Analyzing risk factors for post-acute recovery in older adults with Alzheimer's disease and related dementia: A new semi-parametric model for large-scale medicare claims.

Nearly 300,000 older adults experience a hip fracture every year, the majority of which occur following a fall. Unfortunately, recovery after fall-related trauma such as hip fracture is poor, where older adults diagnosed with Alzheimer's disease and related dementia (ADRD) spend a particularly long time in hospitals or rehabilitation facilities during the post-operative recuperation period. Because older adults value functional recovery and spending time at home versus facilities as key outcomes after hospitalization, identifying factors that influence days spent at home after hospitalization is imperative. While several individual-level factors have been identified, the characteristics of the treating hospital have recently been identified as contributors. However, few methodological rigorous approaches are available to help overcome potential sources of bias such as hospital-level unmeasured confounders, informative hospital size, and loss to follow-up due to death. This article develops a useful tool equipped with unsupervised learning to simultaneously handle statistical complexities that are often encountered in health services research, especially when using large administrative claims databases. The proposed estimator has a closed form, thus only requiring light computation load in a large-scale study. We further develop its asymptotic properties with stabilized inference assisted by unsupervised clustering. Extensive simulation studies demonstrate superiority of the proposed estimator compared to existing estimators.

Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study.

Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.

A statistical framework for recovering pseudo-dynamic networks from static data.

MOTIVATION: The collection of temporal or perturbed data is often a prerequisite for reconstructing dynamic networks in most cases. However, these types of data are seldom available for genomic studies in medicine, thus significantly limiting the use of dynamic networks to characterize the biological principles underlying human health and diseases. RESULTS: We proposed a statistical framework to recover disease risk-associated pseudo-dynamic networks (DRDNet) from steady-state data. We incorporated a varying coefficient model with multiple ordinary differential equations to learn a series of networks. We analyzed the publicly available Genotype-Tissue Expression data to construct networks associated with hypertension risk, and biological findings showed that key genes constituting these networks had pivotal and biologically relevant roles associated with the vascular system. We also provided the selection consistency of the proposed learning procedure and evaluated its utility through extensive simulations. AVAILABILITY AND IMPLEMENTATION: DRDNet is implemented in the R language, and the source codes are available at https://github.com/chencxxy28/DRDnet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Omnibus and robust deconvolution scheme for bulk RNA sequencing data integrating multiple single-cell reference sets and prior biological knowledge.

MOTIVATION: Cell-type deconvolution of bulk tissue RNA sequencing (RNA-seq) data is an important step toward understanding the variations in cell-type composition among disease conditions. Owing to recent advances in single-cell RNA sequencing (scRNA-seq) and the availability of large amounts of bulk RNA-seq data in disease-relevant tissues, various deconvolution methods have been developed. However, the performance of existing methods heavily relies on the quality of information provided by external data sources, such as the selection of scRNA-seq data as a reference and prior biological information. RESULTS: We present the Integrated and Robust Deconvolution (InteRD) algorithm to infer cell-type proportions from target bulk RNA-seq data. Owing to the innovative use of penalized regression with a new evaluation criterion for deconvolution, InteRD has three primary advantages. First, it is able to effectively integrate deconvolution results from multiple scRNA-seq datasets. Second, InteRD calibrates estimates from reference-based deconvolution by taking into account extra biological information as priors. Third, the proposed algorithm is robust to inaccurate external information imposed in the deconvolution system. Extensive numerical evaluations and real-data applications demonstrate that InteRD yields more accurate and robust cell-type proportion estimates that agree well with known biology. AVAILABILITY AND IMPLEMENTATION: The proposed InteRD framework is implemented in R and the package is available at https://cran.r-project.org/web/packages/InteRD/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Fn14-Directed DART Nanoparticles Selectively Target Neoplastic Cells in Preclinical Models of Triple-Negative Breast Cancer Brain Metastasis.

Triple-negative breast cancer (TNBC) patients with brain metastasis (BM) face dismal prognosis due to the limited therapeutic efficacy of the currently available treatment options. We previously demonstrated that paclitaxel-loaded PLGA-PEG nanoparticles (NPs) directed to the Fn14 receptor, termed "DARTs", are more efficacious than Abraxane─an FDA-approved paclitaxel nanoformulation─following intravenous delivery in a mouse model of TNBC BM. However, the precise basis for this difference was not investigated. Here, we further examine the utility of the DART drug delivery platform in complementary xenograft and syngeneic TNBC BM models. First, we demonstrated that, in comparison to nontargeted NPs, DART NPs exhibit preferential association with Fn14-positive human and murine TNBC cell lines cultured in vitro. We next identified tumor cells as the predominant source of Fn14 expression in the TNBC BM-immune microenvironment with minimal expression by microglia, infiltrating macrophages, monocytes, or lymphocytes. We then show that despite similar accumulation in brains harboring TNBC tumors, Fn14-targeted DARTs exhibit significant and specific association with Fn14-positive TNBC cells compared to nontargeted NPs or Abraxane. Together, these results indicate that Fn14 expression primarily by tumor cells in TNBC BMs enables selective DART NP delivery to these cells, likely driving the significantly improved therapeutic efficacy observed in our prior work.

An efficient data integration scheme for synthesizing information from multiple secondary datasets for the parameter inference of the main analysis.

Many observational studies and clinical trials collect various secondary outcomes that may be highly correlated with the primary endpoint. These secondary outcomes are often analyzed in secondary analyses separately from the main data analysis. However, these secondary outcomes can be used to improve the estimation precision in the main analysis. We propose a method called multiple information borrowing (MinBo) that borrows information from secondary data (containing secondary outcomes and covariates) to improve the efficiency of the main analysis. The proposed method is robust against model misspecification of the secondary data. Both theoretical and case studies demonstrate that MinBo outperforms existing methods in terms of efficiency gain. We apply MinBo to data from the Atherosclerosis Risk in Communities study to assess risk factors for hypertension.

Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data.

Multimodal neuroimaging data have attracted increasing attention for brain research. An integrated analysis of multimodal neuroimaging data and behavioral or clinical measurements provides a promising approach for comprehensively and systematically investigating the underlying neural mechanisms of different phenotypes. However, such an integrated data analysis is intrinsically challenging due to the complex interactive relationships between the multimodal multivariate imaging variables. To address this challenge, a novel multivariate-mediator and multivariate-outcome mediation model (MMO) is proposed to simultaneously extract the latent systematic mediation patterns and estimate the mediation effects based on a dense bi-cluster graph approach. A computationally efficient algorithm is developed for dense bicluster structure estimation and inference to identify the mediation patterns with multiple testing correction. The performance of the proposed method is evaluated by an extensive simulation analysis with comparison to the existing methods. The results show that MMO performs better in terms of both the false discovery rate and sensitivity compared to existing models. The MMO is applied to a multimodal imaging dataset from the Human Connectome Project to investigate the effect of systolic blood pressure on whole-brain imaging measures for the regional homogeneity of the blood oxygenation level-dependent signal through the cerebral blood flow.

Synthesizing secondary data into survival analysis to improve estimation efficiency.

The accelerated failure time (AFT) model and Cox proportional hazards (PH) model are broadly used for survival endpoints of primary interest. However, the estimation efficiency from those models can be further enhanced by incorporating the information from secondary outcomes that are increasingly available and highly correlated with primary outcomes. Those secondary outcomes could be longitudinal laboratory measures collected from doctor visits or cross-sectional disease-relevant variables, which are believed to contain extra information related to primary survival endpoints to a certain extent. In this paper, we develop a two-stage estimation framework to combine a survival model with a secondary model that contains secondary outcomes, named as the empirical-likelihood-based weighting (ELW), which comprises two weighting schemes accommodated to the AFT model (ELW-AFT) and the Cox PH model (ELW-Cox), respectively. This innovative framework is flexibly adaptive to secondary outcomes with complex data features, and it leads to more efficient parameter estimation in the survival model even if the secondary model is misspecified. Extensive simulation studies showcase more efficiency gain from ELW compared to conventional approaches, and an application in the Atherosclerosis Risk in Communities study also demonstrates the superiority of ELW by successfully detecting risk factors at the time of hospitalization for acute myocardial infarction.

Improving main analysis by borrowing information from auxiliary data.

In many clinical and observational studies, auxiliary data from the same subjects, such as repeated measurements or surrogate variables, will be collected in addition to the data of main interest. Not directly related to the main study, these auxiliary data in practice are rarely incorporated into the main analysis, though they may carry extra information that can help improve the estimation in the main analysis. Under the setting where part of or all subjects have auxiliary data available, we propose an effective weighting approach to borrow the auxiliary information by building a working model for the auxiliary data, where improvement of estimation precision over the main analysis is guaranteed regardless of the specification of the working model. An information index is also constructed to assess how well the selected working model works to improve the main analysis. Both theoretical and numerical studies show the excellent and robust performance of the proposed method in comparison to estimation without using the auxiliary data. Finally, we utilize the Atherosclerosis Risk in Communities study for illustration.

Modeling multivariate age-related imaging variables with dependencies.

Neuroimaging techniques have been increasingly used to understand the neural biology of aging brains. The neuroimaging variables from distinct brain locations and modalities can exhibit age-related patterns that reflect localized neural decline. However, it is a challenge to identify the impacts of risk factors (eg, mental disorders) on multivariate imaging variables while simultaneously accounting for the dependence structure and nonlinear age trajectories using existing tools. We propose a mixed-effects model to address this challenge by building random effects based on the latent brain aging status. We develop computationally efficient algorithms to estimate the parameters of new random effects. The simulations show that our approach provides accurate parameter estimates, improves the inference efficiency, and reduces the root mean square error compared to existing methods. We further apply this method to the UK Biobank data to investigate the effects of tobacco smoking on the white matter integrity of the entire brain during aging and identify the adverse effects on white matter integrity with multiple fiber tracts.

Semiparametric marginal methods for clustered data adjusting for informative cluster size with nonignorable zeros.

Clustered or longitudinal data are commonly encountered in clinical trials and observational studies. This type of data could be collected through a real-time monitoring scheme associated with some specific event, such as disease recurrence, hospitalization, or emergency room visit. In these contexts, the cluster size could be informative because of its potential correlation with disease status, since more frequency of observations may indicate a worsening health condition. However, for some clusters/subjects, there are no measures or relevant medical records. Under such circumstances, these clusters/subjects may have a considerably lower risk of an event occurrence or may not be susceptible to such events at all, indicating a nonignorable zero cluster size. There is a substantial body of literature using observations from those clusters with a nonzero informative cluster size only, but few works discuss informative nonignorable zero-sized clusters. To utilize the information from both event-free and event-occurring participants, we propose a weighted within-cluster-resampling (WWCR) method and its asymptotically equivalent method, dual-weighted generalized estimating equations (WWGEE) by adopting the inverse probability weighting technique. The asymptotic properties are rigorously presented theoretically. Extensive simulations and an illustrative example of the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study are performed to analyze the finite-sample behavior of our methods and to show their advantageous performance compared to the existing approaches.

A multiple robust propensity score method for longitudinal analysis with intermittent missing data.

Longitudinal data are very popular in practice, but they are often missing in either outcomes or time-dependent risk factors, making them highly unbalanced and complex. Missing data may contain various missing patterns or mechanisms, and how to properly handle it for unbiased and valid inference still presents a significant challenge. Here, we propose a novel semiparametric framework for analyzing longitudinal data with both missing responses and covariates that are missing at random and intermittent, a general and widely encountered situation in observational studies. Within this framework, we consider multiple robust estimation procedures based on innovative calibrated propensity scores, which offers additional relaxation of the misspecification of missing data mechanisms and shows more satisfactory numerical performance. Also, the corresponding robust information criterion on consistent variable selection for our proposed model is developed based on empirical likelihood-based methods. These advocated methods are evaluated in both theory and extensive simulation studies in a variety of situations, showing competing properties and advantages compared to the existing approaches. We illustrate the utility of our approach by analyzing the data from the HIV Epidemiology Research Study.

Joint modeling of longitudinal data with informative cluster size adjusted for zero-inflation and a dependent terminal event.

Repeated measures are often collected in longitudinal follow-up from clinical trials and observational studies. In many situations, these measures are adherent to some specific event and are only available when it occurs; an example is serum creatinine from laboratory tests for hospitalized acute kidney injuries. The frequency of event recurrences is potentially correlated with overall health condition and hence may influence the distribution of the outcome measure of interest, leading to informative cluster size. In particular, there may be a large portion of subjects without any events, thus no longitudinal measures are available, which may be due to insusceptibility to such events or censoring before any events, and this zero-inflation nature of the data needs to be taken into account. On the other hand, there often exists a terminal event that may be correlated with the recurrent events. Previous work in this area suffered from the limitation that not all these issues were handled simultaneously. To address this deficiency, we propose a novel joint modeling approach for longitudinal data adjusting for zero-inflated and informative cluster size as well as a terminal event. A three-stage semiparametric likelihood-based approach is applied for parameter estimation and inference. Extensive simulations are conducted to evaluate the performance of our proposal. Finally, we utilize the Assessment, Serial Evaluation, and Subsequent Sequelae of Acute Kidney Injury (ASSESS-AKI) study for illustration.

np2 QTL: networking phenotypic plasticity quantitative trait loci across heterogeneous environments.

Despite its critical importance to our understanding of plant growth and adaptation, the question of how environment-induced plastic response is affected genetically remains elusive. Previous studies have shown that the reaction norm of an organism across environmental index obeys the allometrical scaling law of part-whole relationships. The implementation of this phenomenon into functional mapping can characterize how quantitative trait loci (QTLs) modulate the phenotypic plasticity of complex traits to heterogeneous environments. Here, we assemble functional mapping and allometry theory through Lokta-Volterra ordinary differential equations (LVODE) into an R-based computing platform, np2 QTL, aimed to map and visualize phenotypic plasticity QTLs. Based on LVODE parameters, np2 QTL constructs a bidirectional, signed and weighted network of QTL-QTL epistasis, whose emergent properties reflect the ecological mechanisms for genotype-environment interactions over any range of environmental change. The utility of np2 QTL was validated by comprehending the genetic architecture of phenotypic plasticity via the reanalysis of published plant height data involving 3502 recombinant inbred lines of maize planted in multiple discrete environments. np2 QTL also provides a tool for constructing a predictive model of phenotypic responses in extreme environments relative to the median environment.

A unified DNA sequence and non-DNA sequence mapping model of complex traits.

Increasing evidence shows that quantitative inheritance is based on both DNA sequence and non-DNA sequence variants. However, how to simultaneously detect these variants from a mapping study has been unexplored, hampering our effort to illustrate the detailed genetic architecture of complex traits. We address this issue by developing a unified model of quantitative trait locus (QTL) mapping based on an open-pollinated design composed of randomly sampling maternal plants from a natural population and their half-sib seeds. This design forms a two-level hierarchical platform for a joint linkage-linkage disequilibrium analysis of population structure. The EM algorithm was implemented to estimate and test DNA sequence-based effects and non-DNA sequence-based effects of QTLs. We applied this model to analyze genetic mapping data from the OP design of a gymnosperm coniferous species, Torreya grandis, identifying 25 significant DNA sequence and non-DNA sequence QTLs for seedling height and diameter growth in different years. Results from computer simulation show that the unified model has good statistical properties and is powerful for QTL detection. Our model enables the tests of how a complex trait is affected differently by DNA-based effects and non-DNA sequence-based transgenerational effects, thus allowing a more comprehensive picture of genetic architecture to be charted and quantified.

Assessing predictive accuracy of survival regressions subject to nonindependent censoring.

Survival regression is commonly applied in biomedical studies or clinical trials, and evaluating their predictive performance plays an essential role for model diagnosis and selection. The presence of censored data, particularly if informative, may pose more challenges for the assessment of predictive accuracy. Existing literature mainly focuses on prediction for survival probabilities with limitation work for survival time. In this work, we focus on accuracy measures of predicted survival times adjusted for a potentially informative censoring mechanism (ie, coarsening at random (CAR); non-CAR) by adopting the technique of inverse probability of censoring weighting. Our proposed predictive metric can be adaptive to various survival regression frameworks including but not limited to accelerated failure time models and proportional hazards models. Moreover, we provide the asymptotic properties of the inverse probability of censoring weighting estimators under CAR. We consider the settings of high-dimensional data under CAR or non-CAR for extensions. The performance of the proposed method is evaluated through extensive simulation studies and analysis of real data from the Critical Assessment of Microarray Data Analysis.

Empirical-likelihood-based criteria for model selection on marginal analysis of longitudinal data with dropout missingness.

Longitudinal data are common in clinical trials and observational studies, where missing outcomes due to dropouts are always encountered. Under such context with the assumption of missing at random, the weighted generalized estimating equation (WGEE) approach is widely adopted for marginal analysis. Model selection on marginal mean regression is a crucial aspect of data analysis, and identifying an appropriate correlation structure for model fitting may also be of interest and importance. However, the existing information criteria for model selection in WGEE have limitations, such as separate criteria for the selection of marginal mean and correlation structures, unsatisfactory selection performance in small-sample setups, and so forth. In particular, there are few studies to develop joint information criteria for selection of both marginal mean and correlation structures. In this work, by embedding empirical likelihood into the WGEE framework, we propose two innovative information criteria named a joint empirical Akaike information criterion and a joint empirical Bayesian information criterion, which can simultaneously select the variables for marginal mean regression and also correlation structure. Through extensive simulation studies, these empirical-likelihood-based criteria exhibit robustness, flexibility, and outperformance compared to the other criteria including the weighted quasi-likelihood under the independence model criterion, the missing longitudinal information criterion, and the joint longitudinal information criterion. In addition, we provide a theoretical justification of our proposed criteria, and present two real data examples in practice for further illustration.

Trajectories of Recovery Following Traumatic Brain Injury Among Older Medicare Beneficiaries.

It is well-known that older adults have poorer recovery following traumatic brain injury (TBI) relative to younger adults with similar injury severity. However, most older adults do recover well from TBI. Identifying those at increased risk of poor recovery could inform appropriate management pathways, facilitate discussions about palliative care or unmet needs, and permit targeted intervention to optimize quality of life or recovery. We sought to explore heterogeneity in recovery from TBI among older adults as measured by home time per month, a patient-centered metric defined as time spent at home and not in a hospital, urgent care, or other facility. Using data obtained from Medicare administrative claims data for years 2010-2018, group-based trajectory modeling was employed to identify unique trajectories of recovery among a sample of United States adults age 65 and older who were hospitalized with TBI. We next determined which patient-level characteristics discriminated poor from favorable recovery using logistic regression. Among 20,350 beneficiaries, four unique trajectories were identified: poor recovery (n = 1929; 9.5%), improving recovery (n = 2,793; 13.7%), good recovery (n = 13,512; 66.4%), and declining recovery (n = 2116; 10.4%). The strongest predictors of membership in the poor relative to the good recovery trajectory group were diagnosis of Alzheimer's disease and related dementias (ADRD; odd ratio [OR] 2.42; 95% confidence interval [CI] 2.16, 2.72) and dual eligibility for Medicaid, a proxy for economic vulnerability (OR 5.13; 95% CI 4.59, 5.74). TBI severity was not associated with recovery trajectories. In conclusion, this study identified four unique trajectories of recovery over one year following TBI among older adults. Two-thirds of older adults hospitalized with TBI returned to the community and stayed there. Recovery of monthly home time was complete for most by 3 months post injury. An important sub-group comprising 10% of patients who did not return home was characterized primarily by eligibility for Medicaid and diagnosis of ADRD. Future studies should seek to further characterize and investigate identified recovery groups to inform management and development of interventions to improve recovery.

Robust integration of secondary outcomes information into primary outcome analysis in the presence of missing data.

In clinical and observational studies, secondary outcomes are frequently collected alongside the primary outcome for each subject, yet their potential to improve the analysis efficiency remains underutilized. Moreover, missing data, commonly encountered in practice, can introduce bias to estimates if not appropriately addressed. This article presents an innovative approach that enhances the empirical likelihood-based information borrowing method by integrating missing-data techniques, ensuring robust data integration. We introduce a plug-in inverse probability weighting estimator to handle missingness in the primary analysis, demonstrating its equivalence to the standard joint estimator under mild conditions. To address potential bias from missing secondary outcomes, we propose a uniform mapping strategy, imputing incomplete secondary outcomes into a unified space. Extensive simulations highlight the effectiveness of our method, showing consistent, efficient, and robust estimators under various scenarios involving missing data and/or misspecified secondary models. Finally, we apply our proposal to the Uniform Data Set from the National Alzheimer's Coordinating Center, exemplifying its practical application.

Association between pre-hip fracture depression and days at home after fracture and assessing sex differences.

BACKGROUND: Hip fracture and depression are important public health issues among older adults, but how pre-fracture depression impacts recovery after hip fracture is unknown, especially among males who often experience greater depression severity. Days at home (DAH), or the days spent outside a hospital or healthcare facility, is a novel, patient-centered outcome that can capture meaningful aspects of fracture recovery. How pre-fracture depression impacts DAH after fracture, and related sex differences, remains unclear. METHODS: Participants included 63,618 Medicare fee-for-service beneficiaries aged 65+ years, with a hospitalization claim for hip fracture surgery between 2010 and 2017. The primary exposure was a diagnosis of depression at hospital admission, and the primary outcome was total DAH over 12 months post-discharge. Longitudinal associations between pre-fracture depression and the count of DAH among beneficiaries were estimated using Poisson regression models after adjustment for covariates; sex-by-depression interactions were also assessed. Incidence rate ratios (IRRs) and 95% confidence intervals (CIs) reflecting relative differences were estimated from these models. RESULTS: Overall, beneficiaries with depression were younger, White females, and spent 11 fewer average DAH compared to counterparts without depression when demographic factors (age and sex) (IRR = 0.91; 95% CI = 0.90, 0.92; p < 0.0001) and social determinants of health (race, Medicaid dual eligibility, and poverty) were adjusted for (IRR = 0.92; 95% CI = 0.91, 0.93; p < 0.0001), but this association attenuated after adjusting for medical complexities (IRR = 0.99; 95% CI = 0.98, 1.01; p = 0.41) and facility and geographical factors (IRR = 1.0037; 95% CI = 0.99, 1.02; p = 0.66). There was no evidence of effect modification by sex. CONCLUSIONS: The comorbidity burden of preexisting depression may impact DAH among both male and female Medicare beneficiaries with hip fracture. Results suggest a holistic health approach and secondary prevention of depressive symptoms after hip fracture.