal subset.

Introduction Tests of ambulatory function are common clinical trial endpoints in Duchenne muscular dystrophy (DMD). Using these tests, the ImagingDMD study has generated a large data set that can describe the contemporary natural history of DMD in 5-12.9-year-olds. Methods Ninety-two corticosteroid-treated boys with DMD and 45 controls participated in this longitudinal study. Participants performed the 6-minute walk test (6MWT) and timed function tests (TFT: 10-m walk/run, climbing 4 stairs, supine to stand). Results Boys with DMD had impaired functional performance even at 5-6.9 years old. Boys older than 7 had significant declines in function over 1 year for 10-m walk/run and 6MWT. Eighty percent of participants could perform all functional tests at 9 years old. TFTs appear to be slightly more responsive and predictive of disease progression than the 6MWT in 7-12.9 year olds. Discussion This study provides insight into the contemporary natural history of key functional endpoints in DMD. Muscle Nerve 58: 631-638, 2018.

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect - differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients.

We propose a Bayesian nonparametric approach (BNP) for causal inference on quantiles in the presence of many confounders. In particular, we define relevant causal quantities and specify BNP models to avoid bias from restrictive parametric assumptions. We first use Bayesian additive regression trees (BART) to model the propensity score and then construct the distribution of potential outcomes given the propensity score using a Dirichlet process mixture (DPM) of normals model. We thoroughly evaluate the operating characteristics of our approach and compare it to Bayesian and frequentist competitors. We use our approach to answer an important clinical question involving acute kidney injury using electronic health records.

In assessing causal mediation effects in randomized studies, a challenge is that the direct and indirect effects can vary across participants due to different measured and unmeasured characteristics. In that case, the population effect estimated from standard approaches implicitly averages over and does not estimate the heterogeneous direct and indirect effects. We propose a Bayesian semiparametric method to estimate heterogeneous direct and indirect effects via clusters, where the clusters are formed by both individual covariate profiles and individual effects due to unmeasured characteristics. These cluster-specific direct and indirect effects can be estimated through a set of regression models where specific coefficients are clustered by a stick-breaking prior. To let clustering be appropriately informed by individual direct and indirect effects, we specify a data-dependent prior. We conduct simulation studies to assess performance of the proposed method compared to other methods. We use this approach to estimate heterogeneous causal direct and indirect effects of an expressive writing intervention for patients with renal cell carcinoma.

We welcome Drs. Foer and Cahill sharing their recent findings (1,2) on the association of glucagon-like peptide 1 receptor agonists with fewer respiratory disease exacerbations and providing their perspective on our study (3). One of the main challenges researchers encounter when conducting comparative effectiveness studies of glucose-lowering agents is selecting the most appropriate comparator (4 …

OBJECTIVES: Maternal weight and anthropometric trajectories may affect both fetal programming and maternal pregnancy health outcomes. We developed a statistical model that categorized concomitant changes in weight and body composition across gestation. METHODS: We conducted a secondary analysis of a prospective cohort of singletons (n = 2,353) enrolled from 11 US perinatal centers, 2009–2013. Weight, mid-upper arm circumference (MUAC), plus triceps and subscapular skinfold thickness (TSF, SSF) were measured up to 5 times across gestation. Prenatal weights were also abstracted from medical records. Gestational weight change (GWG) trajectories and time-varying, region-specific body composition (MUAC, TSF, SSF) were modeled jointly with a flexible latent class trajectory model, adjusting for prepregnancy BMI. RESULTS: Most women had prepregnancy normal weight (56.4%), while fewer were classified as overweight (27.2%) or obese (16.4%). Five trajectory patterns for weight and body composition were identified. (1) InitalHighSteadyGain (12.5%) had the highest 1(st) trimester weight gain and steady gain thereafter, with high model estimated total GWG (36.1 lbs). (2) InitialModerateSlowGain (19.8%) had moderate 1(st) trimester gain, then slow steady gain with the lowest total GWG (28.7 lbs). InitalHighSteadyGain and InitialModerateSlowGain showed relatively stable MUAC, while SSF and TSF increased in 1(st) and 2(nd) trimesters and decreased in 3(rd) trimester. Other GWG trajectory groups showed (3) 1(st) trimester weight loss (LossSteadyGain, 8.2%), (4) stability (StableSteadyGain, 21.5%), or (5) low gain (LowSteadyGain, 38.2%), then steady GWG in the 2(nd) and 3(rd) trimesters with estimated total GWG of 35.7, 36.8 and 32.8 lb, respectively. Small increases in MUAC and SSF were observed in LowSteadyGain across pregnancy, while TSF increased up to 30 wk and then decreased. LossSteadyGain and StableSteadyGain groups showed similarities in initial MUAC, TSF and SSF measurements with small increases in MUAC and TSF, and moderate increases in SSF over time. CONCLUSIONS: Joint modeling of prenatal weight and regional body composition changes identified five trajectory patterns, which offers a more advanced understanding of how these trajectories concomitantly change during pregnancy. FUNDING SOURCES: NICHD; HHS.

In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these limitations, two Cholesky decomposition approaches have been proposed: modified Cholesky decomposition for autoregressive (AR) structure and moving average Cholesky decomposition for moving average (MA) structure, respectively. However, the correlations of repeated outcomes are often not captured parsimoniously using either approach separately. In this paper, we propose a class of flexible, nonstationary, heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the covariance matrix that we denote as ARMACD. We analyze a recent lung cancer study to illustrate the power of our proposed methods.

Objective Improve pooled viral load (VL) testing to increase HIV treatment monitoring capacity, particularly relevant for resource-limited settings. Design We developed marker-assisted mini-pooling with algorithm (mMPA), a new VL pooling deconvolution strategy that uses information from low-cost, routinely collected clinical markers to determine an efficient order of sequential individual VL testing and dictates when the sequential testing can be stopped. Methods We simulated the use of pooled testing to ascertain virological failure status on 918 participants from 3 studies conducted at the Academic Model Providing Access to Healthcare in Eldoret, Kenya, and estimated the number of assays needed when using mMPA and other pooling methods. We also evaluated the impact of practical factors, such as specific markers used, prevalence of virological failure, pool size, VL measurement error, and assay detection cutoffs on mMPA, other pooling methods, and single testing. Results Using CD4 count as a marker to assist deconvolution, mMPA significantly reduces the number of VL assays by 52% [confidence interval (CI): 48% to 57%], 40% (CI: 38% to 42%), and 19% (CI: 15% to 22%) compared with individual testing, simple mini-pooling, and mini-pooling with algorithm, respectively. mMPA has higher sensitivity and negative/positive predictive values than mini-pooling with algorithm, and comparable high specificity. Further improvement is achieved with additional clinical markers, such as age and time on therapy, with or without CD4 values. mMPA performance depends on prevalence of virological failure and pool size but is insensitive to VL measurement error and VL assay detection cutoffs. Conclusions mMPA can substantially increase the capacity of VL monitoring.