Linear Mixed-effects Modeling Approach Research Articles

Effects of COVID-19 on Living Standards of Households in the North East Region of Ghana: A Linear Mixed Effects Model Approach

Effects of COVID-19 on Living Standards of Households in the North East Region of Ghana: A Linear Mixed Effects Model Approach

Slow waves during deep sleep support cardiac function

Introduction The role of sleep in cardiovascular health, particularly the impact of deep non-rapid eye movement (NREM) sleep, is gaining interest in the prevention of cardiovascular diseases (Grandner et al., 2016). Essentially slow waves, prominent brain oscillations during deep sleep, seem to represent an important cardiovascular recovery process for the human body. However, to date, the specific mechanisms through which sleep affects cardiovascular function and whether slow waves accelerate recovery remain elusive. Methods Here, we explored the role of slow waves in promoting cardiovascular function. In this randomized, cross-over trial involving 18 healthy male participants (Huwiler et al., 2023), we investigated how experimentally enhancing slow waves via auditory stimulation (Huwiler et al., 2022) impacts cardiovascular dynamics during sleep and next-day cardiac function, assessed via an echocardiography. All participants underwent three experimental nights including two conditions for slow wave enhancement and one SHAM control condition while brain oscillatory, blood pressure, and cardiac dynamics were continuously measured during sleep. Results Using a linear mixed-effect model approach, we found auditory stimulation to significantly increase slow wave activity (F(2, 33.095) = 11.397, p < 0.001) during times of stimulation. This slow wave enhancement coincided with a dynamic cardiovascular activation, indicated through short increases in blood pressure and a biphasic heart rate response. After sleep, we found a significant increase in general longitudinal strain (F(2, 34) = 81.17, p < 0.001), an increase in left-ventricular ejection fraction (F(2, 34) = 4.55, p = 0.018), and a decrease in E/e’ ratio (F(2, 34) = 3.38, p = 0.046) for both slow wave stimulation conditions compared to SHAM. Discussion/Conclusion We show that slow wave stimulation induces a cardiovascular activation response possibly related to increasing cardiovascular stability during sleep (De Zambotti et al., 2016). Moreover, we demonstrate that slow wave stimulation enhances left-ventricular systolic and diastolic function, both together indicating improved cardiac function. Altogether, this points towards the functional involvement of slow waves in promoting cardiovascular health. Therefore, sleep slow wave stimulation may be considered a potential supplementary method in treating cardiovascular diseases involving decreased left-ventricular function. Furthermore, our findings hint that enhancing slow waves could potentially optimize post-exercise recovery processes and increase cardiovascular well-being.

Me, myself and you: How self-consciousness influences time perception

Several investigations have shown that the processing of self-relevant information differs from processing objective information. The present study aimed to investigate the effect of social stimuli on subjective time processing. Here, social stimuli are images of an unknown male and female person and an image of participants’ self. Forty university students were tested with a time reproduction task in which they were asked to reproduce the duration of the stimulus previously presented. Images of others or themselves were used to mark the temporal intervals. Participants also performed questionnaires to evaluate the level of anxiety and depression as well as self-consciousness. A generalised linear mixed-effects model approach was adopted. Results showed that male participants with higher Private Self-Consciousness scores showed higher time perception accuracy than females. Also, female participants reported higher scores for the Public Self-Consciousness subscale than male participants. The findings are discussed in terms of social context models of how attention is solicited and arousal is generated by social stimuli, highlighting the effect of social context on subjective perception of time.

Rail-Induced Social Changes in Central Guangzhou, China

The economic benefits of rail development are well researched, but the social effects of rail are largely understudied, especially in China. We analyze the association between educational attainment level and urban rail development using the 2000 and 2010 census data from Central Guangzhou, China, and a linear mixed-effects modeling approach. Our results indicate that the change in education level is greater in rail neighborhoods than in non-rail neighborhoods in the central area of Guangzhou, and that the rail/metro-induced effect on educational attainment level is evidenced. This research contributes to the existing literature by exploring a new method for this line of research and providing empirical evidence of social changes in a city that has experienced rapid economic growth and pioneered rail development in China, a developing country. The limitations and implications of the research are discussed.

Seasonal watershed-scale influences on nitrogen concentrations across the Upper Mississippi River basin

ABSTRACT In the Upper Mississippi River basin, after spring fertilizer application, total nitrogen concentration [TN] in streams drops by ~73% from June to September, consistent with effects of seasonal nutrient loading. We hypothesized that this seasonal variability might be affected by land cover (e.g. wetland, cultivated area extent). To test this hypothesis, we adopted a linear mixed-effects modeling approach including periodic functions. However, inclusion of wetland area was not indicated, suggesting that increased model complexity associated with inclusion of contemporary wetland extent was unjustified. While consideration of cultivated extent in relation to mean annual [TN] was shown to improve performance, no evidence was observed that cultivated extent could enhance explanation of intra-annual [TN]. Improved understanding of cause and effect will require improved spatiotemporal data on nitrogen sources and additional critical field experimentation, which in turn is expected to set the stage for advancement in physically- or process-based modeling of nutrient concentrations.

To blend or not to blend? The role of morphological traits for the detection of marine macrobenthos in bulk DNA and eDNA from the ethanol preservative

The impact of methodological choices on the reliability and reproducibility of DNA metabarcoding need to be well understood to allow successful implementation in routine monitoring frameworks. For macrobenthos communities, the metabarcoding protocol focuses on a fragment of the mitochondrial COI gene and depending on the primer set used for amplification of COI, different taxa can be detected. To identify the primer set that allows the best diversity estimates for macrobenthos in the North Sea region, we sampled four distinct and well characterised communities and identified macrobenthos using traditional morpho-taxonomy before molecular processing. Of the five primer sets tested, the Leray primer set yielded the highest number of non-chimeric reads, detected the highest number of macrobenthos species and best recovered beta diversity patterns. Despite the availability of a nearly complete reference database, 19 out of the 59 morphological species were not picked up with DNA metabarcoding. Next to primer choice, the DNA source used in metabarcoding studies can affect whether or not a species is detected. DNA can be extracted from bulk specimens or from the ethanol preservative in which the macrobenthos sample was preserved. The latter DNA source would greatly speed up processing time of samples in the laboratory. We therefore compared species detection in bulk DNA and eDNA from the ethanol preservative from the four macrobenthos communities in the North Sea. Our results show that community composition differed significantly between bulk DNA and eDNA samples, but both sample types are able to differentiate the four macrobenthos communities from the North Sea. Of the 49 species that are detected in both sample types, 27 are also found in the morphological dataset. The 14 species that are exclusively detected in the ethanol preservative are mainly pelagic species. In view of the low read numbers allocated to these species (at most 153 reads) they most likely represent “contaminant” DNA molecules that are attached to the specimens or the organic debris. To better understand the different results between bulk DNA and eDNA from the ethanol preservative, we investigated the importance of four categorical traits in explaining the probability of detecting a species in the two sample types: body, larval stage (benthic or pelagic), longevity and body skeleton (chitin, CaCO3 or soft tissue). A generalized linear mixed effects model approach shows that the probability of detecting a species in the eDNA from the ethanol preservative is significantly lower than for bulk DNA for macrobenthos species having small to medium body size and for species having chitine or CaCO3 in their skeleton. In contrast, detection in the bulk DNA samples is not affected by the investigated traits. Although the ethanol preservative can be used to characterize beta diversity patterns, our results show that monitoring of macrobenthos species will be most robust when using bulk DNA as template for metabarcoding.

Self-reported quality of life and degree of youth-parent agreement: A long-term follow-up of childhood-onset epilepsy.

To prospectively delineate self-reported health-related quality of life (HRQOL) of adolescents and young adults (AYAs) 8 and 10 years after an epilepsy diagnosis and evaluate the degree of AYA-parent agreement in ratings of AYA's HRQOL. Data came from the Health-Related Quality of Life in Children with Epilepsy Study (HERQULES), a 10-year longitudinal study of children, aged 4-12years, with newly diagnosed epilepsy. Epilepsy-specific HRQOL was self-reported by AYA 8 and 10 years after diagnosis and by parents at multiple time points throughout the 10-year follow-up. Measurers of HRQOL over time were analyzed using a linear mixed-effect model approach. AYA-parent agreement was evaluated using intraclass correlation coefficient (ICC) and Bland-Altman plots. A total of 165 AYAs participated at long-term follow-up. There was considerable heterogeneity among AYA's HRQOL, and as a group, there was no significant change in HRQOL from the 8- to 10-year follow-up. Household income at the time of diagnosis, seizure control at follow-up, and a history of emotional problems (anxiety/depression) were independent predictors of HRQOL at follow-up. AYA-parent agreement on AYA's HRQOL was moderate (ICC 0.62, 95% CI 0.51-0.71), although considerable differences were observed at the individual level. AYA-parent agreement varied with AYA's and parent's age, seizure control, and family environment. In the long-term after a diagnosis of epilepsy, AYAs report stable HRQOL over time at the group level, although notable individual differences exist. Seizure control, anxiety/depression, and family environment meaningfully impact AYA's long-term HRQOL. AYA and parent reports on HRQOL are similar at the group level, although they cannot be used interchangeably, given the large individual differences observed.

Perceptions of Clinical Supervision in Ambulatory Settings: Do Supervisee and Supervisors See Eye to Eye?

Introduction: Given the shortage of primary care physicians, faculties of medicine have been seeking ways to encourage medical students to opt for family medicine as their career choice. The Faculty of Medicine in Geneva decided to incorporate a mandatory one-month rotation during clerkship in a primary care physician’s private office. Such a learning environment offers a privileged opportunity for the student but represents a considerable challenge for clinical teachers. With the goal of identifying training priorities, we sought to investigate the differences in perception of clinical supervision held by clinical teachers and by students. Methods: Cognitive apprenticeship is a well-established theoretical framework for studies on clinical supervision. The Maastricht Clinical Teaching Questionnaire, based on this theory, measures five dimensions of clinical supervision. We used this validated questionnaire to compare student assessment and supervisor self-assessment of the clinical supervision. A total of 275 students over two years assessed their clinical supervision experience provided by a group of 128 clinical supervisors. This hierarchical configuration of the collected data (students were grouped according to the supervisor they had assessed) implied using a linear mixed-effects model approach to assess the significance of observed scores. Results: The results clearly show that students consistently scored their supervisor higher on all five dimensions than the supervisors did themselves. Although it appears that students and supervisors do not focus on the same issues pertaining to clinical supervision, and student expectations might not coincide with supervisors’, the fact remains that they both experience the tasks described in each of the five dimensions together. Conclusion: The results of our study point to some improvements to the training of clinical supervisors.

Transcriptome responses in blood reveal distinct biological pathways associated with arsenic exposure through drinking water in rural settings of Punjab, Pakistan

BackgroundGroundwater Arsenic (As) contamination is a global public health concern responsible for various health implications and a neglected area of environmental health research in Pakistan. Because of interindividual differences in genetic predisposition, As-related health issues may not be equally distributed among the As-exposed population. However, till date, no studies have been conducted including multiple SNPs involved in As metabolism and disease risk using a linear mixed effect model approach to analyze peripheral blood transcriptomics results. ObjectivesIn order to detect early responses on the gene expression level and to evaluate the impact of selected SNPs inferring disease risks associated with As exposure, we designed a systematic study to investigate blood transcriptomics profiles of 57 differentially exposed rural subjects living in drinking water As-contaminated settings of Lahore and Kasur districts in Punjab Province in southeast Pakistan. Exposure among the subjects was correlated with individual transcriptome responses applying urinary As profiles as the main biomarker for risk stratification. MethodsWe performed whole genome gene expression analysis in blood of subjects using microarrays. Linear effect mixed models were applied for evaluating the combined impact of SNPs hypothetically increasing the risk for As exposure-induced health effects (GSTM1, GSTT1, As3MT, DNMT1, MTHFR, ERCC2 and EGFR). ResultsOur findings confirmed important signaling, growth factor, cancer and other disease related pathways known to be associated with increased As exposure levels. In addition, upon implementing our integrative SNPs-based genetic risk factor, pathways associated with an increased risk of NAFLD and diabetes appeared significantly enhanced by down-regulation of genes NDUFV3, IKBKB, IL6R, ADIPOR1, PPARA, OGT and FOXO1. ConclusionWe report the first comprehensive study applying state-of-the-art bioinformatics approaches to address multiple SNP-based inter-individual variability in adverse molecular responses among subjects exposed to drinking water As contamination in Pakistan thereby providing strong evidence of various gene expression targets associated with development of known As-related diseases.

Brain–heart interactions considering complex physiological data: processing schemes for time-variant, frequency-dependent, topographical and statistical examination of directed interactions by convergent cross mapping

Background: A multitude of complex methods is available to quantify interactions in highly complex physiological systems. Brain–heart interactions play an important role in identifying couplings between the central nervous system and the autonomic nervous system during defined physiological states or specific diseases. The crucial point of those interaction analyses is adequate pre-processing taking into account nonlinearity of data, intuitive graphical representation and suitable statistical evaluation of the achieved results. Objective: The aim of this study is to provide generalized processing schemes for such investigations taking into account pre-processing, graphical representation and statistical analysis. Approach: Two defined data sets were used to develop these processing schemes. Brain–heart interactions in children with temporal lobe epilepsy during the pre-ictal, ictal and post-ictal periods as well as in patients with paranoid schizophrenia and healthy control subjects during the resting state period were investigated by nonlinear convergent cross mapping (CCM). Surrogate data, bootstrapping and linear mixed-effects model approaches were utilized for statistical analyses. Main results: CCM was able to reveal specific and statistically significant time- and frequency-dependent patterns of brain–heart interactions for children with temporal lobe epilepsy and provide a statistically significant pattern of topographic- and frequency-dependent brain–heart interactions for schizophrenic patients, as well as to show the differences from healthy control subjects. Suitable statistical models were found to quantify group differences. Significance: Generalized processing schemes and crucial points of pre-processing, adapted interaction analysis and performed statistical analysis are provided. The general concept of analyses is transferable also to other methods of interactions analysis and data representing even more complex physiological systems.

Regular exercise and the trajectory of health-related quality of life among Taiwanese adults: a cohort study analysis 2006\u20132014

BackgroundPhysical activity is related to health-related quality of life, but little evidence from multiple waves of panel data in Asian countries area available. This study aims to explore the impacts of different degree of regular exercise on the trajectories of physical and mental dimensions of health-related quality of life (HRQOL) for community-dwelling Taiwanese adults during 2006–2014.MethodsData were derived from the “Landseed Integrated Outreaching Neighborhood Screening (LIONS)” study, with 6182 adults enrolled at the baseline and subsequently followed up to three times till 2014. Linear mixed-effects modeling approach was employed to evaluate the growth curve models of HRQOL (with 16,281 observations) by linear & quadratic time effects, regular exercise (5-level moderate-intensity physical activity), and major influential factors of HRQOL.ResultsRegular exercise showed significantly positive dose-response effects on physical HRQOL (β =1.27~2.54), and regular exercise of 150 min or more showed positive effects on mental HRQOL (β = 1.55~2.03). Besides, irregular exercise could also improve both physical and mental HRQOL (β = 1.27 & β = 0.87). However, such effects were not significant over time (at time slope) on HRQOL. In addition, physical and mental HRQOL improved across time (β = 1.01 and 1.49, respectively), but the time quadratic effect would significantly offset a little bit on physical dimension (β = − 0.22). Moreover, being female, increasing age, living alone, or poorer health status were related to lower physical HRQOL; and being younger, living alone, or poorer health status were associated with lower mental HRQOL.ConclusionsThe positive dose-response relationship between regular exercise and HRQOL or its domains was demonstrated for community-dwelling Taiwanese adults. Thus, a regular exercise habit (better ≧150 min per week) is advised for community-based healthcare professionals and the government to incorporate into health promotion strategies and plans.

Asymmetric dimethylarginine predicts impaired epicardial coronary vasomotion in patients with angina in the absence of obstructive coronary artery disease

BackgroundImpaired epicardial coronary vasomotion is a potential mechanism of angina and a predictor of adverse cardiovascular outcomes in patients without angiographic evidence of obstructive coronary artery disease (CAD). We sought to evaluate the association of asymmetric dimethylarginine (ADMA)—a marker of nitric oxide-mediated vascular dysfunction—with epicardial coronary vasomotor dysfunction in this select population. MethodsInvasive testing for epicardial vasomotor dysfunction was performed using intracoronary acetylcholine in the left anterior descending coronary artery. Impaired vasomotor response was defined as a luminal constriction of >20% on quantitative coronary angiography. Plasma ADMA levels were measured using high performance liquid chromatography. A robust multivariate linear mixed-effect model approach and Akaike information criterion were used to determine predictors of vasomotor dysfunction. ResultsIn 191 patients with angina in the absence of obstructive CAD, abnormal epicardial vasomotion was observed in 137 (71.7%) patients. Median ADMA rose as the extent of impairment progressed: none (0.48 [0.44–0.59] μM), any (0.51 [0.46–0.60] μM, p = 0.12), focal (0.54 [0.49,0.61] μM, p = 0.17), and diffuse (0.55 [0.49,0.63] μM, p = 0.02). In unadjusted analysis, ADMA was highly predictive of vasomotor dysfunction (χ2=15.1, p = 0.002). Notably, ADMA remained a significant predictor even after adjusting for other factors in the best fit model (χ2=10.0, p = 0.02). ConclusionsADMA is an independent predictor of epicardial coronary vasomotor dysfunction in patients with angina in the absence of obstructive CAD. These data support a very early mechanistic role of ADMA in the continuum of atherosclerotic heart disease.

Proximal remote sensing of tree physiology at northern treeline: Do late-season changes in the photochemical reflectance index (PRI) respond to climate or photoperiod?

Relatively little is known of how the world's largest vegetation transition zone – the Forest Tundra Ecotone (FTE) – is responding to climate change. Newly available, satellite-derived time-series of the photochemical reflectance index (PRI) across North America and Europe could provide new insights into the physiological response of evergreen trees to climate change by tracking changes in foliar pigment pools that have been linked to photosynthetic phenology. However, before implementing these data for such purpose at these evergreen dominated systems, it is important to increase our understanding of the fine scale mechanisms driving the connection between PRI and environmental conditions. The goal of this study is thus to gain a more mechanistic understanding of which environmental factors drive changes in PRI during late-season phenological transitions at the FTE – including factors that are susceptible to climate change (i.e., air- and soil-temperatures), and those that are not (photoperiod). We hypothesized that late-season phenological changes in foliar pigment pools captured by PRI are largely driven by photoperiod as opposed to less predictable drivers such as air temperature, complicating the utility of PRI time-series for understanding climate change effects on the FTE. Ground-based, time-series of PRI were acquired from individual trees in combination with meteorological variables and photoperiod information at six FTE sites in Alaska. A linear mixed-effects modeling approach was used to determine the significance (α = 0.001) and effect size (i.e., standardized slope b*) of environmental factors on late-seasonal changes in the PRI signal. Our results indicate that photoperiod had the strongest, significant effect on late-season changes in PRI (b* = 0.08, p < 0.001), but environmental variables susceptible to climate change were also significant (i.e., daily mean solar radiation (b* = −0.03, p < 0.001) and daily mean soil temperature (b* = 0.02, p < 0.001)). These results suggest that interpreting PRI time-series of late-season phenological transitions may indeed facilitate our understanding of how northern treeline responds to climate change.

Contextual constraints on lexico-semantic processing in aging: Evidence from single-word event-related brain potentials

The current study reports the effects of accumulating contextual constraints on neural indices of lexico-semantic processing (i.e., effects of word frequency and orthographic neighborhood) as a function of normal aging. Event-related brain potentials were measured from a sample of older adults as they read sentences that were semantically congruent, provided only syntactic constraints (syntactic prose), or were random word strings. A linear mixed-effects modeling approach was used to probe the effects of accumulating contextual constraints on N400 responses to individual words. Like young adults in prior work, older adults exhibited a classic word position context effect on the N400 in congruent sentences, although the magnitude of the effect was reduced in older relative to younger adults. Moreover, by modeling single-word variability in N400 responses, we observed robust effects of orthographic neighborhood density that were larger in older adults than the young, and preserved effects word frequency. Importantly, in older adults, frequency effects were not modulated by accumulating contextual constraints, unlike in the young. Collectively, these findings indicate that older adults are less likely (or able) to use accumulating top-down contextual constraints, and therefore rely more strongly on bottom-up lexical features to guide semantic access of individual words during sentence comprehension.

Towards a better understanding of soil organic carbon variation in Madagascar

SummarySoil organic carbon (SOC) is an important carbon pool in terrestrial ecosystems. Prediction of SOC based on soil properties and environmental factors helps to describe the spatial and vertical distribution in SOC; however, the effectiveness and accuracy of various prediction methods, including classical and recently developed model approaches, need to be tested for tropical soil and environments. In this study, random forest (RF) and linear mixed effects model (LMM) approaches were tested to predict the spatial and vertical variation of SOC stocks in Eastern Madagascar. Topography, climate, soil types and vegetation‐based variables were used as predictor variables for modelling SOC stocks at different soil depths to 1 m. The LMM was the most accurate method for predicting SOC stocks for different depth ranges; altitude, soil clay content, land use and precipitation were identified as the most relevant factors for prediction. The accuracy of prediction in SOC modelling decreased with increasing soil depth, resulting in a root mean square prediction error (RMSE) that ranged from 1.98 Mg ha−1 (90–100‐cm depth) to 5.54 Mg ha−1 (10–20‐cm depth) for LMM, which resolved 43–68% of the variation in SOC stocks. Explanatory variables, which contributed to the fixed effect of the model, explained from 2.6 to 28.2% of the total variance, whereas the random effect contributed from 21.7 to 35.0%. This study emphasizes the strength of LMM for predicting SOC stocks in tropical soil taking into account the random effect related to sampling. These results could be used to improve SOC mapping in Madagascar.HighlightsPrediction accuracy of vertical variation in SOC stocks was tested with RF and LMM approaches. Linear mixed effects model provides the most accurate predictions of SOC stocks. Altitude, clay content, climate and land use were identified as relevant predictor variables of SOC. The LMM can be used to improve SOC mapping of tropical soil.

The Effect of Surgical Technique and Spacer Texture on Bone Regeneration: A Caprine Study Using the Masquelet Technique.

The Masquelet-induced-membrane technique is a commonly used method for treating segmental bone defects. However, there are no established clinical standards for management of the induced membrane before grafting. Two clinically based theories were tested in a chronic caprine tibial defect model: (1) a textured spacer that increases the induced-membrane surface area will increase bone regeneration; and (2) surgical scraping to remove a thin tissue layer of the inner induced-membrane surface will enhance bone formation. Thirty-two skeletally mature female goats were assigned to four groups: smooth spacer with or without membrane scraping and textured spacer with or without membrane scraping. During an initial surgical procedure (unilateral, left tibia), a defect was created excising bone (5cm), periosteum (9cm), and muscle (10g). Segments initially were stabilized with an intramedullary rod and an antibiotic-impregnated polymethylmethacrylate spacer with a smooth or textured surface. Four weeks later, the spacer was removed and the induced-membrane was either scraped or left intact before bone grafting. Bone formation was assessed using micro-CT (total bone volume in 2.5-cm central defect region) as the primary outcome; radiographs and histologic analysis as secondary outcomes, with the reviewer blinded to the treatment groups of the samples being assessed 12weeks after grafting. All statistical tests were performed using a linear mixed effects model approach. Micro-CT analysis showed greater bone formation in defects with scraped induced membrane (mean, 3034.5mm3; median, 1928.0mm3; quartile [Q]1-Q3, 273.3-2921.1mm3) compared with defects with intact induced membrane (mean, 1709.5mm3; median, 473.8mm3; Q1-Q3, 132.2-1272.3mm3; p=0.034). There was no difference in bone formation between textured spacers (mean, 2405.5mm3; median, 772.7mm3; Q1-Q3, 195.9-2743.8mm3) and smooth spacers (mean, 2473.2mm3; median, 1143.6mm3; Q1-Q3, 230.2-451.1mm3; p=0.917). Scraping the induced-membrane surface to remove the innermost layer of the induced-membrane increased bone regeneration. A textured spacer that increased the induced-membrane surface area had no effect on bone regeneration. Scraping the induced membrane during the second stage of the Masquelet technique may be a rapid and simple means of improving healing of segmental bone defects, which needs to be confirmed clinically.

Longitudinal physical activity assessment in post-surgical gynecologic cancer patients.

e21582 Background: Physical activity is a key determinant of health, and exercise can be effective for decreasing anxiety and depression and improving quality of life in cancer survivors. The primary aim of this study was to report the trajectory of physical activity over time for post-operative gynecologic cancer patients. The secondary aim was to identify correlates of total physical activity over time. Methods: The study population included patients who underwent surgery for endometrial (n = 80) or ovarian (n = 43) cancer and wore an accelerometer for a 3-day period at 1 week, 1 month and 4 months after surgery. Accelerometer data were captured during all waking hours and were transformed into categories of light, moderate and vigorous intensity physical activity. The outcome of interest was total minutes of moderate to vigorous intensity physical activity (MVPA) collected over the three day period at each time point, analyzed using a linear mixed effect model approach, assessing the correlates of age, body mass index (BMI), self-rated health, laparotomy vs. laparoscopic (endometrial only) and neoadjuvant chemotherapy yes/no (ovarian only). Results: Mean age was 58.8 ± 10.1 years with mean BMI of 35.5±10.3 kg/m2. Over each three-day period, patients performed 7 ± 12 minutes of total accumulated MVPA at 1 week, 20 ± 41 minutes of total MVPA at 1 month, and 25 ± 36 minutes at 4-months post-surgery (p = 0.01). Federal guidelines recommend approximately 70 minutes of moderate intensity activity over a three-day period to collect 150 minutes of moderate activity a week. Better self-rated health at baseline was associated with increased activity over time (p = 0.02). BMI, age, type of surgery or use of neoadjuvant chemotherapy did not predict physical activity in patients of either disease site. Conclusions: Physical activity increased over time for endometrial and ovarian cancer patients. Self-reported health at baseline was also positively associated with increased moderate-vigorous physical activity over time. There is insufficient evidence to show that BMI significantly contributes to changes in moderate-to-vigorous physical activity over time. Future research should address barriers to activity and novel interventions.

Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures.

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.

CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates

We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan.

Joint modeling of recurrent event processes and intermittently observed time-varying binary covariate processes.

When conducting recurrent event data analysis, it is common to assume that the covariate processes are observed throughout the follow-up period. In most applications, however, the values of time-varying covariates are only observed periodically rather than continuously. A popular ad-hoc approach is to carry forward the last observed covariate value until it is measured again. This simple approach, however, usually leads to biased estimation. To tackle this problem, we propose to model the covariate effect on the risk of the recurrent events through jointly modeling the recurrent event process and the longitudinal measures. Despite its popularity, estimation of the joint model with binary longitudinal measurements remains a challenge, because the standard linear mixed effects model approach is not appropriate for binary measures. In this paper, we postulate a Markov model for the binary covariate process and a random-effect proportional intensity model for the recurrent event process. We use a Markov chain Monte Carlo algorithm to estimate all the unknown parameters. The performance of the proposed estimator is evaluated via simulations. The methodology is applied to an observational study designed to evaluate the effect of Group A streptococcus on pharyngitis among school children in India.