Wavelength-Dependent Multistate Programmability and Optoelectronic Logic-in-Memory Operation from the Narrow Bandgap pNDI-SVS Floating Gate.

This study introduces wavelength-dependent multistate programmable optoelectronic logic-in-memory (OLIM) operation using a broadband photoresponsive pNDI-SVS floating gate. The distinct optical absorption of the relatively large bandgap DNTT channel (2.6 eV) and the narrow bandgap pNDI-SVS floating gate (1.37 eV) lead to varying light-induced charge carrier accumulation across different wavelengths. In the proposed OLIM device comprising the p-type pNDI-SVS-based optoelectronic memory (POEM) transistor and an IGZO n-type transistor, we achieve controllable output voltage signals by modulating the pull-up performance through optical wavelength and applied bias manipulation. Real-time OLIM operation yields four discernible output values. The device's high mechanical flexibility and seamless surface integration among the paper substrate, pNDI-SVS, parylene gate dielectric, and DNTT region render it compatible for integration into paper-based optoelectronics. Our flexible POEM device on name card substrates demonstrates stable operational performance, with minimal variation (8%) after 100 cycles of repeated memory operation, remaining reliable across various angle measurements.

Electronic and Transport Engineering of A-Type Antiferromagnets with Ferroelectric Sandwich Structure: Toward Multistate Nonvolatile Memory Applications.

Achieving higher-order multistates with mutual interstate switching at the nanoscale is essential for high-density storage devices; yet, it remains a significant challenge. Here, we demonstrate that integrating A-type antiferromagnetic semiconductors sandwiched between ferroelectric layers is an effective strategy to achieve high-performance multistate data storage. Taking the Sc2CO2/VSi2P4 bilayer (bi-VSi2P4)/Sc2CO2 van der Waals multiferroic heterostructure as an example, our first-principles calculations show that by switching the polarization direction of the upper and bottom ferroelectric Sc2CO2 layers, antiferromagnetic bi-VSi2P4 can exhibit four distinct states with different band structures. The intriguing band structure engineering stems from the polarization-field-induced band shift and interface charge transfer. Accordingly, the proposed Sc2CO2/bi-VSi2P4/Sc2CO2-based multiferroic device can achieve four different resistance states, accompanied by fully spin-polarized currents and giant tunneling electroresistance ratios. Our results propose a viable strategy for realizing nonvolatile electrical control of antiferromagnets at the nanoscale and provide insights into the development of advanced memories.

Nonvolatile Multistate Manipulation of Topological Magnetism in Monolayer CrI3 through Quadruple-Well Ferroelectric Materials.

Nonvolatile multistate manipulation of two-dimensional (2D) magnetic materials holds promise for low dissipation, highly integrated, and versatile spintronic devices. Here, utilizing density functional theory calculations and Monte Carlo simulations, we report the realization of nonvolatile and multistate control of topological magnetism in monolayer CrI3 by constructing multiferroic heterojunctions with quadruple-well ferroelectric (FE) materials. The Pt2Sn2Te6/CrI3 heterojunction exhibits multiple magnetic phases upon modulating FE polarization states of FE layers and interlayer sliding. These magnetic phases include Bloch-type skyrmions and ferromagnetism, as well as a newly discovered topological magnetic structure. We reveal that the Dzyaloshinskii-Moriya interaction (DMI) induced by interfacial coupling plays a crucial role in magnetic skyrmion manipulation, which aligns with the Fert-Levy mechanism. Moreover, a regular magnetic skyrmion lattice survives when removing a magnetic field, demonstrating its robustness. The work sheds light on an effective approach to nonvolatile and multistate control of 2D magnetic materials.

Extensively Drug-Resistant Pseudomonas aeruginosa Outbreak Associated With Artificial Tears.

BACKGROUND: Carbapenemase-producing, carbapenem-resistant Pseudomonas aeruginosa (CP-CRPA) are extensively drug-resistant bacteria. We investigated the source of a multistate CP-CRPA outbreak. METHODS: Cases were defined as a US patient's first isolation of P. aeruginosa sequence type 1203 with carbapenemase gene blaVIM-80 and cephalosporinase gene blaGES-9 from any specimen source collected and reported to the Centers for Disease Control and Prevention during 1 January 2022-15 May 2023. We conducted a 1:1 matched case-control study at the post-acute care facility with the most cases, assessed exposures associated with case status for all case-patients, and tested products for bacterial contamination. RESULTS: We identified 81 case-patients from 18 states, 27 of whom were identified through surveillance cultures. Four (7%) of 54 case-patients with clinical cultures died within 30 days of culture collection, and 4 (22%) of 18 with eye infections underwent enucleation. In the case-control study, case-patients had increased odds of receiving artificial tears versus controls (crude matched OR, 5.0; 95% CI, 1.1-22.8). Overall, artificial tears use was reported by 61 (87%) of 70 case-patients with information; 43 (77%) of 56 case-patients with brand information reported use of Brand A, an imported, preservative-free, over-the-counter (OTC) product. Bacteria isolated from opened and unopened bottles of Brand A were genetically related to patient isolates. Food and Drug Administration inspection of the manufacturing plant identified likely sources of contamination. CONCLUSIONS: A manufactured medical product serving as the vehicle for carbapenemase-producing organisms is unprecedented in the United States. The clinical impacts from this outbreak underscore the need for improved requirements for US OTC product importers.

Longitudinal Transitions Between Use of Combustible, Noncombustible, and Multiple Cannabis Products From Adolescence to Young Adulthood and Intersections With Nicotine Use.

Understanding transitions across use of different types of cannabis products and multiple cannabis products and how they intersect with nicotine use in young people can inform etiology and prevention. In this study, we examined transitions across use of combustible and noncombustible forms of cannabis and multiple types of cannabis from adolescence to young adulthood and the role of nicotine use in transitions. In a Southern California longitudinal cohort study (n = 3,298; baseline mean age = 16.1 (standard deviation, 0.4) years) with 9 semiannual survey waves (2015-2021), we used Markov multistate transition modeling to estimate short-term (2-wave) and long-term (9-wave) probabilities of transition across 5 cannabis use states: never use of any product, prior use with no past-6-month (P6M) use of any product, and P6M use of exclusively noncombustible products, exclusively combustible products, and multiple (noncombustible + combustible) products. Sizable transition probabilities from prior and exclusive P6M noncombustible or combustible cannabis use to P6M poly-cannabis-product use were observed in short-term (10.7%-38.9%) and long-term (43.4%-43.8%) analyses. P6M nicotine use increased risk of transitioning from never and prior use to exclusive P6M noncombustible and combustible cannabis use. Cannabis use in any form, even temporary use, during midadolescence may often be followed by poly-cannabis-product use. Nicotine use may amplify the probability of future cannabis use onset or recurrence.

Fitting a progressive three-state colorectal cancer model to interval-censored surveillance data under outcome-dependent sampling using a weighted likelihood approach.

To optimize colorectal cancer (CRC) surveillance, accurate information on the risk of developing CRC from premalignant lesions is essential. However, directly observing this risk is challenging since precursor lesions, i.e., advanced adenomas (AAs), are removed upon detection. Statistical methods for multistate models can estimate risks, but estimation is challenging due to low CRC incidence. We propose an outcome-dependent sampling (ODS) design for this problem in which we oversample CRCs. More specifically, we propose a three-state model for jointly estimating the time distributions from baseline colonoscopy to AA and from AA onset to CRC accounting for the ODS design using a weighted likelihood approach. We applied the methodology to a sample from a Norwegian adenoma cohort (1993-2007), comprising 1, 495 individuals (median follow-up 6.8 years [IQR: 1.1 - 12.8 years]) of whom 648 did and 847 did not develop CRC. We observed a 5-year AA risk of 13% and 34% for individuals having non-advanced adenoma (NAA) and AA removed at baseline colonoscopy, respectively. Upon AA development, the subsequent risk to develop CRC in 5 years was 17% and age-dependent. These estimates provide a basis for optimizing surveillance intensity and determining the optimal trade-off between CRC prevention, costs, and use of colonoscopy resources.

Estimation of age of onset and progression of breast cancer by absolute risk dependent on polygenic risk score and other risk factors.

BACKGROUND: Genetic, lifestyle, reproductive, and anthropometric factors are associated with the risk of developing breast cancer. However, it is not yet known whether polygenic risk score (PRS) and absolute risk based on a combination of risk factors are associated with the risk of progression of breast cancer. This study aims to estimate the distribution of sojourn time (pre-clinical screen-detectable period) and mammographic sensitivity by absolute breast cancer risk derived from polygenic profile and the other risk factors. METHODS: The authors used data from a population-based case-control study. Six categories of 10-year absolute risk based on different combinations of risk factors were derived using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm. Women were classified into low, medium, and high-risk groups. The authors constructed a continuous-time multistate model. To calculate the sojourn time, they simulated the trajectories of subjects through the disease states. RESULTS: There was little difference in sojourn time with a large overlap in the 95% confidence interval (CI) between the risk groups across the six risk categories and PRS studied. However, the age of entry into the screen-detectable state varied by risk category, with the mean age of entry of 53.4 years (95% CI, 52.2-54.1) and 57.0 years (95% CI, 55.1-57.7) in the high-risk and low-risk women, respectively. CONCLUSION: In risk-stratified breast screening, the age at the start of screening, but not necessarily the frequency of screening, should be tailored to a woman's risk level. The optimal risk-stratified screening strategy that would improve the benefit-to-harm balance and the cost-effectiveness of the screening programs needs to be studied.

Racial disparities across multiple stages of the deceased organ donation process.

Pervasive structural violence causes higher organ failure rates among Black Americans and excess Black potential deceased organ donors. Underuse of Black donors would exacerbate organ shortages that disproportionately harm Black transplant candidates. This study investigates racial differences in transit between distinct donation steps among 132 968 potential donors across 557 hospitals and 6 Organ Procurement Organizations (OPOs) from 2015 through 2021. Multilevel multistate modeling with patient covariates and OPO random effects shows adjusted likelihoods (95% confidence interval [CI]) of non-Black versus Black patients transitioning from OPO referral to approach: odds ratio (OR) 1.23 (95% CI 1.18, 1.27), approach to authorization: OR 1.64 (95% CI 1.56, 1.72), authorization to procurement: OR 1.08 (95% CI 1.02, 1.14), and procurement to transplant: OR 0.99 (95% CI 0.93, 1.04). Overall organ utilization rates for Black, Latino, White, and other OPO referrals were 5.88%, 8.17%, 6.78%, and 5.24%, respectively. Adjusting for patient covariates and hospital and OPO random effects, multilevel logistic models estimated that compared with Black patients, Latino, White, and other patients had ORs of organ utilization of 1.82 (95% CI 1.61, 2.04), 3.19 (95% CI 2.91, 3.50), and 1.25 (95% CI 1.06, 1.47), respectively. Nationwide in 2022, donor conversion disparities likely lost more than 1800 donors-70% of whom would have been Black. Achieving racial equity for transplant candidates will require reducing racial disparities in organ donation.

A scalable approach for continuous time Markov models with covariates.

Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set.

Association of lifestyle with valvular heart disease progression and life expectancy among elderly people from different socioeconomic backgrounds.

BACKGROUND: Current cardiovascular prevention strategies are based on studies that seldom include valvular heart disease (VHD). The role of modifiable lifestyle factors on VHD progression and life expectancy among the elderly with different socioeconomic statuses (SES) remains unknown. METHODS: This cohort study included 164,775 UK Biobank participants aged 60 years and older. Lifestyle was determined using a five-factor scoring system covering smoking status, obesity, physical activity, diet, and sleep patterns. Based on this score, participants were then classified into "poor," "moderate," or "ideal" lifestyle groups. SES was classified as high or low based on the Townsend Deprivation Index. The association of lifestyle with major VHD progression was evaluated using a multistate mode. The life table method was employed to determine life expectancy with VHD and without VHD. RESULTS: The UK Biobank documented 5132 incident VHD cases with a mean follow-up of 12.3 years and 1418 deaths following VHD with a mean follow-up of 6.0 years. Compared to those with a poor lifestyle, women and men followed an ideal lifestyle had lower hazard ratios for incident VHD (0.66 with 95% CI, 0.59-0.73 for women and 0.77 with 95% CI, 0.71-0.83 for men) and for post-VHD mortality (0.58 for women, 95% CI 0.46-0.74 and 0.62 for men, 95% CI 0.54-0.73). When lifestyle and SES were combined, the lower risk of incident VHD and mortality were observed among participants with an ideal lifestyle and high SES compared to participants with an unhealthy lifestyle and low SES. There was no significant interaction between lifestyle and SES in their correlation with the incidence and subsequent mortality of VHD. Among low SES populations, 60-year-old women and men with VHD who followed ideal lifestyles lived 4.2 years (95% CI, 3.8-4.7) and 5.1 years (95% CI, 4.5-5.6) longer, respectively, compared to those with poor lifestyles. In contrast, the life expectancy gain for those without VHD was 4.4 years (95% CI, 4.0-4.8) for women and 5.3 years (95% CI, 4.8-5.7) for men when adhering to an ideal lifestyle versus a poor one. CONCLUSIONS: Adopting a healthier lifestyle can significantly slow down the progression from free of VHD to incident VHD and further to death and increase life expectancy for both individuals with and without VHD within diverse socioeconomic elderly populations.

Refracture and mortality risk in the elderly with osteoporotic fractures: the AGES-Reykjavik study.

There is imminent refracture risk in elderly individuals for up to six years, with a decline thereafter except in women below 75 who face a constant elevated risk. Elderly men with fractures face the highest mortality risk, particularly those with hip and vertebral fractures. Targeted monitoring and treatment strategies are recommended. PURPOSE: Current management and interventions for osteoporotic fractures typically focus on bone mineral density loss, resulting in suboptimal evaluation of fracture risk. The aim of the study is to understand the progression of fractures to refractures and mortality in the elderly using multi-state models to better target those at risk. METHODS: This prospective, observational study analysed data from the AGES-Reykjavik cohort of Icelandic elderly, using multi-state models to analyse the evolution of fractures into refractures and mortality, and to estimate the probability of future events in subjects based on prognostic factors. RESULTS: At baseline, 4778 older individuals aged 65 years and older were included. Elderly men, and elderly women above 80 years of age, had a distinct imminent refracture risk that lasted between 2-6 years, followed by a sharp decline. However, elderly women below 75 continued to maintain a nearly constant refracture risk profile for ten years. Hip (30-63%) and vertebral (24-55%) fractures carried the highest 5-year mortality burden for elderly men and women, regardless of age, and for elderly men over 80, lower leg fractures also posed a significant mortality risk. CONCLUSION: The risk of refracture significantly increases in the first six years following the initial fracture. Elderly women, who experience fractures at a younger age, should be closely monitored to address their long-term elevated refracture risk. Elderly men, especially those with hip and vertebral fractures, face substantial mortality risk and require prioritized monitoring and treatment.

Fracture risk assessment in the presence of competing risk of death.

PURPOSE: To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. METHODS: We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. RESULTS: During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. CONCLUSION: Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

Calibration plots for multistate risk predictions models.

INTRODUCTION: There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of random and independent censoring through a simulation. METHODS: We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. RESULTS: The pseudo-value, BLR-IPCW, and MLR-IPCW approaches give unbiased estimates of the calibration curves under random censoring. These methods remained predominately unbiased in the presence of independent censoring, even if the censoring mechanism was strongly associated with the outcome, with bias concentrated in low-density regions of predicted transition probability. CONCLUSIONS: We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot. The methods have been incorporated into the "calibmsm" R package available on CRAN.

Modeling the multi-state natural history of rare diseases with heterogeneous individual patient data: A simulation study.

Multi-state survival models are used to represent the natural history of a disease, forming the basis of a health technology assessment comparing a novel treatment to current practice. Constructing such models for rare diseases is problematic, since evidence sources are typically much sparser and more heterogeneous. This simulation study investigated different one-stage and two-stage approaches to meta-analyzing individual patient data (IPD) in a multi-state survival setting when the number and size of studies being meta-analyzed are small. The objective was to assess methods of different complexity to see when they are accurate, when they are inaccurate and when they struggle to converge due to the sparsity of data. Biologically plausible multi-state IPD were simulated from study- and transition-specific hazard functions. One-stage frailty and two-stage stratified models were estimated, and compared to a base case model that did not account for study heterogeneity. Convergence and the bias/coverage of population-level transition probabilities to, and lengths of stay in, each state were used to assess model performance. A real-world application to Duchenne Muscular Dystrophy, a neuromuscular rare disease, was conducted, and a software demonstration is provided. Models not accounting for study heterogeneity were consistently out-performed by two-stage models. Frailty models struggled to converge, particularly in scenarios of low heterogeneity, and predictions from models that did converge were also subject to bias. Stratified models may be better suited to meta-analyzing disparate sources of IPD in rare disease natural history/economic modeling, as they converge more consistently and produce less biased predictions of lengths of stay.

A discrete approximation method for modeling interval-censored multistate data.

Many longitudinal studies are designed to monitor participants for major events related to the progression of diseases. Data arising from such longitudinal studies are usually subject to interval censoring since the events are only known to occur between two monitoring visits. In this work, we propose a new method to handle interval-censored multistate data within a proportional hazards model framework where the hazard rate of events is modeled by a nonparametric function of time and the covariates affect the hazard rate proportionally. The main idea of this method is to simplify the likelihood functions of a discrete-time multistate model through an approximation and the application of data augmentation techniques, where the assumed presence of censored information facilitates a simpler parameterization. Then the expectation-maximization algorithm is used to estimate the parameters in the model. The performance of the proposed method is evaluated by numerical studies. Finally, the method is employed to analyze a dataset on tracking the advancement of coronary allograft vasculopathy following heart transplantation.

Multiple imputation strategies for missing event times in a multi-state model analysis.

In clinical studies, multi-state model (MSM) analysis is often used to describe the sequence of events that patients experience, enabling better understanding of disease progression. A complicating factor in many MSM studies is that the exact event times may not be known. Motivated by a real dataset of patients who received stem cell transplants, we considered the setting in which some event times were exactly observed and some were missing. In our setting, there was little information about the time intervals in which the missing event times occurred and missingness depended on the event type, given the analysis model covariates. These additional challenges limited the usefulness of some missing data methods (maximum likelihood, complete case analysis, and inverse probability weighting). We show that multiple imputation (MI) of event times can perform well in this setting. MI is a flexible method that can be used with any complete data analysis model. Through an extensive simulation study, we show that MI by predictive mean matching (PMM), in which sampling is from a set of observed times without reliance on a specific parametric distribution, has little bias when event times are missing at random, conditional on the observed data. Applying PMM separately for each sub-group of patients with a different pathway through the MSM tends to further reduce bias and improve precision. We recommend MI using PMM methods when performing MSM analysis with Markov models and partially observed event times.

An augmented illness-death model for semi-competing risks with clinically immediate terminal events.

Preeclampsia is a pregnancy-associated condition posing risks of both fetal and maternal mortality and morbidity that can only resolve following delivery and removal of the placenta. Because in its typical form preeclampsia can arise before delivery, but not after, these two events exemplify the time-to-event setting of "semi-competing risks" in which a non-terminal event of interest is subject to the occurrence of a terminal event of interest. The semi-competing risks framework presents a valuable opportunity to simultaneously address two clinically meaningful risk modeling tasks: (i) characterizing risk of developing preeclampsia, and (ii) characterizing time to delivery after onset of preeclampsia. However, some people with preeclampsia deliver immediately upon diagnosis, while others are admitted and monitored for an extended period before giving birth, resulting in two distinct trajectories following the non-terminal event, which we call "clinically immediate" and "non-immediate" terminal events. Though such phenomena arise in many clinical contexts, to-date there have not been methods developed to acknowledge the complex dependencies between such outcomes, nor leverage these phenomena to gain new insight into individualized risk. We address this gap by proposing a novel augmented frailty-based illness-death model with a binary submodel to distinguish risk of immediate terminal event following the non-terminal event. The model admits direct dependence of the terminal event on the non-terminal event through flexible regression specification, as well as indirect dependence via a shared frailty term linking each submodel. We develop an efficient Bayesian sampler for estimation and corresponding model fit metrics, and derive formulae for dynamic risk prediction. In an extended example using pregnancy outcome data from an electronic health record, we demonstrate the proposed model's direct applicability to address a broad range of clinical questions.

Dealing with time-dependent exposures and confounding when defining and estimating attributable fractions-Revisiting estimands and estimators.

The population-attributable fraction (PAF) is commonly interpreted as the proportion of events that can be ascribed to a certain exposure in a certain population. Its estimation is sensitive to common forms of time-dependent bias in the face of a time-dependent exposure. Predominant estimation approaches based on multistate modeling fail to fully eliminate such bias and, as a result, do not permit a causal interpretation, even in the absence of confounding. While recently proposed multistate modeling approaches can successfully eliminate residual time-dependent bias, and moreover succeed to adjust for time-dependent confounding by means of inverse probability of censoring weighting, inadequate application, and misinterpretation prevails in the medical literature. In this paper, we therefore revisit recent work on previously proposed PAF estimands and estimators in settings with time-dependent exposures and competing events and extend this work in several ways. First, we critically revisit the interpretation and applied terminology of these estimands. Second, we further formalize the assumptions under which a causally interpretable PAF estimand can be identified and provide analogous weighting-based representations of the identifying functionals of other proposed estimands. This representation aims to enhance the applied statistician's understanding of different sources of bias that may arise when the aim is to obtain a valid estimate of a causally interpretable PAF. To illustrate and compare these representations, we present a real-life application to observational data from the Ghent University Hospital ICUs to estimate the fraction of ICU deaths attributable to hospital-acquired infections.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications.

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

Multifaceted density dependence: Social structure and seasonality effects on Serengeti lion demography.

Interactions between density and environmental conditions have important effects on vital rates and consequently on population dynamics and can take complex pathways in species whose demography is strongly influenced by social context, such as the African lion, Panthera leo. In populations of such species, the response of vital rates to density can vary depending on the social structure (e.g. effects of group size or composition). However, studies assessing density dependence in populations of lions and other social species have seldom considered the effects of multiple socially explicit measures of density, and-more particularly for lions-of nomadic males. Additionally, vital-rate responses to interactions between the environment and various measures of density remain largely uninvestigated. To fill these knowledge gaps, we aimed to understand how a socially and spatially explicit consideration of density (i.e. at the local scale) and its interaction with environmental seasonality affect vital rates of lions in the Serengeti National Park, Tanzania. We used a Bayesian multistate capture-recapture model and Bayesian generalized linear mixed models to estimate lion stage-specific survival and between-stage transition rates, as well as reproduction probability and recruitment, while testing for season-specific effects of density measures at the group and home-range levels. We found evidence for several such effects. For example, resident-male survival increased more strongly with coalition size in the dry season compared with the wet season, and adult-female abundance affected subadult survival negatively in the wet season, but positively in the dry season. Additionally, while our models showed no effect of nomadic males on adult-female survival, they revealed strong effects of nomads on key processes such as reproduction and takeover dynamics. Therefore, our results highlight the importance of accounting for seasonality and social context when assessing the effects of density on vital rates of Serengeti lions and of social species more generally.