Tree-based QTL mapping with expected local genetic relatedness matrices.

Understanding the genetic basis of complex phenotypes is a central pursuit of genetics. Genome-wide association studies (GWASs) are a powerful way to find genetic loci associated with phenotypes. GWASs are widely and successfully used, but they face challenges related to the fact that variants are tested for association with a phenotype independently, whereas in reality variants at different sites are correlated because of their shared evolutionary history. One way to model this shared history is through the ancestral recombination graph (ARG), which encodes a series of local coalescent trees. Recent computational and methodological breakthroughs have made it feasible to estimate approximate ARGs from large-scale samples. Here, we explore the potential of an ARG-based approach to quantitative-trait locus (QTL) mapping, echoing existing variance-components approaches. We propose a framework that relies on the conditional expectation of a local genetic relatedness matrix (local eGRM) given the ARG. Simulations show that our method is especially beneficial for finding QTLs in the presence of allelic heterogeneity. By framing QTL mapping in terms of the estimated ARG, we can also facilitate the detection of QTLs in understudied populations. We use local eGRM to analyze two chromosomes containing known body size loci in a sample of Native Hawaiians. Our investigations can provide intuition about the benefits of using estimated ARGs in population- and statistical-genetic methods in general.

A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data.

With the steadily increasing abundance of longitudinal neuroimaging studies with large sample sizes and multiple repeated measures, questions arise regarding the appropriate modeling of variance and covariance. The current study examined the influence of standard classes of variance-covariance structures in linear mixed effects (LME) modeling of fMRI data from patients with pediatric mild traumatic brain injury (pmTBI; N = 181) and healthy controls (N = 162). During two visits, participants performed a cognitive control fMRI paradigm that compared congruent and incongruent stimuli. The hemodynamic response function was parsed into peak and late peak phases. Data were analyzed with a 4-way (GROUP×VISIT×CONGRUENCY×PHASE) LME using AFNI's 3dLME and compound symmetry (CS), autoregressive process of order 1 (AR1), and unstructured (UN) variance-covariance matrices. Voxel-wise results dramatically varied both within the cognitive control network (UN>CS for CONGRUENCY effect) and broader brain regions (CS>UN for GROUP:VISIT) depending on the variance-covariance matrix that was selected. Additional testing indicated that both model fit and estimated standard error were superior for the UN matrix, likely as a result of the modeling of individual terms. In summary, current findings suggest that the interpretation of results from complex designs is highly dependent on the selection of the variance-covariance structure using LME modeling.

A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

Relationship between Intraocular Pressure Fluctuation and Visual Field Progression Rates in the United Kingdom Glaucoma Treatment Study.

PURPOSE: To investigate whether intraocular pressure (IOP) fluctuation is associated independently with the rate of visual field (VF) progression in the United Kingdom Glaucoma Treatment Study. DESIGN: Randomized, double-masked, placebo-controlled multicenter trial. PARTICIPANTS: Participants with ≥5 VFs (213 placebo, 217 treatment). METHODS: Associations between IOP metrics and VF progression rates (mean deviation [MD] and five fastest locations) were assessed with linear mixed models. Fluctuation variables were mean Pascal ocular pulse amplitude (OPA), standard deviation (SD) of diurnal Goldmann IOP (diurnal fluctuation), and SD of Goldmann IOP at all visits (long-term fluctuation). Fluctuation values were normalized for mean IOP to make them independent from the mean IOP. Correlated nonfluctuation IOP metrics (baseline, peak, mean, supine, and peak phasing IOP) were combined with principal component analysis, and principal component 1 (PC1) was included as a covariate. Interactions between covariates and time from baseline modeled the effect of the variables on VF rates. Analyses were conducted separately in the two treatment arms. MAIN OUTCOME MEASURES: Associations between IOP fluctuation metrics and rates of MD and the five fastest test locations. RESULTS: In the placebo arm, only PC1 was associated significantly with the MD rate (estimate, -0.19 dB/year [standard error (SE), 0.04 dB/year]; P < 0.001), whereas normalized IOP fluctuation metrics were not. No variable was associated significantly with MD rates in the treatment arm. For the fastest five locations in the placebo group, PC1 (estimate, -0.58 dB/year [SE, 0.16 dB/year]; P < 0.001), central corneal thickness (estimate, 0.26 dB/year [SE, 0.10 dB/year] for 10 µm thicker; P = 0.01) and normalized OPA (estimate, -3.50 dB/year [SE, 1.04 dB/year]; P = 0.001) were associated with rates of progression; normalized diurnal and long-term IOP fluctuations were not. In the treatment group, only PC1 (estimate, -0.27 dB/year [SE, 0.12 dB/year]; P = 0.028) was associated with the rates of progression. CONCLUSIONS: No evidence supports that either diurnal or long-term IOP fluctuation, as measured in clinical practice, are independent factors for glaucoma progression; other aspects of IOP, including mean IOP and peak IOP, may be more informative. Ocular pulse amplitude may be an independent factor for faster glaucoma progression. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

The Benefits of Permutation-Based Genome-Wide Association Studies.

Linear mixed models (LMMs) are a commonly used method for genome-wide association studies (GWAS) that aim to detect associations between genetic markers and phenotypic measurements in a population of individuals while accounting for population structure and cryptic relatedness. In a standard GWAS, hundreds of thousands to millions of statistical tests are performed, requiring control for multiple hypothesis testing. Typically, static corrections that penalize the number of tests performed are used to control for the family-wise error rate, which is the probability of making at least one false positive. However, it has been shown that in practice this threshold is too conservative for normally distributed phenotypes and not stringent enough for non-normally distributed phenotypes. Therefore, permutation-based LMM approaches have recently been proposed to provide a more realistic threshold that takes phenotypic distributions into account. In this work, we will discuss the advantages of permutation-based GWAS approaches, including new simulations and results from a re-analysis of all publicly available Arabidopsis thaliana phenotypes from the AraPheno database.

Efficient computation of high-dimensional penalized generalized linear mixed models by latent factor modeling of the random effects.

Modern biomedical datasets are increasingly high-dimensional and exhibit complex correlation structures. Generalized linear mixed models (GLMMs) have long been employed to account for such dependencies. However, proper specification of the fixed and random effects in GLMMs is increasingly difficult in high dimensions, and computational complexity grows with increasing dimension of the random effects. We present a novel reformulation of the GLMM using a factor model decomposition of the random effects, enabling scalable computation of GLMMs in high dimensions by reducing the latent space from a large number of random effects to a smaller set of latent factors. We also extend our prior work to estimate model parameters using a modified Monte Carlo Expectation Conditional Minimization algorithm, allowing us to perform variable selection on both the fixed and random effects simultaneously. We show through simulation that through this factor model decomposition, our method can fit high-dimensional penalized GLMMs faster than comparable methods and more easily scale to larger dimensions not previously seen in existing approaches.

A mixed model approach to estimate the survivor average causal effect in cluster-randomized trials.

In many medical studies, the outcome measure (such as quality of life, QOL) for some study participants becomes informatively truncated (censored, missing, or unobserved) due to death or other forms of dropout, creating a nonignorable missing data problem. In such cases, the use of a composite outcome or imputation methods that fill in unmeasurable QOL values for those who died rely on strong and untestable assumptions and may be conceptually unappealing to certain stakeholders when estimating a treatment effect. The survivor average causal effect (SACE) is an alternative causal estimand that surmounts some of these issues. While principal stratification has been applied to estimate the SACE in individually randomized trials, methods for estimating the SACE in cluster-randomized trials are currently limited. To address this gap, we develop a mixed model approach along with an expectation-maximization algorithm to estimate the SACE in cluster-randomized trials. We model the continuous outcome measure with a random intercept to account for intracluster correlations due to cluster-level randomization, and model the principal strata membership both with and without a random intercept. In simulations, we compare the performance of our approaches with an existing fixed-effects approach to illustrate the importance of accounting for clustering in cluster-randomized trials. The methodology is then illustrated using a cluster-randomized trial of telecare and assistive technology on health-related QOL in the elderly.

Multivariate probit linear mixed models for multivariate longitudinal binary data.

When analyzing multivariate longitudinal binary data, we estimate the effects on the responses of the covariates while accounting for three types of complex correlations present in the data. These include the correlations within separate responses over time, cross-correlations between different responses at different times, and correlations between different responses at each time point. The number of parameters thus increases quadratically with the dimension of the correlation matrix, making parameter estimation difficult; the estimated correlation matrix must also meet the positive definiteness constraint. The correlation matrix may additionally be heteroscedastic; however, the matrix structure is commonly considered to be homoscedastic and constrained, such as exchangeable or autoregressive with order one. These assumptions are overly strong, resulting in skewed estimates of the covariate effects on the responses. Hence, we propose probit linear mixed models for multivariate longitudinal binary data, where the correlation matrix is estimated using hypersphere decomposition instead of the strong assumptions noted above. Simulations and real examples are used to demonstrate the proposed methods. An open source R package, BayesMGLM, is made available on GitHub at https://github.com/kuojunglee/BayesMGLM/ with full documentation to produce the results.

Safety signal detection with control of latent factors.

Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.

Planning stepped wedge cluster randomized trials to detect treatment effect heterogeneity.

Stepped wedge design is a popular research design that enables a rigorous evaluation of candidate interventions by using a staggered cluster randomization strategy. While analytical methods were developed for designing stepped wedge trials, the prior focus has been solely on testing for the average treatment effect. With a growing interest on formal evaluation of the heterogeneity of treatment effects across patient subpopulations, trial planning efforts need appropriate methods to accurately identify sample sizes or design configurations that can generate evidence for both the average treatment effect and variations in subgroup treatment effects. To fill in that important gap, this article derives novel variance formulas for confirmatory analyses of treatment effect heterogeneity, that are applicable to both cross-sectional and closed-cohort stepped wedge designs. We additionally point out that the same framework can be used for more efficient average treatment effect analyses via covariate adjustment, and allows the use of familiar power formulas for average treatment effect analyses to proceed. Our results further sheds light on optimal design allocations of clusters to maximize the weighted precision for assessing both the average and heterogeneous treatment effects. We apply the new methods to the Lumbar Imaging with Reporting of Epidemiology Trial, and carry out a simulation study to validate our new methods.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

BACKGROUND: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs. METHODS: We compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed. RESULTS: For most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate. CONCLUSIONS: We recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

Lifestyle-related factors in late midlife as predictors of frailty from late midlife into old age: a longitudinal birth cohort study.

BACKGROUND: Few studies have examined longitudinal changes in lifestyle-related factors and frailty. METHODS: We examined the association between individual lifestyle factors (exercise, diet, sleep, alcohol, smoking and body composition), their sum at baseline, their change over the 17-year follow-up and the rate of change in frailty index values using linear mixed models in a cohort of 2,000 participants aged 57-69 years at baseline. RESULTS: A higher number of healthy lifestyle-related factors at baseline was associated with lower levels of frailty but not with its rate of change from late midlife into old age. Participants who stopped exercising regularly (adjusted ß × Time = 0.19, 95%CI = 0.10, 0.27) and who began experiencing sleeping difficulties (adjusted ß × Time = 0.20, 95%CI = 0.10, 0.31) experienced more rapid increases in frailty from late midlife into old age. Conversely, those whose sleep improved (adjusted ß × Time = -0.10, 95%CI = -0.23, -0.01) showed a slower increase in frailty from late midlife onwards. Participants letting go of lifestyle-related factors (decline by 3+ factors vs. no change) became more frail faster from late midlife into old age (adjusted ß × Time = 0.16, 95% CI = 0.01, 0.30). CONCLUSIONS: Lifestyle-related differences in frailty were already evident in late midlife and persisted into old age. Adopting one new healthy lifestyle-related factor had a small impact on a slightly less steeply increasing level of frailty. Maintaining regular exercise and sleeping habits may help prevent more rapid increases in frailty.

HEPPI: a randomized controlled trial of the efficacy of a cognitive-emotional intervention for homebound older adults.

OBJECTIVES: This randomized controlled trial aimed to assess the efficacy of the Homebound Elderly People Psychotherapeutic Intervention (HEPPI), a home-delivered cognitive-emotional intervention, among the homebound older population presenting with mild cognitive impairment and depressive or anxiety symptoms. METHODS: Participants were randomly assigned either to the intervention group or the treatment-as-usual group and completed baseline, post-intervention, and three-month follow-up assessments. Changes in episodic memory and symptoms of depression and anxiety were the primary outcomes. Secondary outcomes included changes in global cognition, attentional control, subjective memory complaints, functional status, and quality of life. Data were analyzed on an intention-to-treat basis employing a linear mixed models approach. ClinicalTrials.gov identifier: NCT05499767. RESULTS: Compared with the treatment-as-usual group, the HEPPI group reported significant immediate improvement in cognition, mood, and daily functional performance. Positive effects of HEPPI were maintained over the follow-up phase only in depressive symptomatology, perceived incapacity to perform advanced instrumental activities of daily living, and self-reported emotional ability. A significant impact of the intervention on the subjective memory complaints level was observed only three months after the intervention. CONCLUSIONS: This study suggests that HEPPI may be a promising home-delivered cognitive-emotional intervention to help homebound older adults improve their mental health.

Exploring the Interplay between the Hologenome and Complex Traits in Bovine and Porcine Animals Using Genome-Wide Association Analysis.

Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host organism's collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs' gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.

Multilevel modeling in single-case studies with zero-inflated and overdispersed count data.

Count outcomes are frequently encountered in single-case experimental designs (SCEDs). Generalized linear mixed models (GLMMs) have shown promise in handling overdispersed count data. However, the presence of excessive zeros in the baseline phase of SCEDs introduces a more complex issue known as zero-inflation, often overlooked by researchers. This study aimed to deal with zero-inflated and overdispersed count data within a multiple-baseline design (MBD) in single-case studies. It examined the performance of various GLMMs (Poisson, negative binomial [NB], zero-inflated Poisson [ZIP], and zero-inflated negative binomial [ZINB] models) in estimating treatment effects and generating inferential statistics. Additionally, a real example was used to demonstrate the analysis of zero-inflated and overdispersed count data. The simulation results indicated that the ZINB model provided accurate estimates for treatment effects, while the other three models yielded biased estimates. The inferential statistics obtained from the ZINB model were reliable when the baseline rate was low. However, when the data were overdispersed but not zero-inflated, both the ZINB and ZIP models exhibited poor performance in accurately estimating treatment effects. These findings contribute to our understanding of using GLMMs to handle zero-inflated and overdispersed count data in SCEDs. The implications, limitations, and future research directions are also discussed.

Clinical factors and early life experiences associated with therapeutic alliance development in treatment for depression or binge eating.

OBJECTIVE: This study examines childhood and clinical factors theorized to impact therapeutic alliance development over the course of psychotherapy. METHOD: Raters assessed the therapeutic alliance of 212 client-therapist dyads, participating in two randomized controlled trials of schema therapy and cognitive behavioural therapy for binge eating or major depression, at three time points. Linear mixed models were used to characterize therapeutic alliance development over time and assess the influence of childhood trauma, perceived parental bonding, diagnosis and therapy type on scores. RESULTS: Participants differed in initial alliance ratings for all subscales but had similar growth trajectories in all but the patient hostility subscale. A diagnosis of bulimia nervosa or binge eating disorder predicted greater initial levels of client distress, client dependency and overall client contribution to a strong therapeutic alliance, compared with a diagnosis of depression. Therapy type, childhood trauma and perceived parental bonds did not predict alliance scores. CONCLUSION: Findings highlight the potential influence of clinical and personal characteristics on alliance strength and development, with implications for maximizing treatment outcomes through anticipating and responding to these challenges.

A Description of the Statistical Methods for the Vaccine Impact on Diarrhea in Africa (VIDA) Study.

BACKGROUND: Diarrheal diseases remain a health threat to children in low- and middle-income countries. The Vaccine Impact on Diarrhea in Africa (VIDA) study was a 36-month, prospective, matched case-control study designed to estimate the etiology, incidence, and adverse clinical consequences of moderate-to-severe diarrhea (MSD) in children aged 0-59 months. VIDA was conducted following rotavirus vaccine introduction at 3 censused sites in sub-Saharan Africa that participated in the Global Enteric Multicenter Study (GEMS) ∼10 years earlier. We describe the study design and statistical methods of VIDA and where they differ from GEMS. METHODS: We aimed to enroll 8-9 MSD cases every 2 weeks from sentinel health centers in 3 age strata (0-11, 12-23, 24-59 months) and 1 to 3 controls matched by age, sex, date of case enrollment, and village. Clinical, epidemiological, and anthropometric data were collected at enrollment and ∼60 days later. A stool specimen collected at enrollment was analyzed by both conventional methods and quantitative PCR for enteric pathogens. For the matched case-control study, we estimated the population-based, pathogen-specific attributable fraction (AF) and attributable incidence adjusted for age, site, and other pathogens, and identified episodes attributable to a specific pathogen for additional analyses. A prospective cohort study nested within the original matched case-control study allowed assessment of (1) the association between potential risk factors and outcomes other than MSD status and (2) the impact of MSD on linear growth. CONCLUSIONS: GEMS and VIDA together comprise the largest and most comprehensive assessment of MSD conducted to date in sub-Saharan Africa populations at highest risk for morbidity and mortality from diarrhea. The statistical methods used in VIDA have endeavored to maximize the use of available data to produce more robust estimates of the pathogen-specific disease burden that might be prevented by effective interventions.

The Effect of HIV Treatment Interruption on Subsequent Immunological Response.

Recovery of CD4-positive T lymphocyte count after initiation of antiretroviral therapy (ART) has been thoroughly examined among people with human immunodeficiency virus infection. However, immunological response after restart of ART following care interruption is less well studied. We compared CD4 cell-count trends before disengagement from care and after ART reinitiation. Data were obtained from the East Africa International Epidemiology Databases to Evaluate AIDS (IeDEA) Collaboration (2001-2011; n = 62,534). CD4 cell-count trends before disengagement, during disengagement, and after ART reinitiation were simultaneously estimated through a linear mixed model with 2 subject-specific knots placed at the times of disengagement and treatment reinitiation. We also estimated CD4 trends conditional on the baseline CD4 value. A total of 10,961 patients returned to care after disengagement from care, with the median gap in care being 2.7 (interquartile range, 2.1-5.4) months. Our model showed that CD4 cell-count increases after ART reinitiation were much slower than those before disengagement. Assuming that disengagement from care occurred 12 months after ART initiation and a 3-month treatment gap, CD4 counts measured at 3 years since ART initiation would be lower by 36.5 cells/µL than those obtained under no disengagement. Given that poorer CD4 restoration is associated with increased mortality/morbidity, specific interventions targeted at better retention in care are urgently required.

Joint frailty modeling of time-to-event data to elicit the evolution pathway of events: a generalized linear mixed model approach.

Multimorbidity constitutes a serious challenge on the healthcare systems in the world, due to its association with poorer health-related outcomes, more complex clinical management, increases in health service utilization and costs, but a decrease in productivity. However, to date, most evidence on multimorbidity is derived from cross-sectional studies that have limited capacity to understand the pathway of multimorbid conditions. In this article, we present an innovative perspective on analyzing longitudinal data within a statistical framework of survival analysis of time-to-event recurrent data. The proposed methodology is based on a joint frailty modeling approach with multivariate random effects to account for the heterogeneous risk of failure and the presence of informative censoring due to a terminal event. We develop a generalized linear mixed model method for the efficient estimation of parameters. We demonstrate the capacity of our approach using a real cancer registry data set on the multimorbidity of melanoma patients and document the relative performance of the proposed joint frailty model to the natural competitor of a standard frailty model via extensive simulation studies. Our new approach is timely to advance evidence-based knowledge to address increasingly complex needs related to multimorbidity and develop interventions that are most effective and viable to better help a large number of individuals with multiple conditions.

Mood and implicit confidence independently fluctuate at different time scales.

Mood is an important ingredient of decision-making. Human beings are immersed into a sea of ​​emotions where episodes of high mood alternate with episodes of low mood. While changes in mood are well characterized, little is known about how these fluctuations interact with metacognition, and in particular with confidence about our decisions. We evaluated how implicit measurements of confidence are related with mood states of human participants through two online longitudinal experiments involving mood self-reports and visual discrimination decision-making tasks. Implicit confidence was assessed on each session by monitoring the proportion of opt-out trials when an opt-out option was available, as well as the median reaction time on standard correct trials as a secondary proxy of confidence. We first report a strong coupling between mood, stress, food enjoyment, and quality of sleep reported by participants in the same session. Second, we confirmed that the proportion of opt-out responses as well as reaction times in non-opt-out trials provided reliable indices of confidence in each session. We introduce a normative measure of overconfidence based on the pattern of opt-out selection and the signal-detection-theory framework. Finally and crucially, we found that mood, sleep quality, food enjoyment, and stress level are not consistently coupled with these implicit confidence markers, but rather they fluctuate at different time scales: mood-related states display faster fluctuations (over one day or half-a-day) than confidence level (two-and-a-half days). Therefore, our findings suggest that spontaneous fluctuations of mood and confidence in decision making are independent in the healthy adult population.