Bivariate Linear Mixed Models Research Articles

The use of fixed shelling percentage biases genotype selection in hybrid maize multi-environment yield trials

Context or problemPhenotyping is an integral part of plant breeding operations. In many cases the trait measured is not identical to the target trait for reasons of speed and or cost. This is a form of indirect selection, where correlation between the trait measured and the target phenotype influences the rate of genetic gain. Low correlations lead to slow rates of genetic gain. In sub-Saharan African maize breeding programs, maize grain yield in breeding experimental plots is measured as a field weight (FW), which includes the grain and cob. The weight of grain from each plot is estimated as a standard proportion of grain to total ear weight using a shelling percentage of 80 %. This approach assumes that there is no genetic, environment or genetic by environment interaction in shelling percentage which, if present, would contribute to slower rates of genetic gain for grain yield. Objective or research questionThis study investigated the magnitude of genetic and environmental variation in shelling percentage and its impact on selection in six hybrid maize multi-environment yield trials in Ethiopia over two seasons. MethodsThe data of shelled grain weight (SW) and cob weight (CW) from the trials were analyzed using a bivariate linear mixed model. ResultsGenetic variances for both traits varied across the six testing sites ranging from 0.199 to 2.975 for SW and from 0.029 to 0.245 for CW. The genetic correlations between pairs of sites for SW and CW also varied, indicating the existence of genotype by environment interaction for these traits. Additionally, the bivariate regressions between FW and SW indicated there was substantial genetic deviation around the 80 % shelling response, and this relationship was impacted by environmental influences. ConclusionThe use of a constant relationship of 80 % shelling biases grain yield prediction in multi-environment hybrid maize yield trials and thus reduces the rate of genetic gain in maize breeding programs. Implications or significanceTaking into account the variations in the shelling percentage of the genotypes across sites in predicting grain yield from field weight improves the accuracy of genotype selection and the rate of genetic gain in maize breeding programs.

Pairwise fitting of piecewise mixed models for the joint modeling of multivariate longitudinal outcomes, in a randomized crossover trial.

Many statistical models have been proposed in the literature for the analysis of longitudinal data. One may propose to model two or more correlated longitudinal processes simultaneously, with a goal of understanding their association over time. Joint modeling is then required to carefully study the association structure among the outcomes as well as drawing joint inferences about the different outcomes. In this study, we sought to model the associations among six nutrition outcomes while circumventing the computational challenge posed by their clustered and high-dimensional nature. We analyzed data from a 2 2 randomized crossover trial conducted in Kenya, to compare the effect of high-dose and low-dose iodine in household salt on systolic blood pressure (SBP) and diastolic blood pressure (DBP) in women of reproductive age and their household matching pair of school-aged children. Two additional outcomes, namely, urinary iodine concentration (UIC) in women and children were measured repeatedly to monitor the amount of iodine excreted through urine. We extended the model proposed by Mwangi etal. (2021, Communications in Statistics: Case Studies, Data Analysis and Applications, 7(3), 413-431) allowing flexible piecewise joint models for six outcomes to depend on separate random effects, which are themselves correlated. This entailed fitting 15 bivariate general linear mixed models and deriving inference for the joint model using pseudo-likelihood theory. We analyzed the outcomes separately and jointly using piecewise linear mixed-effects (PLME) model and further validated the results using current state-of-the-art Jones and Kenward methodology (JKME model) used for analyzing randomized crossover trials. The results indicate that high-dose iodine in salt significantly reduced blood pressure (BP) compared to low-dose iodine in salt. Estimates for the random effects and residual error components showed that SBP and DBP had strong positive correlation, with effect of the random slope indicating that significantly related outcomes are strongly associated in their evolution. There was a moderately strong inverse relationship between evolutions of UIC and BP both in women and children. These findings confirmed the original hypothesis that high-dose iodine salt has significant lowering effect on BP. We further sought to evaluate the performance of our proposed PLME model against the widely used JKME model, within the multivariate joint modeling framework through a simulation study mimicking a crossover design. From our findings, the multivariate joint PLME model performed exceptionally well both in estimation of random-effects matrix (G) and Hessian matrix (H), allowing satisfactory model convergence during estimation. It allowed a more complex fit to the data with both random intercepts and slopes effects compared to the multivariate joint JKME model that allowed for random intercepts only. When a hierarchical viewpoint is adopted, in the sense that outcomes are specified conditionally upon random effects, the variance-covariance matrix of the random effects must be positive definite. In some cases, additional random effects could explain much variability in the data, thus improving precision in estimation of the estimands (effect size) parameters. The key highlight in this evaluation shows that multivariate joint JKME model is a powerful tool especially while fitting mixed models with random intercepts only, in crossover design settings. Addition of random slopes may lead to model complexities in most cases, resulting in unsatisfactory model convergence during estimation. To circumvent convergence pitfalls, extention of JKME model to PLME model allows a more flexible fit to the data (generated from crossover design settings), especially in the multivariate joint modelingframework.

Genetic parameters for yearling male reproduction traits in tropical composite cattle population.

Fertility is economically important but is hard to quantify and measure in breeding programs which has led extensive breeding programs to ignore fertility in their selection criteria. While female fertility traits have been extensively researched, male fertility traits have been largely ignored. It is estimated that 20% to 40% of bulls have sub-fertility, reducing the number of calves born and profits, highlighting the importance of investigating bull fertility. The most practical measure of male fertility is a bull breeding soundness evaluation (BBSE) which assesses structure as well as semen quality and quantity. Generally, traits recorded in a BBSE are neither genetically evaluated nor used for selection in breeding programs. All traits recorded during a BBSE were analyzed through a series of univariate and bivariate linear mixed models using a genomic relationship matrix to estimate genetic parameters. All genotype and phenotype data were obtained from a tropical composite commercial cattle population and imputed to 27,638 single-nucleotide polymorphisms (SNPs) with a total of 2,613 genotyped animals with BBSE records ranging from 616 to 826 animals depending on the trait. The heritabilities of the 27 traits recorded during a BBSE ranged from 0.02 to 0.49. Seven of the male fertility traits were recommended to be included in a breeding program based on their heritability and their phenotypic and genetic correlations. These traits are scrotal circumference, percent normal sperm, proximal droplets, distal midpiece reflex, knobbed acrosomes, vacuoles/teratoids, and sheath score. Using these seven traits in a breeding program would result in higher calving rates, increasing production and profitability.

Bayesian longitudinal modeling of blood pressure measurements of hypertensive patients at Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital Hosanna, Southern Ethiopia

Hypertension is characterized by the persistent elevation of blood pressure (BP) above the normal range or the use of antihypertensive medication. It represents a major global health issue and serves as a significant risk factor for conditions such as stroke, myocardial infarction, vascular disease, and chronic kidney disease. This study aimed to model the longitudinal measurements of diastolic blood pressure and systolic blood pressure in hypertensive patients using a Bayesian approach. The data were obtained from the records of Wachemo University Nigist Elleni Mohamed Memorial Teaching and Referral Hospital. It encompassed the basic demographic and clinical characteristics of 200 hypertensive patients who commenced antihypertensive treatment between September 2015 and March 2020. Descriptive analysis and a Linear mixed-effects model were employed to analyze the data, utilizing the Bayesian approach with the JMbayes package in R software. The results of this study revealed significant associations between age, time, family history, related diseases, and the mean change of systolic blood pressure (SBP) measurements within the bivariate linear mixed model. Likewise, time, residence, related diseases, and the interaction of time with sex and diabetes demonstrated a significant effect on the mean change of diastolic blood pressure (DBP) measurements in antihypertensive patients. By employing a bivariate linear mixed model, an association of 0.8565 was observed between the evolution (AE) of SBP and DBP. Based on the study findings, we can summarize that age, time, family history, and related diseases have a significant impact on the mean change of SBP, while time, residence, related diseases, and the interaction of time with sex and diabetes affect the mean change of DBP. Furthermore, a strong association exists between the evolution of SBP and DBP.

A meta-analysis of the performance of ultrasound, hepatobiliary scintigraphy, CT and MRI in the diagnosis of acute cholecystitis.

To evaluate the recently reported relative diagnostic accuracy of US, CT, MRI, and cholescintigraphy for diagnosing acute cholecystitis. 2 radiologists independently performed systematic electronic searches for articles published between 2000 and 2021 and applied inclusion/exclusion criteria. 2 different radiologists extracted data from the articles and scored each with a methodological quality tool. Pooled estimates of sensitivity and specificity were calculated with a bivariate linear mixed model. A second analysis made head-to-head comparisons (US vs. CT, US vs. cholescintigraphy). Factors were also analyzed for potential confounding effects on diagnostic accuracy. Of 6121 initial titles, 22 were included. The prevalence of cholecystitis varied widely across studies (9.4-98%). Pooled sensitivity and specificity estimates were 69% (confidence limit [CL] 62-76%) and 79% (CL 71-86%) for US, 91% (CL 86-94%) and 63% (CL 51-74%) for cholescintigraphy, 78% (CL 69-84%) and 81% (CL 71-88%) for CT, and 91% (CL 78-97%) and 93% (CL 70-99%) for MRI. Regarding head-to-head comparisons, the sensitivity of CT (87.6%, CL 70-96%) was significantly higher than US (66.8%, CL 43-84%), while specificities (81.7% with CL 54-95% for US, 91.9% with CL 67-99% for CT) were similar. The sensitivity of cholescintigraphy (87.4%, CL 76-94%) was significantly greater than US (61.6%, CL 44-77%), while the specificity of US (82%, CL 65-92%) was significantly higher than cholescintigraphy (68%, CL 47-84%). Recent data suggests that CT may have a higher sensitivity than US for diagnosing acute cholecystitis, with similar specificity. Cholescintigraphy remains a highly sensitive modality with lower specificity than previously reported. MRI remains under studied, but with promising results.

Biomarkers for Response to Anti-PD-1/Anti-PD-L1 Immune Checkpoint Inhibitors: A Large Meta-Analysis.

Immune checkpoint inhibitors (ICIs) that block PD-1/PD-L1 have consistently demonstrated durable clinical activity across multiple histologies but have low overall response rates for many cancers-indicating that too few patients benefit from ICIs. Many studies have explored potential predictive biomarkers (eg, PD-1/PD-L1 expression, tumor mutational burden [TMB]), no consensus biomarker has been identified. This meta-analysis combined predictive accuracy metrics for various biomarkers, across multiple cancer types, to determine which biomarkers are most accurate for predicting ICI response. Data from 18,792 patients from 100 peer-reviewed studies that evaluated putative biomarkers for response to anti-PD-1/anti- PD-L1 treatment were meta-analyzed using bivariate linear mixed models. Biomarker performance was assessed based on the global area under the receiver operating characteristic curve (AUC) and 95% bootstrap confidence intervals. PD-L1 immunohistochemistry, TMB, and multimodal biomarkers discriminated responders and nonresponders better than random assignment (AUCs >.50). Excluding multimodal biomarkers, these biomarkers correctly classified at least 50% of the responders (sensitivity 95% CIs, >.50). Notably, variation in biomarker performance was observed across cancer types. Although some biomarkers consistently performed better, heterogeneity in performance was observed across cancer types, and additional research is needed to identify highly accurate and precise biomarkers for widespread clinical use.

Genetic relationships among yearling fertility, body composition and weight traits in tropically adapted composite cattle

Context Reproduction is an economically important trait in both males and females; however, the relationships between fertility and body composition traits are little researched, but essential to breeding programs, as they will help inform selection decisions and allow the greatest opportunity for genetic gain. Aims Estimate genetic and phenotypic correlations between male and female yearling fertility traits and investigate their relationship with yearling body composition traits, which have an effect on the attainment of puberty. Methods Genotype and phenotype data were obtained from a tropical composite commercial cattle population and imputed to 27 638 single nucleotide polymorphisms. A series of univariate and bivariate linear mixed models using a genomic relationship matrix were run to estimate genetic parameters, genetic and phenotypic correlations for a series of male and female fertility and body composition traits. These parameters were then compared to help understand the genetic relationships. Key results Scrotal circumference was favourably genetically correlated with weight (0.34), fat traits (0.06–0.24), muscle (0.24) and heifer days to calving (−0.32). Heifer days to calving was favourably correlated with muscle (−0.18) but not fat traits (0.11 to 0.21). The genetic correlations between heifer days to calving and sperm morphology traits were generally unfavourable (−0.32 to 0.25). Sperm morphology traits were favourably genetically correlated with fat traits (−0.84 to 0.31) and muscle (−0.61 to 0.31) but not weight (−0.15 to 0.09). Conclusions and implications Yearling sperm morphology traits were unfavourably correlated with heifer days to calving, indicating that they are not good candidates for indirect selection on improving female fertility in the herd. A different trend was found for yearling scrotal circumference and heifer days to calving, identifying it as a good candidate for indirect selection of heifer fertility as it is easy to measure and heritable. The genetic correlations estimated between composition traits with male and female fertility traits allow breeding programs to make an informed selection decision to optimise genetic gain across all traits.

Diagnostic performance of circulating tumor DNA as a minimally invasive biomarker for hepatocellular carcinoma: a systematic review and meta-analysis.

This study aimed to assess the diagnostic performance of circulating tumor DNA (ctDNA) in hepatocellular carcinoma (HCC). We enrolled all relevant studies published up to 5 January 2022. Three primary subgroups were investigated: qualitative or quantitative ctDNA analyses, combined alpha-fetoprotein (AFP), and ctDNA assay. In addition to the three primary subgroups, we also evaluated the diagnostic value of methylated SEPTIN9 (mSEPT9), which has been studied extensively in the diagnosis of hepatocellular carcinoma. After a search based on four primary databases, we used a bivariate linear mixed model to analyze the pooled sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). We also plotted hierarchical summary receiver operating characteristics (HSROC) and utilized lambda as well as the area under the curve (AUC) to create summary receiver operating characteristic (SROC) curves to estimate the diagnostic value of ctDNA. A total of 59 qualified articles with 9,766 subjects were incorporated into our meta-analysis. The integrated SEN, SPE, and DOR in the qualitative studies were 0.50 (95% CI [0.43-0.56]), 0.90 (95% CI [0.86-0.93]), and 8.72 (95% CI [6.18-12.32]), respectively, yielding an AUC of 0.78 and lambda of 1.93 (95% CI [1.56-2.33]). For quantitative studies, the corresponding values were 0.69 (95% CI [0.63-0.74]), 0.84 (95% CI [0.77-0.89]), 11.88 (95% CI [7.78-18.12]), 0.81, and 2.32 (95% CI [1.96-2.69]), respectively. Six studies were included to evaluate the SETP9 methylation, which yielded an AUC of 0.86, a SEN of 0.80 (95% CI [0.71-0.87]), and a SPE of 0.77 (95% CI [0.68-0.85]). Likewise, ctDNA concentration yielded an AUC of 0.73, with a SEN of 0.63 (95% CI [0.56-0.70]) and a SPE of 0.86 (95% CI [0.74-0.93]). AFP combined with ctDNA assay resulted in an AUC of 0.89, with a SEN of 0.82 (95% CI [0.77-0.86]) and a SPE of 0.84 (95% CI [0.76-0.90]). This study shows that circulating tumor DNA, particularly mSEPT9, shows promising diagnostic potential in HCC; however, it is not enough to diagnose HCC independently, and ctDNA combined with conventional assays such as AFP can effectively improve diagnostic performance.

Relationship Between Brain Tissue Oxygen and Near-Infrared Spectroscopy in Patients with Nontraumatic Subarachnoid Hemorrhage.

Continuous monitoring of cerebral oxygenation is one of the diagnostic tools used in patients with brain injury. Direct and invasive measurement of cerebral oxygenation with a partial brain oxygen pressure (PbtO2) probe is promising but invasive. Noninvasive assessment of regional transcranial oxygen saturation using near-infrared spectroscopy (NIRS) may be feasible. The aim of this study was to evaluate the interchangeability between PbtO2 and NIRS over time in patients with nontraumatic subarachnoid hemorrhage. This retrospective study was performed in a neurocritical care unit. Study participants underwent hourly PbtO2 and NIRS measurements over 72h. Temporal agreement between markers was described by their pointwise correlation. A secondary analysis assessed the structure of covariation between marker trajectories using a bivariate linear mixed model. Fifty-one patients with subarachnoid hemorrhage were included. A total of 3362 simultaneous NIRS and PbtO2 measurements were obtained. The correlation at each measurement time ranged from - 0.25 to 0.25. The global correlation over time was - 0.026 (p = 0.130). The bivariate linear mixed model confirmed the lack of significant correlation between the PbtO2 and NIRS measurements at follow-up. NIRS was unable to detect PbtO2 values below 20mm Hg (area under the receiver operating characteristic curve 0.539 [95% confidence interval 0.536-0.542]; p = 0.928), and percentage changes in NIRS were unable to detect a decrease in PbtO2 ≥ 10% (area under the receiver operating characteristic curve 0.615 [95% confidence interval 0.614-0.616]; p < 0.001). PbtO2 and NIRS measurements were not correlated. There is no evidence that NIRS could be a substitute for PbtO2 monitoring in patients with nontraumatic subarachnoid hemorrhage.

0672 Twenty-Four Hour Activity Patterns, Pain, and Mental Health Trajectories After a Traumatic Event

Abstract Introduction Among those who experience trauma, some individuals develop Adverse Posttraumatic Neuropsychiatric Sequelae (APNS). Identifying biomarkers associated with APNS is critical for predicting trajectories of recovery and informing interventions to reduce the impact of trauma. In this study, we examined objective 24-hour activity patterns and APNS in the weeks following a traumatic event. Methods Participants (n = 2,021) were recruited from emergency departments after experiencing trauma. Over 8 weeks, they wore wrist accelerometry devices to measure 24-hour activity patterns, and completed surveys assessing 10 Research Domain Criteria (RDoC) constructs associated with APNS. We aimed to 1) examine the relationship between 24-hour activity patterns and APNS symptoms, and 2) examine how 24-hour activity patterns changed over time in relation to changes in APNS over the 8-week period. A bivariate linear mixed model approach was used to model the cross-sectional and longitudinal associations with each of the 10 RDoC constructs. Results Overall, participants reporting more pain in the weeks following trauma also showed objectively less 24-hour activity variance (Pearson correlation = -0.14, p = 0.001). An improving pain trajectory over the 8 weeks was associated with increases in daily activity (Pearson correlation = -0.14, p &amp;lt; 0.001) variability in activity (Pearson correlation = -0.12, p &amp;lt; 0.001), and increases in sleep consolidation (Pearson correlation = 0.013, p &amp;lt; 0.001). Within subjects, an increasing number of transitions between sleep and wake over the study period was also associated with worsening self-reported anxiety (Pearson correlation = 0.06, p = 0.003) and sleep problems (Pearson correlation = 0.10, p = 0.003) Conclusion After a traumatic event, several 24-hour activity biomarkers were associated with APNS. Results suggest pain, activity, and sleep highly influence each other in the weeks following a traumatic event, and 24-hour activity patterns should be considered when making predictions about who is likely to recover from trauma. Targeted interventions for sleep and pain may promote recovery in the other domains. Support (If Any) NIMH U01MH110925, US Army Medical Research and Material Command, the One Mind Foundation, The Mayday Fund, Verily Life Sciences. LDS is supported by a Department of Veterans Affairs Career Development Award (IK2CX002032).

Empirical comparisons of meta-analysis methods for diagnostic studies: a meta-epidemiological study

ObjectivesSeveral methods are commonly used for meta-analyses of diagnostic studies, such as the bivariate linear mixed model (LMM). It estimates the overall sensitivity, specificity, their correlation, diagnostic OR (DOR) and...

Association between housing and health of refugees and asylum seekers in Germany: explorative cluster and mixed model analysis

BackgroundAccommodation for asylum seekers and refugees (ASR) in Germany differs in many ways depending on a range of political, structural, social, and environmental factors. These contextual differences present a challenge for assessing health impacts of refugee accommodation. We aimed to devise a broad typology of refugee accommodation that allows to assess associations between housing and health of ASR.MethodsWe performed a cluster analysis of population-based, cross-sectional secondary data in Germany to identify clusters of refugee accommodation. We then assessed health disparities across clusters by performing bivariate analysis and linear mixed model regression analysis.ResultsWe identified four clusters, three of them reflected different types of private accommodation and one pointed to collective accommodation. The collective accommodation cluster clearly differed from the private accommodation clusters in terms of space, area, level of restrictions, social connections and respondent satisfaction. Across private accommodation clusters we also found differences in space, area, and level of restrictions. In regression analysis, belonging to one of the private accommodation cluster was significantly associated with better mental health compared to belonging to the collective accommodation cluster. Physical health was significantly lower in one private accommodation cluster characterized by poor access to public transport and a higher level of restrictions compared to a private accommodation cluster showing better connections and a lower level of restrictions.ConclusionWe demonstrate that unfavourable conditions cluster in collective accommodation with negative outcomes for mental health but not for physical health. We also found health disparities across types of private accommodation. We conclude that housing plays a role in the production of health inequalities in ASR but needs to be assessed in a differentiated, multidimensional way.

Genetic variability in the feeding behavior of crossbred growing cattle and associations with performance and feed efficiency.

The objectives of the present study were to estimate genetic parameters for several feeding behavior traits in growing cattle, as well as the genetic associations among and between feeding behavior and both performance and feed efficiency traits. An additional objective was to investigate the use of feeding behavior traits as predictors of genetic merit for feed intake. Feed intake and live-weight data on 6,088 growing cattle were used of which 4,672 had ultrasound data and 1,548 had feeding behavior data. Feeding behavior traits were defined based on individual feed events or meal events (where individual feed events were grouped into meals). Univariate and bivariate animal linear mixed models were used to estimate (co)variance components. Heritability estimates (± SE) for the feeding behavior traits ranged from 0.19 ± 0.08 for meals per day to 0.61 ± 0.10 for feeding time per day. The coefficient of genetic variation per trait varied from 5% for meals per day to 22% for the duration of each feed event. Genetically heavier cattle, those with a higher daily energy intake (MEI), or those that grew faster had a faster feeding rate, as well as a greater energy intake per feed event and per meal. Better daily feed efficiency (i.e., lower residual energy intake) was genetically associated with both a shorter feeding time per day and shorter meal time per day. In a validation population of 321 steers and heifers, the ability of estimated breeding values (EBV) for MEI to predict (adjusted) phenotypic MEI was demonstrated; EBVs for MEI were estimated using multi-trait models with different sets of predictor traits such as liveweight and/or feeding behaviors. The correlation (± SE) between phenotypic MEI and EBV for MEI marginally improved (P < 0.001) from 0.64 ± 0.03 to 0.68 ± 0.03 when feeding behavior phenotypes from the validation population were included in a genetic evaluation that already included phenotypic mid-test metabolic live-weight from the validation population. This is one of the largest studies demonstrating that significant exploitable genetic variation exists in the feeding behavior of young crossbred growing cattle; such feeding behavior traits are also genetically correlated with several performance and feed efficiency metrics. Nonetheless, there was only a marginal benefit to the inclusion of time-related feeding behavior phenotypes in a genetic evaluation for MEI to improve the precision of the EBVs for this trait.

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study.

BackgroundDepression is a prevalent mental health challenge. Current depression assessment methods using self-reported and clinician-administered questionnaires have limitations. Instrumenting smartphones to passively and continuously collect moment-by-moment data sets to quantify human behaviors has the potential to augment current depression assessment methods for early diagnosis, scalable, and longitudinal monitoring of depression.ObjectiveThe objective of this study was to investigate the feasibility of predicting depression with human behaviors quantified from smartphone data sets, and to identify behaviors that can influence depression.MethodsSmartphone data sets and self-reported 8-item Patient Health Questionnaire (PHQ-8) depression assessments were collected from 629 participants in an exploratory longitudinal study over an average of 22.1 days (SD 17.90; range 8-86). We quantified 22 regularity, entropy, and SD behavioral markers from the smartphone data. We explored the relationship between the behavioral features and depression using correlation and bivariate linear mixed models (LMMs). We leveraged 5 supervised machine learning (ML) algorithms with hyperparameter optimization, nested cross-validation, and imbalanced data handling to predict depression. Finally, with the permutation importance method, we identified influential behavioral markers in predicting depression.ResultsOf the 629 participants from at least 56 countries, 69 (10.97%) were females, 546 (86.8%) were males, and 14 (2.2%) were nonbinary. Participants’ age distribution is as follows: 73/629 (11.6%) were aged between 18 and 24, 204/629 (32.4%) were aged between 25 and 34, 156/629 (24.8%) were aged between 35 and 44, 166/629 (26.4%) were aged between 45 and 64, and 30/629 (4.8%) were aged 65 years and over. Of the 1374 PHQ-8 assessments, 1143 (83.19%) responses were nondepressed scores (PHQ-8 score <10), while 231 (16.81%) were depressed scores (PHQ-8 score ≥10), as identified based on PHQ-8 cut-off. A significant positive Pearson correlation was found between screen status–normalized entropy and depression (r=0.14, P<.001). LMM demonstrates an intraclass correlation of 0.7584 and a significant positive association between screen status–normalized entropy and depression (β=.48, P=.03). The best ML algorithms achieved the following metrics: precision, 85.55%-92.51%; recall, 92.19%-95.56%; F1, 88.73%-94.00%; area under the curve receiver operating characteristic, 94.69%-99.06%; Cohen κ, 86.61%-92.90%; and accuracy, 96.44%-98.14%. Including age group and gender as predictors improved the ML performances. Screen and internet connectivity features were the most influential in predicting depression.ConclusionsOur findings demonstrate that behavioral markers indicative of depression can be unobtrusively identified from smartphone sensors’ data. Traditional assessment of depression can be augmented with behavioral markers from smartphones for depression diagnosis and monitoring.

The assessment of circulating cell-free DNA as a diagnostic tool for breast cancer: an updated systematic review and meta-analysis of quantitative and qualitative ssays.

This updated meta-analysis aimed to assess the diagnostic accuracy of circulating cell-free DNA (cfDNA) in breast cancer (BC). An extensive systematic search was performed in PubMed, Scopus, Embase, and Science Direct databases to retrieve all related literature. Various diagnostic estimates, including sensitivity (SE), specificity (SP), likelihood ratios (LRs), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (sROC) curve, were also calculated using bivariate linear mixed models. In this meta-analysis, 57 unique articles (130 assays)on 4246 BC patients and 2,952 controls, were enrolled. For quantitative approaches, pooled SE, SP, PLR, NLR, DOR, and AUC were obtained as 0.80, 0.88, 6.7, 0.23, 29, and 0.91, respectively. Moreover, for qualitative approaches, pooled SE and SP for diagnostic performance were obtained as 0.36 and 0.98, respectively. In addition, PLR was 14.9 and NLR was 0.66. As well, the combined DOR was 23, and the AUC was 0.79. Regardless of promising SE and SP, analysis of LRs suggested that quantitative assays are not robust enough neither for BC confirmation nor for its exclusion. On the other hand, qualitative assays showed satisfying performance only for confirming the diagnosis of BC, but not for its exclusion. Furthermore, qualitative cfDNA assays showed a better diagnostic performance in patients at the advanced stage of cancer, which represented no remarkable clinical significance as a biomarker for early detection.

Statistical estimation and comparison of group-specific bivariate correlation coefficients in family-type clustered studies

Bivariate correlation coefficients (BCCs) are often calculated to gauge the relationship between two variables in medical research. In a family-type clustered design where multiple participants from same units/families are enrolled, BCCs can be defined and estimated at various hierarchical levels (subject level, family level and marginal BCC). Heterogeneity usually exists between subject groups and, as a result, subject level BCCs may differ between subject groups. In the framework of bivariate linear mixed effects modeling, we define and estimate BCCs at various hierarchical levels in a family-type clustered design, accommodating subject group heterogeneity. Simplified and modified asymptotic confidence intervals are constructed to the BCC differences and Wald type tests are conducted. A real-world family-type clustered study of Alzheimer disease (AD) is analyzed to estimate and compare BCCs among well-established AD biomarkers between mutation carriers and non-carriers in autosomal dominant AD asymptomatic individuals. Extensive simulation studies are conducted across a wide range of scenarios to evaluate the performance of the proposed estimators and the type-I error rate and power of the proposed statistical tests. Abbreviations: BCC: bivariate correlation coefficient; BLM: bivariate linear mixed effects model; CI: confidence interval; AD: Alzheimer’s disease; DIAN: The Dominantly Inherited Alzheimer Network; SA: simple asymptotic; MA: modified asymptotic

Diagnostic performance of circulating cell-free DNA for hepatocellular carcinoma: a systematic review and meta-analysis.

Background: We aimed to assess the diagnostic performance of circulating cell-free DNA (cfDNA) in hepatocellular carcinoma (HCC). Materials & methods: After a systematic literature search bivariate linear mixed models wereused to integrate sensitivity, specificity, positive likelihood ratio, negative likelihood ratioand diagnostic odds ratio. The area under receiver operating characteristics curvesof the included studies was used to estimate the diagnostic value. Results: Thirty-eight articles enrolled in quantitative synthesis. In overall analysis the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and area under receiver operating characteristics curves for cfDNA in distinguishing HCC patients from healthy controls were 0.54, 0.90, 5.23, 0.51, 10.27and 0.82, respectively. Conclusion: This study suggests that cfDNA has a promising diagnostic accuracy in detection of HCC.

Associations between cognitive and brain volume changes in cognitively normal older adults

Investigation of relationships between age-related changes in regional brain volumes and changes in domain-specific cognition could provide insights into the neural underpinnings of individual differences in cognitive aging. Domain-specific cognition (memory, verbal fluency, visuospatial ability) and tests of executive function and attention (Trail-Making Test Part A and B) and 47 brain volumes of interest (VOIs) were assessed in 836 Baltimore Longitudinal Study of Aging participants with mean follow-up of 4.1 years (maximum 23.1 years). To examine the correlation between changes in domain-specific cognition and changes in brain volumes, we used bivariate linear mixed effects models with unstructured variance-covariance structure to estimate longitudinal trajectories for each variable of interest and correlations among the random effects of these measures. Higher annual rates of memory decline were associated with greater volume loss in 14 VOIs primarily within the temporal and occipital lobes. Verbal fluency decline was associated with greater ventricular enlargement and volume loss in 24 VOIs within the frontal, temporal, and parietal lobes. Decline in visuospatial ability was associated with volume loss in 3 temporal and parietal VOIs. Declines on the attentional test were associated with volume loss in 4 VOIs located within temporal and parietal lobes. Greater declines on the executive function test were associated with greater ventricular enlargement and volume loss in 10 frontal, parietal, and temporal VOIs. Our findings highlight domain-specific patterns of regional brain atrophy that may contribute to individual differences in cognitive aging.

Small area estimation of expenditure means and ratios under a unit-level bivariate linear mixed model

Under a unit-level bivariate linear mixed model, this paper introduces small area predictors of expenditure means and ratios, and derives approximations and estimators of the corresponding mean squared errors. For the considered model, the REML estimation method is implemented. Several simulation experiments, designed to analyze the behavior of the introduced fitting algorithm, predictors and mean squared error estimators, are carried out. An application to real data from the Spanish household budget survey illustrates the behavior of the proposed statistical methodology. The target is the estimation of means of food and non-food household annual expenditures and of ratios of food household expenditures by Spanish provinces.

Spatial Aspects of Gardens Drive Ranging in Urban Foxes (Vulpes vulpes): The Resource Dispersion Hypothesis Revisited.

Red foxes are a well-established species of urban ecosystems in the UK and worldwide. Understanding the spatial ecology of foxes in urban landscapes is important for enhancement of urban biodiversity and effective disease management. The Resource Dispersion Hypothesis (RDH) holds that territory (home range) size is linked to distribution and richness of habitat patches such that aggregation of rich resources should be negatively associated with range size. Here, we tested the RDH on a sample of 20 red foxes (Vulpes vulpes) in the city of Brighton and Hove. We focused on residential garden areas, as foxes were associated with these in previous studies. We equipped 12 male and 8 female foxes with GPS collars recording at 15 min intervals during discrete seasons over four years. We regressed fox core area size against garden size, number of garden patches, and edge density within and between patches as extracted from GIS in a series of bivariate linear mixed models. We found that foxes used smaller core areas where gardens were large and well-connected and larger core areas where numerous, smaller gardens were fragmented by internal barriers (e.g., fences, walls) or bisected by other habitats such as managed grassland or built-up areas. Our findings confirm the RDH and help to inform future urban planning for wildlife.