Associations of Allostatic Load with Level of and Change in Cognitive Function Among Middle-Aged and Older Hispanic/Latino Adults: The Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA).

Background: Higher allostatic load (AL), a multi-system measure of physiological dysregulation considered a proxy for chronic stress exposure, is associated with poorer global cognition (GC) in older non-Hispanic white adults. However, evidence of these associations in middle-aged and older US-based Hispanic/Latino adults is limited. Objective: To examine associations of AL with level of cognition, performance in cognition 7 years later, and change in cognition over 7 years among middle-aged and older US-based Hispanic/Latino adults. Methods: We used data (n = 5,799, 45-74 years at baseline) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and SOL-Investigation of Neurocognitive Aging (SOL-INCA). The AL score comprised 16 biomarkers representing cardiometabolic, glucose, cardiopulmonary, parasympathetic, and inflammatory systems (higher scores = greater dysregulation). Cognitive outcomes included GC and individual tests of verbal learning and memory, world fluency (WF), Digit Symbol Substitution (DSS), and Trail Making (Parts A & B). Survey-linear regressions assessed associations of AL with performance in cognition at baseline, 7 years later, and via 7-year cognitive change scores adjusting for sociodemographic characteristics, lifestyle factors, and depressive symptoms. Results: Higher AL was associated with lower baseline performance in GC and WF; and lower 7-year follow-up performance in these same measures plus DSS and Trail Making Parts A & B. Higher AL was associated with more pronounced 7-year change (reduction) in GC and on WF and DSS tests. Conclusions: Findings extend previous evidence in predominantly older non-Hispanic white cohorts to show that AL is related to level of and change in GC (as well as WF and DSS) among middle-aged and older US-based Hispanic/Latino adults.

Impact of Booster Vaccination Interval on SARS-CoV-2 Infection, Hospitalization, and Death.

OBJECTIVES: We investigated how booster interval affects the risks of SARS-CoV-2 infection and Covid-19-related hospitalization and death in different age groups. METHODS: We collected data on booster receipts and Covid-19 outcomes between September 22, 2021 and February 9, 2023 for 5,769,205 North Carolina residents ≥12 years of age who had completed their primary vaccination series. We related Covid-19 outcomes to baseline characteristics and booster doses through Cox regression models. RESULTS: For adults ≥65 years of age, boosting every 9 months was associated with proportionate reductions (compared with no boosting) of 18.9% (95% CI, 18.5 to 19.4) in the cumulative frequency of infection, 37.8% (95% CI, 35.3 to 40.3) in the cumulative risk of hospitalization or death, and 40.9% (95% CI, 37.2 to 44.7) in the cumulative risk of death at two years after completion of primary vaccination. The reductions were lower by boosting every 12 months and higher by boosting every 6 months. The reductions were smaller for individuals 12-64 years of age. CONCLUSION: Boosting at a shorter interval was associated with a greater reduction in Covid-19 outcomes, especially hospitalization and death. Frequent boosting conferred greater benefits for individuals aged ≥65 than for individuals aged 12-64.

Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components.

Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.

A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders.

Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to characterize the reward-based decision-making of MDD patients. The model assumes that a subject's decision-making process is updated based on a reward prediction error weighted by the subject-specific learning rate. To account for the fact that one favors a decision leading to a potentially high reward, but this decision process is not necessarily linear, we model reward sensitivity with a non-decreasing and nonlinear function. For inference, we estimate the latter via approximation by I-splines and then maximize the joint conditional log-likelihood. We show that the resulting estimators are consistent and asymptotically normal. Through extensive simulation studies, we demonstrate that under different reward-generating distributions, the semiparametric inverse RL outperforms the parametric inverse RL. We apply the proposed method to EMBARC and find that MDD and control groups have similar learning rates but different reward sensitivity functions. There is strong statistical evidence that reward sensitivity functions have nonlinear forms. Using additional brain imaging data in the same study, we find that both reward sensitivity and learning rate are associated with brain activities in the negative affect circuitry under an emotional conflict task.

Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials.

BACKGROUND: The current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness. METHODS: We specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials. RESULTS: For remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23-1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09-1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10-1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13-1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79-1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85-1.12; p = 0.74). CONCLUSIONS: The proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.

Characterizing cognitive profiles in diverse middle-aged and older Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging (HCHS/SOL).

Introduction: We investigated cognitive profiles among diverse, middle-aged and older Hispanic/Latino adults in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) cohort using a cross-sectional observational study design. Methods: Based on weighted descriptive statistics, the average baseline age of the target population was 56.4 years, slightly more than half were women (54.6%), and 38.4% reported less than a high school education. We used latent profile analysis of demographically adjusted z scores on SOL-INCA neurocognitive tests spanning domains of verbal memory, language, processing speed, and executive function. Results: Statistical fit assessment indices combined with clinical interpretation suggested five profiles: (1) a Higher Global group performing in the average-to-high-average range across all cognitive and instrumental activity of daily living (IADL) tests (13.8%); (2) a Higher Memory group with relatively high performance on memory tests but average performance across all other cognitive/IADL tests (24.6%); (3) a Lower Memory group with relatively low performance on memory tests but average performance across all other cognitive/IADL tests (32.8%); (4) a Lower Executive Function group with relatively low performance on executive function and processing speed tests but average-to-low-average performance across all other cognitive/IADL tests (16.6%); and (5) a Lower Global group performing low-average-to-mildly impaired across all cognitive/IADL tests (12.1%). Discussion: Our results provide evidence of heterogeneity in the cognitive profiles of a representative, community-dwelling sample of diverse Hispanic/Latino adults. Our analyses yielded cognitive profiles that may assist efforts to better understand the early cognitive changes that may portend Alzheimer's disease and related dementias among diverse Hispanics/Latinos. Highlights: The present study characterized cognitive profiles among diverse middle-aged and older Hispanic/Latino adults.Latent profile analysis of neurocognitive test scores was the primary analysis conducted.The target population consists of middle-aged and older Hispanic/Latino adults enrolled in the Hispanic Community Health Study/Study of Latinos and ancillary Study of Latinos - Investigation of Neurocognitive Aging.

Glycemic Control, Cognitive Aging, and Impairment Among Diverse Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging (Hispanic Community Health Study/Study of Latinos).

OBJECTIVE: Hispanics/Latinos in the United States have the highest prevalence of undiagnosed and untreated diabetes and are at increased risk for cognitive impairment. In this study, we examine glycemic control in relation to cognitive aging and impairment in a large prospective cohort of middle-aged and older Hispanics/Latinos of diverse heritages. RESEARCH DESIGN AND METHODS: Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) is a Hispanic Community Health Study/Study of Latinos (HCHS/SOL) ancillary study. HCHS/SOL is a multisite (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA), probability sampled prospective cohort study. SOL-INCA enrolled 6,377 diverse Hispanics/Latinos age 50 years and older (2016-2018). The primary outcomes were cognitive function, 7-year cognitive decline and mild cognitive impairment (MCI). The primary glycemia exposure variables were measured from fasting blood samples collected at HCHS/SOL visit 1 (2008-2011). RESULTS: Visit 1 mean age was 56.5 years ± 8.2 SD, and the average glycosylated hemoglobin A1C (HbA1c) was 6.12% (43.5 ± 14.6 mmol/mol). After covariates adjustment, higher HbA1c was associated with accelerated 7-year global (b = -0.045; 95% CI = -0.070; -0.021; in z-score units) and executive cognitive decline, and a higher prevalence of MCI (odds ratio = 1.20; 95% CI = 1.11;1.29). CONCLUSIONS: Elevated HbA1c levels were associated with 7-year executive cognitive decline and increased MCI risk among diverse middle-aged and older Hispanics/Latinos. Our findings indicate that poor glycemic control in midlife may pose significant risks for cognitive decline and MCI later in life among Hispanics/Latinos of diverse heritages.

Hearing Loss and Associated 7-Year Cognitive Outcomes Among Hispanic and Latino Adults.

Importance: Hearing loss appears to have adverse effects on cognition and increases risk for cognitive impairment. These associations have not been thoroughly investigated in the Hispanic and Latino population, which faces hearing health disparities. Objective: To examine associations between hearing loss with 7-year cognitive change and mild cognitive impairment (MCI) prevalence among a diverse cohort of Hispanic/Latino adults. Design, Setting, and Participants: This cohort study used data from a large community health survey of Hispanic Latino adults in 4 major US cities. Eligible participants were aged 50 years or older at their second visit to study field centers. Cognitive data were collected at visit 1 and visit 2, an average of 7 years later. Data were last analyzed between September 2023 and January 2024. Exposure: Hearing loss at visit 1 was defined as a pure-tone average (500, 1000, 2000, and 4000 Hz) greater than 25 dB hearing loss in the better ear. Main outcomes and measures: Cognitive data were collected at visit 1 and visit 2, an average of 7 years later and included measures of episodic learning and memory (the Brief-Spanish English Verbal Learning Test Sum of Trials and Delayed Recall), verbal fluency (word fluency-phonemic fluency), executive functioning (Trails Making Test-Trail B), and processing speed (Digit-Symbol Substitution, Trails Making Test-Trail A). MCI at visit 2 was defined using the National Institute on Aging-Alzheimer Association criteria. Results: A total of 6113 Hispanic Latino adults were included (mean [SD] age, 56.4 [8.1] years; 3919 women [64.1%]). Hearing loss at visit 1 was associated with worse cognitive performance at 7-year follow-up (global cognition: ß = -0.11 [95% CI, -0.18 to -0.05]), equivalent to 4.6 years of aging and greater adverse change (slowing) in processing speed (ß = -0.12 [95% CI, -0.23 to -0.003]) equivalent to 5.4 years of cognitive change due to aging. There were no associations with MCI. Conclusions and relevance: The findings of this cohort study suggest that hearing loss decreases cognitive performance and increases rate of adverse change in processing speed. These findings underscore the need to prevent, assess, and treat hearing loss in the Hispanic and Latino community.

Case report: Successful treatment of a rare HER2-positive advanced breast squamous cell carcinoma.

Background: Breast squamous cell carcinoma (SCC) is an uncommon and highly aggressive variant of metaplastic breast cancer. Despite its rarity, there is currently no consensus on treatment guidelines for this specific subtype. Previous studies have demonstrated that chemotherapy alone has limited efficacy in treating breast SCC. However, the potential for targeted therapy in combination with chemotherapy holds promise for future treatment options. Case presentation: In this case report, we present a patient with advanced HER2-positive breast SCC, exhibiting a prominent breast mass, localized ulcers, and metastases in the lungs and brain. Our treatment approach involved the administration of HER2-targeted drugs in conjunction with paclitaxel, resulting in a sustained control of tumor growth. Conclusion: This case represents a rare occurrence of HER2-positive breast SCC, with limited available data on the efficacy of previous HER2-targeted drugs in treating such patients. Our study presents the first application of HER2-targeted drugs in this particular case, offering novel therapeutic insights for future considerations. Additionally, it is imperative to conduct further investigations to assess the feasibility of treatment options in a larger cohort of patients.

Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk.

Background: Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods: The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results: At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion: PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata.

Fitting the Cox proportional hazards model to big data.

The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions. We show that the final estimator has the same asymptotic distribution as the conventional maximum partial likelihood estimator using the whole dataset but requires only a small fraction of computation time. We demonstrate the usefulness of the proposed method through extensive simulation studies and an application to the UK Biobank data.

Proportional rates models for multivariate panel count data.

Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.

Safety signal detection with control of latent factors.

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

Associations of alcohol and cannabis use with change in posttraumatic stress disorder and depression symptoms over time in recently trauma-exposed individuals.

BACKGROUND: Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians. METHODS: In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance. RESULTS: Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12. CONCLUSIONS: Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.

Connections between reproductive health and cognitive aging among women enrolled in the HCHS/SOL and SOL-INCA.

INTRODUCTION: Reproductive health history may contribute to cognitive aging and risk for Alzheimer's disease, but this is understudied among Hispanic/Latina women. METHODS: Participants included 2126 Hispanic/Latina postmenopausal women (44 to 75 years) from the Study of Latinos-Investigation of Neurocognitive Aging. Survey linear regressions separately modeled the associations between reproductive health measures (age at menarche, history of oral contraceptive use, number of pregnancies, number of live births, age at menopause, female hormone use at Visit 1, and reproductive span) with cognitive outcomes at Visit 2 (performance, 7-year change, and mild cognitive impairment [MCI] prevalence). RESULTS: Younger age at menarche, oral contraceptive use, lower pregnancies, lower live births, and older age at menopause were associated with better cognitive performance. Older age at menarche was protective against cognitive change. Hormone use was linked to lower MCI prevalence. DISCUSSION: Several aspects of reproductive health appear to impact cognitive aging among Hispanic/Latina women.

Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure.

Patients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.

BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.

Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.

Marginal proportional hazards models for multivariate interval-censored data.

Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.