Generalized single index modeling of longitudinal data with multiple binary responses.

In health and clinical research, medical indices (eg, BMI) are commonly used for monitoring and/or predicting health outcomes of interest. While single-index modeling can be used to construct such indices, methods to use single-index models for analyzing longitudinal data with multiple correlated binary responses are underdeveloped, although there are abundant applications with such data (eg, prediction of multiple medical conditions based on longitudinally observed disease risk factors). This article aims to fill the gap by proposing a generalized single-index model that can incorporate multiple single indices and mixed effects for describing observed longitudinal data of multiple binary responses. Compared to the existing methods focusing on constructing marginal models for each response, the proposed method can make use of the correlation information in the observed data about different responses when estimating different single indices for predicting response variables. Estimation of the proposed model is achieved by using a local linear kernel smoothing procedure, together with methods designed specifically for estimating single-index models and traditional methods for estimating generalized linear mixed models. Numerical studies show that the proposed method is effective in various cases considered. It is also demonstrated using a dataset from the English Longitudinal Study of Aging project.

Comparing two hazard curves when there is a treatment time-lag effect.

In cancer and other medical studies, time-to-event (eg, death) data are common. One major task to analyze time-to-event (or survival) data is usually to compare two medical interventions (eg, a treatment and a control) regarding their effect on patients' hazard to have the event in concern. In such cases, we need to compare two hazard curves of the two related patient groups. In practice, a medical treatment often has a time-lag effect, that is, the treatment effect can only be observed after a time period since the treatment is applied. In such cases, the two hazard curves would be similar in an initial time period, and the traditional testing procedures, such as the log-rank test, would be ineffective in detecting the treatment effect because the similarity between the two hazard curves in the initial time period would attenuate the difference between the two hazard curves that is reflected in the related testing statistics. In this paper, we suggest a new method for comparing two hazard curves when there is a potential treatment time-lag effect based on a weighted log-rank test with a flexible weighting scheme. The new method is shown to be more effective than some representative existing methods in various cases when a treatment time-lag effect is present.

Multiblock partial least squares and rank aggregation: Applications to detection of bacteriophages associated with antimicrobial resistance in the presence of potential confounding factors.

Urban environments, characterized by bustling mass transit systems and high population density, host a complex web of microorganisms that impact microbial interactions. These urban microbiomes, influenced by diverse demographics and constant human movement, are vital for understanding microbial dynamics. We explore urban metagenomics, utilizing an extensive dataset from the Metagenomics & Metadesign of Subways & Urban Biomes (MetaSUB) consortium, and investigate antimicrobial resistance (AMR) patterns. In this pioneering research, we delve into the role of bacteriophages, or "phages"-viruses that prey on bacteria and can facilitate the exchange of antibiotic resistance genes (ARGs) through mechanisms like horizontal gene transfer (HGT). Despite their potential significance, existing literature lacks a consensus on their significance in ARG dissemination. We argue that they are an important consideration. We uncover that environmental variables, such as those on climate, demographics, and landscape, can obscure phage-resistome relationships. We adjust for these potential confounders and clarify these relationships across specific and overall antibiotic classes with precision, identifying several key phages. Leveraging machine learning tools and validating findings through clinical literature, we uncover novel associations, adding valuable insights to our comprehension of AMR development.

Predicting high-risk periods for weight regain following initial weight loss.

OBJECTIVE: The aim of this study was to develop a predictive algorithm of "high-risk" periods for weight regain after weight loss. METHODS: Longitudinal mixed-effects models and random forest regression were used to select predictors and develop an algorithm to predict weight regain on a week-to-week basis, using weekly questionnaire and self-monitoring data (including daily e-scale data) collected over 40 weeks from 46 adults who lost ≥5% of baseline weight during an initial 12-week intervention (Study 1). The algorithm was evaluated in 22 adults who completed the same Study 1 intervention but lost <5% of baseline weight and in 30 adults recruited for a separate 30-week study (Study 2). RESULTS: The final algorithm retained the frequency of self-monitoring caloric intake and weight plus self-report ratings of hunger and the importance of weight-management goals compared with competing life demands. In the initial training data set, the algorithm predicted weight regain the following week with a sensitivity of 75.6% and a specificity of 45.8%; performance was similar (sensitivity: 81%-82%, specificity: 30%-33%) in testing data sets. CONCLUSIONS: Weight regain can be predicted on a proximal, week-to-week level. Future work should investigate the clinical utility of adaptive interventions for weight-loss maintenance and develop more sophisticated predictive models of weight regain.

Identification of shared biological features in four different lung cell lines infected with SARS-CoV-2 virus through RNA-seq analysis.

The COVID-19 pandemic caused by SARS-CoV-2 has resulted in millions of confirmed cases and deaths worldwide. Understanding the biological mechanisms of SARS-CoV-2 infection is crucial for the development of effective therapies. This study conducts differential expression (DE) analysis, pathway analysis, and differential network (DN) analysis on RNA-seq data of four lung cell lines, NHBE, A549, A549.ACE2, and Calu3, to identify their common and unique biological features in response to SARS-CoV-2 infection. DE analysis shows that cell line A549.ACE2 has the highest number of DE genes, while cell line NHBE has the lowest. Among the DE genes identified for the four cell lines, 12 genes are overlapped, associated with various health conditions. The most significant signaling pathways varied among the four cell lines. Only one pathway, "cytokine-cytokine receptor interaction", is found to be significant among all four cell lines and is related to inflammation and immune response. The DN analysis reveals considerable variation in the differential connectivity of the most significant pathway shared among the four lung cell lines. These findings help to elucidate the mechanisms of SARS-CoV-2 infection and potential therapeutic targets.

Multivariate single index modeling of longitudinal data with multiple responses.

In medical studies, composite indices and/or scores are routinely used for predicting medical conditions of patients. These indices are usually developed from observed data of certain disease risk factors, and it has been demonstrated in the literature that single index models can provide a powerful tool for this purpose. In practice, the observed data of disease risk factors are often longitudinal in the sense that they are collected at multiple time points for individual patients, and there are often multiple aspects of a patient's medical condition that are of our concern. However, most existing single-index models are developed for cases with independent data and a single response variable, which are inappropriate for the problem just described in which within-subject observations are usually correlated and there are multiple mutually correlated response variables involved. This paper aims to fill this methodological gap by developing a single index model for analyzing longitudinal data with multiple responses. Both theoretical and numerical justifications show that the proposed new method provides an effective solution to the related research problem. It is also demonstrated using a dataset from the English Longitudinal Study of Aging.

Comparing D3-Creatine Dilution and Dual-Energy X-ray Absorptiometry Muscle Mass Responses to Strength Training in Low-Functioning Older Adults.

BACKGROUND: In contrast to dual-energy x-ray absorptiometry (DXA), the D3-creatine (D3Cr) dilution method provides a direct measure of skeletal muscle mass and in a cohort of older men has been strongly associated with health-related outcomes. However, sensitivity to detect changes in D3Cr-derived muscle mass due to an intervention is limited. METHODS: Twenty-one older adults (≥70 years) with low-to-moderate physical function were randomized to a 15-week high-intensity strength training (ST) or a health education (HE) group. Full-body progressive intensity ST was performed 3 days per week. RESULTS: The mean age was 82.1 years, with 64% females. After 15 weeks, both D3Cr muscle mass (MM; 2.29 kg; 95% CI: 0.22, 4.36) and DXA appendicular lean mass (ALM; 1.04 kg; 95% CI: 0.31, 1.77) were greater in ST group compared to HE. Baseline correlations between D3Cr MM and DXA ALM (r = 0.79; 95% CI: 0.53, 0.92) or total lean body mass (LBM; r = 0.79; 95% CI: 0.52, 0.91) were high. However, longitudinal changes in D3Cr MM were weakly correlated with changes in DXA ALM (r = 0.19; 95% CI: -0.35, 0.64) and LBM (r = 0.40; 95% CI: -0.13, 0.76). More participants showed positive response rates, defined as a >5% increase from baseline, with D3Cr MM (80%) than DXA measures (14%-43%). CONCLUSIONS: A progressive ST intervention in low-functioning older adults increased D3Cr MM and DXA ALM. These data suggest that the D3Cr dilution is potentially sensitive to detect changes in muscle mass in response to resistance exercise training. These results are preliminary and could be used for planning larger trials to replicate these results.

Impact of transition from face-to-face to telehealth on behavioral obesity treatment during the COVID-19 pandemic.

OBJECTIVE: This study evaluated whether the transition of a face-to-face behavioral intervention to videoconferencing-based telehealth delivery during the COVID-19 pandemic resulted in significantly smaller weight losses than those typically observed in gold-standard, face-to-face programs. METHODS: Participants were 160 adults with obesity (mean [SD] age = 49.2 [11.9] years, BMI = 36.1 [4.2] kg/m2 ) enrolled in two cohorts of a 16-week comprehensive weight-management program. Cohort 1 began in person and transitioned to telehealth (Zoom) delivery during week 11 of the intervention because of COVID-19; Cohort 2 was conducted completely remotely. A noninferiority approach (using a clinically relevant noninferiority margin of 2.5%) was used to assess whether the weight losses observed were inferior to the 8% losses from baseline typically produced by gold-standard, face-to-face lifestyle interventions. RESULTS: From baseline to postintervention, participants lost an average of 7.4 [4.9] kg, representing a reduction of 7.2% [4.6%]. This magnitude of weight change was significantly greater than 5.5% (t[159] = 4.7, p < 0.001), and, thus, was within the proposed noninferiority margin. CONCLUSIONS: These findings demonstrate that the results of behavioral weight-management interventions are robust, whether delivered in person or remotely, and that individuals can achieve clinically meaningful benefits from behavioral treatment even during a global pandemic. Pragmatic "lessons learned," including modified trial recruitment techniques, are discussed.

Non-parametric treatment time-lag effect estimation.

In general, the change point problem considers inference of a change in distribution for a set of time-ordered observations. This has applications in a large variety of fields, and can also apply to survival data. In survival analysis, most existing methods compare two treatment groups for the entirety of the study period. Some treatments may take a length of time to show effects in subjects. This has been called the time-lag effect in the literature, and in cases where time-lag effect is considerable, such methods may not be appropriate to detect significant differences between two groups. In this paper, we propose a novel non-parametric approach for estimating the point of treatment time-lag effect by using an empirical divergence measure. Theoretical properties of the estimator are studied. The results from the simulated data and the applications to real data examples support our proposed method.

Effective disease surveillance by using covariate information.

Effective surveillance of infectious diseases, cancers, and other deadly diseases is critically important for public health and safety of our society. Incidence data of such diseases are often collected spatially from different clinics and hospitals through a regional, national or global disease reporting system. In such a system, new batches of data keep being collected over time, and a decision needs to be made immediately after new data are collected regarding whether there is a disease outbreak at the current time point. This is the disease surveillance problem that will be focused in this article. There are some existing methods for solving this problem, most of which use the disease incidence data only. In practice, however, disease incidence is often associated with some covariates, including the air temperature, humidity, and other weather or environmental conditions. In this article, we develop a new methodology for disease surveillance which can make use of helpful covariate information to improve its effectiveness. A novelty of this new method is behind the property that only those covariate information that is associated with a true disease outbreak can help trigger a signal. The new method can accommodate seasonality, spatio-temporal data correlation, and nonparametric data distribution. These features make it feasible to use in many real applications.

Edge-Preserving Denoising of Image Sequences.

To monitor the Earth's surface, the satellite of the NASA Landsat program provides us image sequences of any region on the Earth constantly over time. These image sequences give us a unique resource to study the Earth's surface, changes of the Earth resource over time, and their implications in agriculture, geology, forestry, and more. Besides natural sciences, image sequences are also commonly used in functional magnetic resonance imaging (fMRI) of medical studies for understanding the functioning of brains and other organs. In practice, observed images almost always contain noise and other contaminations. For a reliable subsequent image analysis, it is important to remove such contaminations in advance. This paper focuses on image sequence denoising, which has not been well-discussed in the literature yet. To this end, an edge-preserving image denoising procedure is suggested. The suggested method is based on a jump-preserving local smoothing procedure, in which the bandwidths are chosen such that the possible spatio-temporal correlations in the observed image intensities are accommodated properly. Both theoretical arguments and numerical studies show that this method works well in the various cases considered.

Joint modeling of multivariate nonparametric longitudinal data and survival data: A local smoothing approach.

In many clinical studies, evaluating the association between longitudinal and survival outcomes is of primary concern. For analyzing data from such studies, joint modeling of longitudinal and survival data becomes an appealing approach. In some applications, there are multiple longitudinal outcomes whose longitudinal pattern is difficult to describe by a parametric form. For such applications, existing research on joint modeling is limited. In this article, we develop a novel joint modeling method to fill the gap. In the new method, a local polynomial mixed-effects model is used for describing the nonparametric longitudinal pattern of the multiple longitudinal outcomes. Two model estimation procedures, that is, the local EM algorithm and the local penalized quasi-likelihood estimation, are explored. Practical guidelines for choosing tuning parameters and for variable selection are provided. The new method is justified by some theoretical arguments and numerical studies.

Ratings of Perceived Exertion During Walking: Predicting Major Mobility Disability and Effect of Structured Physical Activity in Mobility-Limited Older Adults.

BACKGROUND: This study evaluated the association between ratings of perceived exertion (RPE) of walking and major mobility disability (MMD), as well as their transitions in response to a physical activity (PA) compared to a health education (HE) program. METHODS: Older adults (n = 1633) who were at risk for mobility impairment were randomized to structured PA or HE programs. During a 400 m walk, participants rated exertion as "light" or "hard." An MMD event was defined as the inability to walk 400 m. MMD events and RPE values were assessed every 6 months for an average of 2.6 years. RESULTS: Participants rating their exertion as "hard" had a nearly threefold higher risk of MMD compared with those rating their exertion as "light" (HR: 2.61, 95% CI: 2.19-3.11). The association was held after adjusting for disease conditions, depression, cognitive function, and walking speed (HR: 2.24, 95% CI: 1.87-2.69). The PA group was 25% more likely to transition from "light" to "hard" RPE than the HE group (HR: 1.25, 95% CI: 1.05-1.49). Additionally, the PA group was 27% (HR: 0.73, 95% CI: 0.55 - 0.97) less likely to transition from a "hard" RPE to inability to walk 400 m and was more likely to recover their ability to walk 400 m by transitioning to a "hard" RPE (HR: 2.10, 95% CI: 1.39-3.17) than the HE group. CONCLUSIONS: Older adults rating "hard" effort during a standardized walk test were at increased risk of subsequent MMD. A structured PA program enabled walking recovery, but was more likely to increase transition from "light" to "hard" effort, which may reflect the greater capacity to perform the test.

Innovations in Geroscience to enhance mobility in older adults.

Aging is the primary risk factor for functional decline; thus, understanding and preventing disability among older adults has emerged as an important public health challenge of the 21st century. The science of gerontology - or geroscience - has the practical purpose of "adding life to the years." The overall goal of geroscience is to increase healthspan, which refers to extending the portion of the lifespan in which the individual experiences enjoyment, satisfaction, and wellness. An important facet of this goal is preserving mobility, defined as the ability to move independently. Despite this clear purpose, this has proven to be a challenging endeavor as mobility and function in later life are influenced by a complex interaction of factors across multiple domains. Moreover, findings over the past decade have highlighted the complexity of walking and how targeting multiple systems, including the brain and sensory organs, as well as the environment in which a person lives, can have a dramatic effect on an older person's mobility and function. For these reasons, behavioral interventions that incorporate complex walking tasks and other activities of daily living appear to be especially helpful for improving mobility function. Other pharmaceutical interventions, such as oxytocin, and complementary and alternative interventions, such as massage therapy, may enhance physical function both through direct effects on biological mechanisms related to mobility, as well as indirectly through modulation of cognitive and socioemotional processes. Thus, the purpose of the present review is to describe evolving interventional approaches to enhance mobility and maintain healthspan in the growing population of older adults in the United States and countries throughout the world. Such interventions are likely to be greatly assisted by technological advances and the widespread adoption of virtual communications during and after the COVID-19 era.

Disparities in Pancreatic Ductal Adenocarcinoma-The Significance of Hispanic Ethnicity, Subgroup Analysis, and Treatment Facility on Clinical Outcomes.

BACKGROUND: Disparities exist among patients with pancreatic ductal adenocarcinoma (PDAC). Non-White race is regarded as a negative predictor of expected treatment and overall survival. Data suggest that Academic Research Programs (ARP) provide better outcomes for minorities, but ethnic/minority outcomes are underreported. We hypothesize that outcomes among racially/ethnically diverse PDAC patients may be influenced by treatment facility. METHODS: The National Cancer Database was used to identify 170,327 patients diagnosed with PDAC between 2004 and 2015. Cox proportional-hazard regression was used to compare survival between race/ethnic groups across facilities. RESULTS: In unadjusted models, compared to non-Hispanic Whites (NHW), non-Hispanic Blacks (NHB) had the worst overall survival (HR = 1.05, 95%CI: 1.03-1.06, P < .001) and Hispanics had the best overall survival (HR = 0.92, 95%CI: 0.90-0.94, P < .001). After controlling for socioeconomic and clinical covariates, NHB (HR = 0.95, 95%CI: 0.93-0.96, P < .001) had better overall survival compared to NHW, and Hispanics continued to have the best comparative outcomes (HR = 0.84, 95%CI: 0.82-0.86, P < .001). Among Hispanics, Dominicans and South/Central Americans lived the longest, at 10.25 and 9.82 months, respectively. The improved survival in Hispanics was most pronounced at ARP (HR = 0.80, 95%CI: 0.77-0.84, P < .001) and Integrated Network Cancer Programs (HR = 0.78, 95%CI: 0.73-0.84, P < .001). NHB had improved survival over NHW at Comprehensive Community Care Programs (HR = 0.96, 95%CI: 0.93-0.98, P = .002) and ARP (HR = 0.96, 95%CI: 0.94-0.98, P = .001), which was influenced by income, education, and surgical resection. CONCLUSION: Survival was improved at ARP for all populations. Hispanics had the best comparative overall survival. NHB had improved overall survival at higher volume centers, but this was dependent upon income, education, and surgical resection.

Optimizing the Treatment of CRPS With Ketamine.

OBJECTIVE: This study aimed to develop a method that objectively measures the clinical benefits of ketamine infusions to treat complex regional pain syndrome (CRPS), thus making it possible, for the first time, to determine the optimal dosing of ketamine and duration of treatment to treat CRPS. MATERIALS AND METHODS: All patients were diagnosed with hyperalgesia associated with CRPS. Patients underwent an outpatient, 4-day, escalating dose ketamine infusion. Hyperalgesia was measured using pain thresholds. Clinical outcome was determined without knowledge of the patient's pain thresholds throughout treatment. RESULTS: We found a correlation between pain thresholds and the intensity of pain reported by the patient at various sites of the body. We found that clinical outcomes correlated with improvement in pain thresholds. There was a plateau in pain thresholds between days 3 and 4 for the lower extremities. There was no plateau in pain thresholds observed for the upper extremities. DISCUSSION: Our findings suggest that 4 days of treatment are sufficient for the treatment of CRPS of the lower extremities. For the upper extremities, >4 days may be required. Our study is the first to utilize quantitative sensory testing to direct the treatment of a chronic pain disorder.

Early detection of high disease activity in juvenile idiopathic arthritis by sequential monitoring of patients' health-related quality of life scores.

Juvenile idiopathic arthritis (JIA) is a chronic disease. During its "high disease activity (HDA)" stage, JIA can cause severe pain, and thus could seriously affect patients' physical and psychological health. Early detection of the HDA stage of JIA can reduce the damage of the disease by treating it at an early stage and alleviating the painful experience of the patients. So far, no effective cure of JIA has been found, and one major goal of disease management is to improve patients' quality of life. To this end, patients' health-related quality of life (HRQOL) scores are routinely collected over time from JIA patients. In this paper, we demonstrate that a new statistical methodology called dynamic screening system (DySS) is effective for early detection of the HDA stage of JIA. By this approach, a patient's HRQOL scores are monitored sequentially, and a signal is given by DySS once the longitudinal pattern of the scores is found to be significantly different from the pattern of patients with low disease activity. Dimension reduction of the observed HRQOL scores and the corresponding impact on the performance of DySS are also discussed.

Association of Long-Term Dynamics in Circulating Testosterone with Serum PSA in Prostate Cancer-Free Men with Initial-PSA < 4 ng/mL.

We previously reported that an accelerated decline in circulating testosterone level is associated with a higher risk of prostate cancer (PCa). This study is to examine whether testosterone change rate is related to serum prostate-specific antigen (PSA) concentration among PCa-free men. Longitudinal data were derived from electronic medical records at a tertiary hospital in the Southeastern USA. PCa-free men with initial-PSA < 4 ng/mL and ≥ 2 testosterone measurements were included (n = 632). Three PSA measures (peak, the most recent, and average PSA) during the study period (from first testosterone measurement to the most recent hospital visit) were examined using multivariable-adjusted geometric means and were compared across quintiles of testosterone change rate (ng/dL/month) and current testosterone level (cross-sectional). Mean (standard deviation, SD) age at baseline was 59.3 (10.5) years; mean study period was 93.0 (55.3) months. After adjusting for covariates including baseline testosterone, the three PSA measures all significantly increased across quintile of testosterone change rate from increase to decline (peak PSA: quint 1 = 1.09, quint 5 = 1.41; the most recent PSA: quint 1 = 0.85, quint 5 = 1.00; average PSA: quint 1 = 0.89, quint 5 = 1.02; all Ptrend < 0.001). But current testosterone level was not associated with PSA levels. Stratified analyses indicated men with higher adiposity (body mass index > 24.1 kg/m2) or lower baseline testosterone (≤ 296 ng/dL) were more sensitive to testosterone change in regard to PSA. Among PCa-free men, accelerated testosterone decline might correlate with higher serum PSA concentration. It will help to elucidate the mechanisms relating aging-accompanying testosterone dynamics to prostate carcinogenesis.

Week-to-week predictors of weight loss and regain.

OBJECTIVES: Despite increased interest in the development of individually tailored weight management programs, little is known about what factors proximally predict weight change. METHOD: The current study investigated proximal (week-to-week) predictors of weight loss and regain in 74 adults during a 3-month, Internet-based behavioral weight loss program followed by a 9-month "maintenance" period (during which no additional intervention was provided). Participants were asked to self-weigh daily using scales that transmitted weight via the cellular network and to answer a brief questionnaire each week querying mood, behaviors, and cognitions hypothesized to be associated with weight loss and regain. RESULTS: Longitudinal multilevel models demonstrated that weight loss during initial intervention was proximally predicted by (a) greater frequency of self-monitoring weight and caloric intake, consistency between eating choices and weight loss goals, and importance of "staying on track" with these goals and (b) less negative mood, boredom with weight control efforts, hunger, and temptation to eat foods "not on plan" (ps < .05). Greater weight regain after intervention was also proximally predicted by these factors (with effects in the opposite direction) and additionally by less physical activity, less positive mood, more stress, greater temptation to skip planned physical activity, and higher ratings of the amount of effort required to stay on track (ps < .05). CONCLUSIONS: Results confirmed the importance of self-monitoring for weight loss and maintenance and identified other key week-to-week predictors of weight change. Results also supported efforts to develop intervention approaches specifically focused on weight loss maintenance. Future research should investigate whether using identified predictors to tailor intervention content and timing can improve weight outcomes. (PsycINFO Database Record (c) 2019 APA, all rights reserved).