MMGAT: a graph attention network framework for ATAC-seq motifs finding.

BACKGROUND: Motif finding in Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data is essential to reveal the intricacies of transcription factor binding sites (TFBSs) and their pivotal roles in gene regulation. Deep learning technologies including convolutional neural networks (CNNs) and graph neural networks (GNNs), have achieved success in finding ATAC-seq motifs. However, CNN-based methods are limited by the fixed width of the convolutional kernel, which makes it difficult to find multiple transcription factor binding sites with different lengths. GNN-based methods has the limitation of using the edge weight information directly, makes it difficult to aggregate the neighboring nodes' information more efficiently when representing node embedding. RESULTS: To address this challenge, we developed a novel graph attention network framework named MMGAT, which employs an attention mechanism to adjust the attention coefficients among different nodes. And then MMGAT finds multiple ATAC-seq motifs based on the attention coefficients of sequence nodes and k-mer nodes as well as the coexisting probability of k-mers. Our approach achieved better performance on the human ATAC-seq datasets compared to existing tools, as evidenced the highest scores on the precision, recall, F1_score, ACC, AUC, and PRC metrics, as well as finding 389 higher quality motifs. To validate the performance of MMGAT in predicting TFBSs and finding motifs on more datasets, we enlarged the number of the human ATAC-seq datasets to 180 and newly integrated 80 mouse ATAC-seq datasets for multi-species experimental validation. Specifically on the mouse ATAC-seq dataset, MMGAT also achieved the highest scores on six metrics and found 356 higher-quality motifs. To facilitate researchers in utilizing MMGAT, we have also developed a user-friendly web server named MMGAT-S that hosts the MMGAT method and ATAC-seq motif finding results. CONCLUSIONS: The advanced methodology MMGAT provides a robust tool for finding ATAC-seq motifs, and the comprehensive server MMGAT-S makes a significant contribution to genomics research. The open-source code of MMGAT can be found at https://github.com/xiaotianr/MMGAT , and MMGAT-S is freely available at https://www.mmgraphws.com/MMGAT-S/ .

Functional network dynamics revealed by EEG microstates reflect cognitive decline in amyotrophic lateral sclerosis.

Recent electroencephalography (EEG) studies have shown that patterns of brain activity can be used to differentiate amyotrophic lateral sclerosis (ALS) and control groups. These differences can be interrogated by examining EEG microstates, which are distinct, reoccurring topographies of the scalp's electrical potentials. Quantifying the temporal properties of the four canonical microstates can elucidate how the dynamics of functional brain networks are altered in neurological conditions. Here we have analysed the properties of microstates to detect and quantify signal-based abnormality in ALS. High-density resting-state EEG data from 129 people with ALS and 78 HC were recorded longitudinally over a 24-month period. EEG topographies were extracted at instances of peak global field power to identify four microstate classes (labelled A-D) using K-means clustering. Each EEG topography was retrospectively associated with a microstate class based on global map dissimilarity. Changes in microstate properties over the course of the disease were assessed in people with ALS and compared with changes in clinical scores. The topographies of microstate classes remained consistent across participants and conditions. Differences were observed in coverage, occurrence, duration, and transition probabilities between ALS and control groups. The duration of microstate class B and coverage of microstate class C correlated with lower limb functional decline. The transition probabilities A to D, C to B and C to B also correlated with cognitive decline (total ECAS) in those with cognitive and behavioural impairments. Microstate characteristics also significantly changed over the course of the disease. Examining the temporal dependencies in the sequences of microstates revealed that the symmetry and stationarity of transition matrices were increased in people with late-stage ALS. These alterations in the properties of EEG microstates in ALS may reflect abnormalities within the sensory network and higher-order networks. Microstate properties could also prospectively predict symptom progression in those with cognitive impairments.

Predicting lung function decline in cystic fibrosis: the impact of initiating ivacaftor therapy.

BACKGROUND: Modulator therapies that seek to correct the underlying defect in cystic fibrosis (CF) have revolutionized the clinical landscape. Given the heterogeneous nature of lung disease progression in the post-modulator era, there is a need to develop prediction models that are robust to modulator uptake. METHODS: We conducted a retrospective longitudinal cohort study of the CF Foundation Patient Registry (N = 867 patients carrying the G551D mutation who were treated with ivacaftor from 2003 to 2018). The primary outcome was lung function (percent predicted forced expiratory volume in 1 s or FEV1pp). To characterize the association between ivacaftor initiation and lung function, we developed a dynamic prediction model through covariate selection of demographic and clinical characteristics. The ability of the selected model to predict a decline in lung function, clinically known as an FEV1-indicated exacerbation signal (FIES), was evaluated both at the population level and individual level. RESULTS: Based on the final model, the estimated improvement in FEV1pp after ivacaftor initiation was 4.89% predicted (95% confidence interval [CI]: 3.90 to 5.89). The rate of decline was reduced with ivacaftor initiation by 0.14% predicted/year (95% CI: 0.01 to 0.27). More frequent outpatient visits prior to study entry and being male corresponded to a higher overall FEV1pp. Pancreatic insufficiency, older age at study entry, a history of more frequent pulmonary exacerbations, lung infections, CF-related diabetes, and use of Medicaid insurance corresponded to lower FEV1pp. The model had excellent predictive accuracy for FIES events with an area under the receiver operating characteristic curve of 0.83 (95% CI: 0.83 to 0.84) for the independent testing cohort and 0.90 (95% CI: 0.89 to 0.90) for 6-month forecasting with the masked cohort. The root-mean-square errors of the FEV1pp predictions for these cohorts were 7.31% and 6.78% predicted, respectively, with standard deviations of 0.29 and 0.20. The predictive accuracy was robust across different covariate specifications. CONCLUSIONS: The methods and applications of dynamic prediction models developed using data prior to modulator uptake have the potential to inform post-modulator projections of lung function and enhance clinical surveillance in the new era of CF care.

Hebbian learning can explain rhythmic neural entrainment to statistical regularities.

In many domains, learners extract recurring units from continuous sequences. For example, in unknown languages, fluent speech is perceived as a continuous signal. Learners need to extract the underlying words from this continuous signal and then memorize them. One prominent candidate mechanism is statistical learning, whereby learners track how predictive syllables (or other items) are of one another. Syllables within the same word predict each other better than syllables straddling word boundaries. But does statistical learning lead to memories of the underlying words-or just to pairwise associations among syllables? Electrophysiological results provide the strongest evidence for the memory view. Electrophysiological responses can be time-locked to statistical word boundaries (e.g., N400s) and show rhythmic activity with a periodicity of word durations. Here, I reproduce such results with a simple Hebbian network. When exposed to statistically structured syllable sequences (and when the underlying words are not excessively long), the network activation is rhythmic with the periodicity of a word duration and activation maxima on word-final syllables. This is because word-final syllables receive more excitation from earlier syllables with which they are associated than less predictable syllables that occur earlier in words. The network is also sensitive to information whose electrophysiological correlates were used to support the encoding of ordinal positions within words. Hebbian learning can thus explain rhythmic neural activity in statistical learning tasks without any memory representations of words. Learners might thus need to rely on cues beyond statistical associations to learn the words of their native language. RESEARCH HIGHLIGHTS: Statistical learning may be utilized to identify recurring units in continuous sequences (e.g., words in fluent speech) but may not generate explicit memory for words. Exposure to statistically structured sequences leads to rhythmic activity with a period of the duration of the underlying units (e.g., words). I show that a memory-less Hebbian network model can reproduce this rhythmic neural activity as well as putative encodings of ordinal positions observed in earlier research. Direct tests are needed to establish whether statistical learning leads to declarative memories for words.

Observer effect, quasi-probabilities and generalized Specker's boxes.

Quantum non-locality and contextuality can be simulated with quasi-probabilities, i.e. probabilities that take negative values. Here, we show that another quantum phenomenon, the observer effect, admits a quasi-probabilistic description too. We also investigate post-quantum observer effects based on the Specker's triangle scenario. This scenario comprises three observables, with the possibility of measuring two simultaneously. Represented as three boxes with a hidden ball, this scenario exhibits counterintuitive behaviour: regardless of the chosen pair of boxes, one box always contains the ball. Moreover, the scenario demonstrates a strong observer effect. When an observer selects and opens the first box, finding it empty, the ball is guaranteed to be in the second box, thereby allowing the observer to determine the ball's location among the remaining two boxes. We extend this scenario to include additional boxes and multiple balls. By employing negative probabilities, we demonstrate amplification of the observer effect. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

Agreement and disagreement in a non-classical world.

The Agreement Theorem Aumann (1976 Ann. Stat. 4, 1236-1239. (doi:10.1214/aos/1176343654)) states that if two Bayesian agents start with a common prior, then they cannot have common knowledge that they hold different posterior probabilities of some underlying event of interest. In short, the two agents cannot 'agree to disagree'. This result applies in the classical domain where classical probability theory applies. But in non-classical domains, such as the quantum world, classical probability theory does not apply. Inspired principally by their use in quantum mechanics, we employ signed probabilities to investigate the epistemics of the non-classical world. We find that here, too, it cannot be common knowledge that two agents assign different probabilities to an event of interest. However, in a non-classical domain, unlike the classical case, it can be common certainty that two agents assign different probabilities to an event of interest. Finally, in a non-classical domain, it cannot be common certainty that two agents assign different probabilities, if communication of their common certainty is possible-even if communication does not take place. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

Measures of contextuality in cyclic systems and the negative probabilities measure CNT3.

Several principled measures of contextuality have been proposed for general systems of random variables (i.e. inconsistently connected systems). One such measure is based on quasi-couplings using negative probabilities (here denoted by [Formula: see text], Dzhafarov & Kujala, 2016 Quantum interaction). Dzhafarov & Kujala (Dzhafarov & Kujala 2019 Phil. Trans. R. Soc. A 377, 20190149. (doi:10.1098/rsta.2019.0149)) introduced a measure of contextuality, [Formula: see text], that naturally generalizes to a measure of non-contextuality. Dzhafarov & Kujala (Dzhafarov & Kujala 2019 Phil. Trans. R. Soc. A 377, 20190149. (doi:10.1098/rsta.2019.0149)) additionally conjectured that in the class of cyclic systems these two measures are proportional. Here we prove that conjecture is correct. Recently, Cervantes (Cervantes 2023 J. Math. Psychol. 112, 102726. (doi:10.1016/j.jmp.2022.102726)) showed the proportionality of [Formula: see text] and the Contextual Fraction measure introduced by Abramsky & Brandenburger (Abramsky & Brandenburger 2011 New J. Phys. 13, 113036. (doi:10.1088/1367-2630/13/11/113036)). The present proof completes the description of the interrelations of all contextuality measures proposed within or translated into the Contextuality-by-Default framework so far as they pertain to cyclic systems. This article is part of the theme issue 'Quantum contextuality, causality and freedom of choice'.

An analysis of the dual burden of childhood stunting and wasting in Myanmar: a copula geoadditive modelling approach.

OBJECTIVE: To analyze the spatial variation and risk factors of the dual burden of childhood stunting and wasting in Myanmar. DESIGN: Analysis was carried out on nationally representative data obtained from the Myan-mar Demographic and Health Survey conducted during 2015-2016. Childhood stunting and wasting are used as proxies of chronic and acute childhood undernutrition. A child with standardized height-for-age Z score (HAZ) below -2 is categorized as stunted while that with a weight-for-height Z score (WHZ) below -2 as wasted. SETTING: A nationally representative sample of households from the 15 states and regions of Myanmar. PARTICIPANTS: Children under the age of five (n 4162). RESULTS: Overall marginal prevalence of childhood stunting and wasting were 28.9% (95% CI 27.5, 30.2) and 7.3% (95% CI 6.5, 8.0) while their concurrent prevalence was 1.6% (95% CI 1.2, 2.0). The study revealed mild positive association between stunting and wasting across Myanmar. Both stunting and wasting had significant spatial variation across the country with eastern regions having higher burden of stunting while southern regions having higher prevalence of wasting. Child age and maternal weight-for-height Z score had significant non- linear association with both stunting and wasting while child gender, ethnicity and household wealth quintile had significant association with stunting. CONCLUSION: The study provides data-driven evidence about the association between stunting and wasting and their spatial variation across Myanmar. The resulting insights can aid in the formulation and implementation of targeted, region-specific interventions towards improving the state of childhood under-nutrition in Myanmar.

Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias.

The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population.

Evaluation of the effectiveness of single nucleotide polymorphisms compared to microsatellite markers for parentage verification in Moroccan horses.

The International Society for Animal Genetics (ISAG) currently advocates for a transition towards single nucleotide polymorphism (SNP) markers as a potential alternative for equine parentage verification. To ascertain the efficacy of this transition, it is imperative to evaluate the performance of parentage testing using SNPs in juxtaposition with short tandem repeats (STRs). As per ISAG's recommendation, we used an equine genotyping-by-sequencing panel with 144 SNPs for this purpose. Equine parentage is currently realized using 16 microsatellites (STRs) excluding the LEX3 marker. In this study, 1074 horses were genotyped using the 144 SNPs panel, including 432 foals, 414 mares, and 228 stallions, from five different breeds: 293 Arabians, 167 Barbs, 189 Thoroughbreds, 73 Anglo-Arabians, and 352 Arabian-Barbs. As a result, two SNPs markers were eliminated from the panel system due to inconsistent amplification across all examined individuals leaving 142 SNPs markers for analysis. A comparative analysis between SNPs and STRs markers revealed that the mean expected heterozygosity was 0.457 for SNPs and 0.76 for STRs, while the mean observed heterozygosity stood at 0.472 for SNPs and 0.72 for STRs. Furthermore, the probability of identity was calculated to be 5.722 × 10-57 for SNPs and 1.25 × 10-15 for STRs markers. In alignment with the Hardy-Weinberg equilibrium in polyploids test, 110 out of the total SNPs were consistent with the Hardy-Weinberg equilibrium in polyploids test (p > 0.05). Employing both SNPs and STRs markers, the mean polymorphic information content was discerned to be 0.351 for SNPs and 0.72 for STRs. The cumulative exclusion probabilities for SNP markers exceeded 99.99%, indicating that the 142 SNPs panel might be adequate for parentage testing. In contrast, when utilizing STRs markers, the combined average exclusion probabilities for one and both parents were determined to be 99.8% and 99.9%, respectively. Our comprehensive study underscores the potential of SNPs in equine parentage verification, especially when compared to STRs in terms of exclusion probabilities. As a corollary, the application of SNPs for parentage verification and identification can significantly contribute to the conservation initiative for the five Moroccan horse breeds. Nonetheless, further research is required to address and replace the deficient SNPs within the panel.

Pseudo-value regression trees.

This paper presents a semi-parametric modeling technique for estimating the survival function from a set of right-censored time-to-event data. Our method, named pseudo-value regression trees (PRT), is based on the pseudo-value regression framework, modeling individual-specific survival probabilities by computing pseudo-values and relating them to a set of covariates. The standard approach to pseudo-value regression is to fit a main-effects model using generalized estimating equations (GEE). PRT extend this approach by building a multivariate regression tree with pseudo-value outcome and by successively fitting a set of regularized additive models to the data in the nodes of the tree. Due to the combination of tree learning and additive modeling, PRT are able to perform variable selection and to identify relevant interactions between the covariates, thereby addressing several limitations of the standard GEE approach. In addition, PRT include time-dependent effects in the node-wise models. Interpretability of the PRT fits is ensured by controlling the tree depth. Based on the results of two simulation studies, we investigate the properties of the PRT method and compare it to several alternative modeling techniques. Furthermore, we illustrate PRT by analyzing survival in 3,652 patients enrolled for a randomized study on primary invasive breast cancer.

Finite-Time H∞ Controllers Design for Stochastic Time-Delay Markovian Jump Systems with Partly Unknown Transition Probabilities.

This paper concentrates on the finite-time H∞ control problem for a type of stochastic discrete-time Markovian jump systems, characterized by time-delay and partly unknown transition probabilities. Initially, a stochastic finite-time (SFT) H∞ state feedback controller and an SFT H∞ observer-based state feedback controller are constructed to realize the closed-loop control of systems. Then, based on the Lyapunov-Krasovskii functional (LKF) method, some sufficient conditions are established to guarantee that closed-loop systems (CLSs) satisfy SFT boundedness and SFT H∞ boundedness. Furthermore, the controller gains are obtained with the use of the linear matrix inequality (LMI) approach. In the end, numerical examples reveal the reasonableness and effectiveness of the proposed designing schemes.

How the Post-Data Severity Converts Testing Results into Evidence for or against Pertinent Inferential Claims.

The paper makes a case that the current discussions on replicability and the abuse of significance testing have overlooked a more general contributor to the untrustworthiness of published empirical evidence, which is the uninformed and recipe-like implementation of statistical modeling and inference. It is argued that this contributes to the untrustworthiness problem in several different ways, including [a] statistical misspecification, [b] unwarranted evidential interpretations of frequentist inference results, and [c] questionable modeling strategies that rely on curve-fitting. What is more, the alternative proposals to replace or modify frequentist testing, including [i] replacing p-values with observed confidence intervals and effects sizes, and [ii] redefining statistical significance, will not address the untrustworthiness of evidence problem since they are equally vulnerable to [a]-[c]. The paper calls for distinguishing between unduly data-dependant 'statistical results', such as a point estimate, a p-value, and accept/reject H0, from 'evidence for or against inferential claims'. The post-data severity (SEV) evaluation of the accept/reject H0 results, converts them into evidence for or against germane inferential claims. These claims can be used to address/elucidate several foundational issues, including (i) statistical vs. substantive significance, (ii) the large n problem, and (iii) the replicability of evidence. Also, the SEV perspective sheds light on the impertinence of the proposed alternatives [i]-[iii], and oppugns [iii] the alleged arbitrariness of framing H0 and H1 which is often exploited to undermine the credibility of frequentist testing.

Survival Probability in Multidrug Resistant Pulmonary Tuberculosis Patients in a South Indian Region.

Background: Drug-resistant tuberculosis is a burgeoning threat to public health requiring novel strategies to combat the infection. Although national tuberculosis elimination programs focus on improving health services, challenges in eradicating tuberculosis still exist. Factors attributing to unfavorable outcomes are unknown in Warangal district of Telangana state. Methods: This study included 296 patients diagnosed with multidrug-resistant pulmonary tuberculosis. The study participants followed up for a maximum of 20 months to determine treatment outcomes. Statistical applications of Kaplan-Meier curve and log-rank test used to find the survival probabilities in subgroups. Results: The survival of multidrug-resistant pulmonary tuberculosis patients was ascertained, in male and female patients, aged between 31 and 50 years. Resistance to rifampicin was prominent. The study found a survival rate of 76.68% and a mortality rate of 23.31%. The log-rank test revealed a significant difference in survival in subcategories with and without comorbidities (P = .03), non-adherence to treatment (P = .0001), treatment duration (P = .02), regimens (P = .01), and grading of radiograph (P = .0001). Conclusion: This study identified factors that influenced the survival probability of multidrug-resistant pulmonary tuberculosis patients, including comorbidities, weight band, non-adherence to treatment, treatment duration, regimens, and grading of radiograph. These findings emphasize the need for enhanced management strategies to improve treatment outcomes.

Mutual information analysis of mutation, nonlinearity, and triple interactions in proteins.

Mutations are the cause of several diseases as well as the underlying force of evolution. A thorough understanding of their biophysical consequences is essential. We present a computational framework for evaluating different levels of mutual information (MI) and its dependence on mutation. We used molecular dynamics trajectories of the third PDZ domain and its different mutations. Nonlinear MI between all residue pairs are calculated by tensor Hermite polynomials up to the fifth order and compared with results from multivariate Gaussian distribution of joint probabilities. We show that MI is written as the sum of a Gaussian and a nonlinear component. Results for the PDZ domain show that the Gaussian term gives a sufficiently accurate representation of MI when compared with nonlinear terms up to the fifth order. Changes in MI between residue pairs show the characteristic patterns resulting from specific mutations. Emergence of new peaks in the MI versus residue index plots of mutated PDZ shows how mutation may change allosteric pathways. Triple correlations are characterized by evaluating MI between triplets of residues. We observed that certain triplets are strongly affected by mutation. Susceptibility of residues to perturbation is obtained by MI and discussed in terms of linear response theory.

Immunohistochemical expressions of EMT markers in pan-RAS-pERK1/2-positive tumors improve diagnosis and prognosis assessment of non-muscle invasive bladder cancer and muscle invasive bladder cancer patients.

Mutation or overexpression renders pan-RAS (rat sarcoma) proteins insensitive to inactivation. Activated pan-RAS communicates signal from the cell surface receptor to activate RAS-MAPK/ERK (RAS-mitogen-activated protein kinases/extracellular signal regulated kinases) signaling and orchestrates epithelial-to-mesenchymal transition-activating transcription factors (EMT-ATFs) reprogramming to induce EMT. Owing to limited studies available in bladder cancer, the present study is taken up to examine the expressions of the EMT-associated markers in pan-RAS-pERK1/2 (pan-RAS-phosphoERK1/2)-positive well-characterized cohort of forty-two non-muscle invasive bladder cancer (NMIBC) and forty-five muscle invasive bladder cancer (MIBC) patients. Immunohistochemical staining was performed on paraffin embedded tissue sections to determine the immunolevels and cellular localization of marker proteins. Semi-quantitative expressions of pan-RAS, pERK1/2, and EMT markers (E-cadherin, Vimentin, N-cadherin, Snail, Slug Twist, and Zeb1) were statistically examined with clinicohistopathological profile of the patients using SPSS, version 20.0 software. The study documents the diagnostic relevance of immunohistochemical expressions of pan-RAS-pERK1/2/EMT-associated markers in order to stratify NMIBC and MIBC patients. Follow-up studies supported the role of altered EMT phenotype in pan-RAS-pERK1/2-activated positive tumors with disease aggressiveness. To the best of our knowledge, our study is the first concluding the impact of altered EMT phenotype via pan-RAS-pERK1/2 axis on the short survival outcome [short overall survival (OS) (p = 0.04), short progression-free survival (PFS) (p = 0.02) and short cancer-specific survival (CSS) (p = 0.03)] of muscle invasive bladder cancer patients.

A hierarchical model for analyzing multisite individual-level disease surveillance data from multiple systems.

Passive surveillance systems are widely used to monitor diseases occurrence over wide spatial areas due to their cost-effectiveness and integration into broadly distributed healthcare systems. However, such systems are generally associated with imperfect ascertainment of disease cases and with heterogeneous capture probabilities arising from factors such as differential access to care. Augmenting passive surveillance systems with other surveillance efforts provides a way to estimate the true number of incident cases. We develop a hierarchical modeling framework for analyzing data from multiple surveillance systems that allows for individual-level covariate-dependent heterogeneous capture probabilities, and borrows information across surveillance sites to improve estimation of the true number of incident cases. Inference is carried out via a two-stage Bayesian procedure. Simulation studies illustrated superior performance of the proposed approach with respect to bias, root mean square error, and coverage compared to a model that does not borrow information across sites. We applied the proposed model to data from three surveillance systems reporting pulmonary tuberculosis (PTB) cases in a major center of ongoing transmission in China. The analysis yielded bias-corrected estimates of PTB cases from the passive system and led to the identification of risk factors associated with PTB rates, as well as factors influencing the operating characteristics of the implemented surveillance systems.

A longitudinal transition imputation model for categorical data applied to a large registry dataset.

Imputation of longitudinal categorical covariates with several waves and many predictors is cumbersome in terms of implausible transitions, colinearity, and overfitting. We designed a simulation study with data obtained from a general practitioners' morbidity registry in Belgium for three waves, with smoking as the longitudinal covariate of interest. We set varying proportions of data on smoking to missing completely at random and missing not at random with proportions of missingness equal to 10%, 30%, 50%, and 70%. This study proposed a 3-stage approach that allows flexibility when imputing time-dependent categorical covariates. First, multiple imputation using fully conditional specification or multiple imputation for the predictor variables was deployed using the wide format such that previous and future information of the same patient was utilized. Second, a joint Markov transition model for initial, forward, backward, and intermittent probabilities was developed for each imputed dataset. Finally, this transition model was used for imputation. We compared the performance of this methodology with an analyses of the complete data and with listwise deletion in terms of bias and root mean square error. Next, we applied this methodology in a clinical case for years 2017 to 2021, where we estimated the effect of several covariates on the pneumococcal vaccination. This methodological framework ensures that the plausibility of transitions is preserved, overfitting and colinearity issues are resolved, and confounders can be utilized. Finally, a companion R package was developed to enable the replication and easy application of this methodology.

Migratory connectivity of North American waterfowl across administrative flyways.

Management of waterfowl that migrate seasonally across North America occurs within four flyways that were delineated in the early 1900s to include the annual movements of populations. Movements may have changed over the past century since the administrative flyways were established, and may do so while management plans are in use, so information about transitions among flyways through time can illustrate how management assumptions may change. Today there are more than 12 million records from 60 years of migratory waterfowl band recoveries to assess adaptive management approaches that will be most effective when they account for movements within and between flyways. We examined how much the movement of North American waterfowl occurs between flyways, whether those movements have changed through time, and whether movements of mallards are representative of multiple species, as suggested by current harvest management strategies. We estimated the probability a duck would transition from one flyway to another and the strength of migratory connectivity (MC) for each species within and among flyways. We used capture-mark-recovery models to estimate population-specific movement within and among flyways (transition probabilities) for 15 migratory waterfowl species that were banded during breeding and recovered during winter. We developed new functionality in the R package MigConnectivity to estimate the species-specific strength of MC using transition probability samples from the capture-mark-recovery models. We found the regular movement of duck populations among flyways, overall weak MC, and no consistent change in migratory movements through time. Mallard movements were median among all duck species, but significantly different from many species, particularly diving ducks. Despite the significant movement between flyways, our work suggests flyway management of waterfowl matches many of the seasonal movements of these species when considering mid-continent flyway management. We recommend models accounting for all transition probabilities between populations and regularly estimating harvest derivations, transition probabilities, and MC metrics to verify that the current movements match model assumptions.

Estimating disease incidence rates and transition probabilities in elderly patients using multi-state models: a case study in fragility fracture using a Bayesian approach.

BACKGROUND: Multi-state models are complex stochastic models which focus on pathways defined by the temporal and sequential occurrence of numerous events of interest. In particular, the so-called illness-death models are especially useful for studying probabilities associated to diseases whose occurrence competes with other possible diseases, health conditions or death. They can be seen as a generalization of the competing risks models, which are widely used to estimate disease-incidences among populations with a high risk of death, such as elderly or cancer patients. The main advantage of the aforementioned illness-death models is that they allow the treatment of scenarios with non-terminal competing events that may occur sequentially, which competing risks models fail to do. METHODS: We propose an illness-death model using Cox proportional hazards models with Weibull baseline hazard functions, and applied the model to a study of recurrent hip fracture. Data came from the PREV2FO cohort and included 34491 patients aged 65 years and older who were discharged alive after a hospitalization due to an osteoporotic hip fracture between 2008-2015. We used a Bayesian approach to approximate the posterior distribution of each parameter of the model, and thus cumulative incidences and transition probabilities. We also compared these results with a competing risks specification. RESULTS: Posterior transition probabilities showed higher probabilities of death for men and increasing with age. Women were more likely to refracture as well as less likely to die after it. Free-event time was shown to reduce the probability of death. Estimations from the illness-death and the competing risks models were identical for those common transitions although the illness-death model provided additional information from the transition from refracture to death. CONCLUSIONS: We illustrated how multi-state models, in particular illness-death models, may be especially useful when dealing with survival scenarios which include multiple events, with competing diseases or when death is an unavoidable event to consider. Illness-death models via transition probabilities provide additional information of transitions from non-terminal health conditions to absorbing states such as death, what implies a deeper understanding of the real-world problem involved compared to competing risks models.