Longitudinal Relationship Between Brain Atrophy Patterns, Cognitive Decline, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease Explored by Orthonormal Projective Non-Negative Matrix Factorization.

Background: Longitudinal magnetic resonance imaging (MRI) has been proposed for tracking the progression of Alzheimer's disease (AD) through the assessment of brain atrophy. Objective: Detection of brain atrophy patterns in patients with AD as the longitudinal disease tracker. Methods: We used a refined version of orthonormal projective non-negative matrix factorization (OPNMF) to identify six distinct spatial components of voxel-wise volume loss in the brains of 83 subjects with AD from the ADNI3 cohort relative to healthy young controls from the ABIDE study. We extracted non-negative coefficients representing subject-specific quantitative measures of regional atrophy. Coefficients of brain atrophy were compared to subjects with mild cognitive impairment and controls, to investigate the cross-sectional and longitudinal associations between AD biomarkers and regional atrophy severity in different groups. We further validated our results in an independent dataset from ADNI2. Results: The six non-overlapping atrophy components represent symmetric gray matter volume loss primarily in frontal, temporal, parietal and cerebellar regions. Atrophy in these regions was highly correlated with cognition both cross-sectionally and longitudinally, with medial temporal atrophy showing the strongest correlations. Subjects with elevated CSF levels of TAU and PTAU and lower baseline CSF Aß42 values, demonstrated a tendency toward a more rapid increase of atrophy. Conclusions: The present study has applied a transferable method to characterize the imaging changes associated with AD through six spatially distinct atrophy components and correlated these atrophy patterns with cognitive changes and CSF biomarkers cross-sectionally and longitudinally, which may help us better understand the underlying pathology of AD.

RKHS-based covariate balancing for survival causal effect estimation.

Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan-Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.

On a simple estimation of the proportional odds model under right truncation.

Retrospective sampling can be useful in epidemiological research for its convenience to explore an etiological association. One particular retrospective sampling is that disease outcomes of the time-to-event type are collected subject to right truncation, along with other covariates of interest. For regression analysis of the right-truncated time-to-event data, the so-called proportional reverse-time hazards model has been proposed, but the interpretation of its regression parameters tends to be cumbersome, which has greatly hampered its application in practice. In this paper, we instead consider the proportional odds model, an appealing alternative to the popular proportional hazards model. Under the proportional odds model, there is an embedded relationship between the reverse-time hazard function and the usual hazard function. Building on this relationship, we provide a simple procedure to estimate the regression parameters in the proportional odds model for the right truncated data. Weighted estimations are also studied.

Testing network autocorrelation without replicates.

In this paper, we propose a portmanteau test for whether a graph-structured network dataset without replicates exhibits autocorrelation across units connected by edges. Specifically, the well known Ljung-Box test for serial autocorrelation of time series data is generalized to the network setting using a specially derived central limit theorem for a weakly stationary random field. The asymptotic distribution of the test statistic under the null hypothesis of no autocorrelation is shown to be chi-squared, yielding a simple and easy-to-implement procedure for testing graph-structured autocorrelation, including spatial and spatial-temporal autocorrelation as special cases. Numerical simulations are carried out to demonstrate and confirm the derived asymptotic results. Convergence is found to occur quickly depending on the number of lags included in the test statistic, and a significant increase in statistical power is also observed relative to some recently proposed permutation tests. An example application is presented by fitting spatial autoregressive models to the distribution of COVID-19 cases across counties in New York state.

Mother's and Father's Serious Mental Illness and Risk of Child Injury in a Taiwanese Birth Cohort.

Objective: Parental serious mental illness (SMI) is associated with childhood injury. This study investigated whether child injury risk differs according to which parent is affected, SMI diagnosis, or timing of SMI onset.Methods: This cohort study included 1,999,322 singletons born in 2004-2014 identified from the national Taiwanese registries. General estimating equation Poisson models were used to estimate incidence rate ratios (IRRs) of injury events and hospitalizations before the age of 5 years among children according to which parent was affected, SMI diagnosis (schizophrenia [ICD-9-CM codes: 295, 297, 298.3, 298.4, 298.9], bipolar disorder [296.00-296.16, 296.40-296.81, 296.89-296.99, 298.1, 648.4], or major depressive disorder [MDD; 296.20-296.36, 296.82, 298.0]), and timing of diagnosis (before or after childbirth, as a proxy of timing of onset). Data analysis was performed on data obtained from April 20, 2017, to May 6, 2020.Results: Relative to unexposed children, the IRRs of injury hospitalizations for children with two SMI-affected parents, maternal SMI only, and paternal SMI only were 1.85 (95% CI, 1.38-2.48), 1.58 (95% CI, 1.48-1.68), and 1.34 (95% CI, 1.23-1.46), respectively. The IRRs of injury hospitalizations for maternal schizophrenia, bipolar disorder, and MDD were 2.09 (95% CI, 1.82-2.40), 1.77 (95% CI, 1.56-2.00), and 1.38 (95% CI, 1.26-1.50), respectively. The IRRs for paternal schizophrenia, bipolar disorder, and MDD were 1.39 (95% CI, 1.20-1.60), 1.61 (95% CI, 1.39-1.87), and 1.19 (95% CI, 1.05-1.36), respectively. The magnitude of excess risk was similar for children whose parent(s) experienced SMI diagnosed before and after childbirth.Conclusions: We found children with two SMI-affected parents or at least one parent with schizophrenia or bipolar disorder to be at greatest risk of severe injury requiring hospitalization. These parents may benefit from extra parenting support and injury prevention coaching.

Model selection among Dimension-Reduced generalized Cox models.

Conventional semiparametric hazards regression models rely on the specification of particular model formulations, such as proportional-hazards feature and single-index structures. Instead of checking these modeling assumptions one-by-one, we proposed a class of dimension-reduced generalized Cox models, and then a consistent model selection procedure among this class to select covariates with proportional-hazards feature and a proper model formulation for non-proportional-hazards covariates. In this class, the non-proportional-hazards covariates are treated in a nonparametric manner, and a partial sufficient dimension reduction is introduced to reduce the curse of dimensionality. A semiparametric efficient estimation is proposed to estimate these models. Based on the proposed estimation, we further constructed a cross-validation type criterion to consistently select the correct model among this class. Most importantly, this class of hazards regression models contains the fully nonparametric hazards regression model as the most saturated submodel, and hence no further model diagnosis is required. Overall speaking, this model selection approach is more effective than performing a sequence of conventional model checking. The proposed method is illustrated by simulation studies and a data example.

Comparing quality of primary healthcare between public and private providers in China: study protocol of a cross-sectional study using unannounced standardised patients in seven provinces of China.

INTRODUCTION: The Chinese government has encouraged the development of private sector in delivering healthcare, including primary healthcare (PHC) in the new round of national health reform since 2009. However, the debate about the role of the private sector in achieving universal health coverage continues with poor support from theories and empirical evidence. This study intends to compare the quality of PHC services between the private and public providers in seven provinces in China, using unannounced standardised patients (USPs). METHODS: We are developing and validating 13 USP cases most commonly observed in the PHC setting. Six domains of quality will be assessed by the USP: effectiveness, safety, patient centredness, efficiency, timeliness and equity. The USP will make 2200 visits to 705 public and 521 private PHC institutions across seven provinces, following a multistage clustered sample design. Using each USP-provider encounter as the analytical unit, we will first descriptively compare the raw differences in quality between the private and public providers and then analyse the association of ownership types and quality, using propensity score weighting. ETHICS AND DISSEMINATION: The study was primarily funded by the National Natural Science Foundation of China (#71974211, #71874116 and # 72074163) and was also supported by the China Medical Board (#16-260, #18-300 and #18-301), and have received ethical approval from Sun Yat-sen University (#2019-024). The validated USP tool and the data collected in this study will be freely available for the public after the primary analysis of the study. TRIAL REGISTRATION NUMBER: Chinese Clinical Trial Registry: #ChiCTR2000032773.

Maternal Gestational Weight Gain in Relation to Antidepressant Continuation in Pregnancy.

OBJECTIVE: Both excessive and inadequate gestational weight gain (GWG) are associated with adverse health outcomes for the woman and her child. Antidepressant use in pregnancy could affect GWG, based on evidence in nonpregnant women that some antidepressants may cause weight gain and others weight loss. Previous studies of antidepressant use and GWG were small with limited ability to account for confounding, including by maternal mental health status and severity. We assessed the association of antidepressant continuation in pregnancy with GWG among women using antidepressants before pregnancy. STUDY DESIGN: Our retrospective cohort study included singleton livebirths from 2001 to 2014 within Kaiser Permanente Washington, an integrated health care system. Data were obtained from electronic health records and linked Washington State birth records. Among women with ≥1 antidepressant fill within 6 months before pregnancy, women who filled an antidepressant during pregnancy were considered "continuers;" women without a fill were "discontinuers." We calculated mean differences in GWG and relative risks (RR) of inadequate and excessive weight gain based on Institute of Medicine guidelines. Using inverse probability of treatment weighting with generalized estimating equations, we addressed differences in maternal characteristics, including mental health conditions. RESULTS: Among the 2,887 births, 1,689 (59%) were to women who continued antidepressants in pregnancy and 1,198 (42%) were to discontinuers. After accounting for confounding, continuers had similar weight gain to those who discontinued (mean difference: 1.3 lbs, 95% confidence interval [CI]: -0.1 to 2.8 lbs) and similar risks of inadequate and excessive GWG (RR: 0.95, 95% CI: 0.80-1.14 and RR: 1.06, 95% CI: 0.98-1.14, respectively). Findings were comparable for specific antidepressants and trimesters of exposure. CONCLUSION: We did not find evidence that continuation of antidepressants in pregnancy led to differences in GWG. KEY POINTS: · Antidepressant use is associated with weight change in nonpregnant populations.. · Prior evidence on whether antidepressant use in pregnancy affects gestational weight gain is sparse.. · We accounted for confounding by characteristics such as mental health conditions and their severity.. · We found no association between pregnancy antidepressant continuation and gestational weight gain..

Leisure Time Physical Activity, Sedentary Time in Pregnancy, and Infant Weight at Approximately 12 Months.

Background: Maternal leisure time physical activity (LTPA) and sedentary time during pregnancy may influence programming of infant growth in a sex-specific manner. Materials and Methods: Participants (N = 35,212) from the Danish National Birth Cohort reported moderate/vigorous LTPA (hours/week) in early (conception to mean 16 weeks of gestation) and late pregnancy (mean 31 weeks of gestation to delivery) during interviews at 16 weeks of gestation and 6 months postpartum. Participants reported screen time at work and time spent watching television/videos (hours/day) in early pregnancy. Infant weight at 12 months of age was reported by mothers. Weight-for-length was categorized using sex-specific international standards. Results: Participants reported on average 1 hour per week of early pregnancy moderate/vigorous LTPA, 0.5 hour per week of late pregnancy LTPA, and 3 hours per day of early pregnancy sedentary time. Early pregnancy LTPA category was not associated with infant weight (p for trend = 0.62). There were suggested associations of early pregnancy sedentary time above the first quartile with greater odds of infant underweight (odds ratio = 1.15-1.27; p for trend = 0.27). Associations were similar in male and female infants. Conclusions: There is no clear relationship between early or late pregnancy LTPA and infant weight at 12 months in our study. Maternal early pregnancy sedentary time may be associated with infant underweight at 12 months.

Association of Parental Mental Illness With Child Injury Occurrence, Hospitalization, and Death During Early Childhood.

Importance: Injury is a leading cause of childhood morbidity and mortality worldwide. Serious mental illness (SMI) is a major contributor to the global burden of disease. Objective: To compare injury event rates in children from birth to 5 years of age among Taiwanese children with and without parents with SMI, including schizophrenia, bipolar disorder, and major depressive disorder. Design, Setting, and Participants: This population-based, retrospective cohort study of an 11-year Taiwanese birth cohort used data from the Taiwan National Health Insurance Research Database (covering 99% of Taiwanese citizens), the Maternal and Child Health Database, and birth and death certificate databases. The study included 1 999 322 singletons with Taiwanese citizenship born from January 1, 2004, to December 31, 2014, and followed up from birth to their fifth birthday, December 31, 2014, or the date of death, yielding a total of 7 741 026 person-years. Data analysis was performed from April 20, 2017, to September 24, 2019. Exposures: Physician-diagnosed parental SMI defined using outpatient and inpatient records from 6 years before the child's birth to 5 years after delivery. Main Outcome and Measures: Rates of medically attended injury events, injury hospitalization, and injury death retrieved from outpatient records, inpatient records, and death certificates. Generalized estimating equation for log-linear models estimated injury incidence rate ratios (IRRs) comparing parental SMI-exposed children and unexposed children. Results: The study cohort included 1 999 322 singletons (52.1% males without parental SMI and 52.2% males with parental SMI). Incidence rates of child injury-related outcomes were higher among children exposed to parental SMI (294.8 injury events per 1000 person-years) compared with children who were unexposed (256.1 injury events per 1000 person-years). After adjustment for sociodemographic factors, children with parental SMI had higher rates of injury events (IRR, 1.14; 95% CI, 1.13-1.15), injury hospitalization (IRR, 1.49; 95% CI, 1.42-1.57), and injury death (IRR, 1.82; 95% CI, 1.38-2.39) compared with unexposed children. The results were confirmed in sensitivity analyses. Appendicitis, a negative control outcome, was not associated with parental SMI (IRR, 1.10; 95% CI, 0.94-1.28). In addition, children with and without parental SMI had similar patterns of preventive health care. The mean (SD) number of prenatal visits was 8.09 (2.50) for children with parental SMI and 8.17 (2.47) among unaffected children. The mean (SD) number of well-child visits was 5.70 (2.24) for children with parental SMI and 5.80 (2.21) among unaffected children. Conclusions and Relevance: In this study, children with parental SMI had increased risk of injury, particularly serious injury. Excess risk may be reduced by providing effective mental health treatment, parenting support, and home safety education to parents with SMI who are raising young children.

Women's beliefs about what causes obesity: variation by race/ethnicity and acculturation in a Washington State sample.

Objective: Individuals' beliefs about the causes of multifactorial health conditions (causal attributions) shape how they conceptualize and respond to health threats and are therefore important for health promotion. Studies of racial/ethnic and cultural variation in obesity causal beliefs, however, are scarce. To address this gap, this study described beliefs about the underlying causes of obesity (genetic inheritance, diet, and physical activity) in Hispanic and non-Hispanic White women participating in a longitudinal cohort study in South King County, Washington State (n = 1,002).Design: Analysis of baseline survey data. Self-reported obesity causal beliefs were compared by race/ethnicity and acculturation indicators (survey language and nativity) using marginal effect estimates generated from multinomial logistic regression models.Results: Hispanic women had a higher probability of not believing 'at all' in inheritance and physical activity as causes of obesity - an absolute increase of 33% and 5% over non-Hispanic White women, respectively. Both acculturation indicators were also associated with a higher probability of not believing 'at all' in inheritance as a cause of obesity, though Hispanic women who completed the survey in English and were born in the United States had genetic causal beliefs similar to non-Hispanic White women. Behavioral attributions did not vary by acculturation indicators in Hispanic women.Conclusions: Differences in obesity casual beliefs, particularly genetic attributions, exist and may be important for developing and delivering effective obesity-related health promotion interventions. Identifying the determinants and public health consequences of cultural variation in obesity attributions should be the focus of future research.

Sufficient dimension reduction with simultaneous estimation of effective dimensions for time-to-event data.

When there is not enough scientific knowledge to assume a particular regression model, sufficient dimension reduction is a flexible yet parsimonious nonparametric framework to study how covariates are associated with an outcome. We propose a novel estimator of low-dimensional composite scores, which can summarize the contribution of covariates on a right-censored survival outcome. The proposed estimator determines the degree of dimension reduction adaptively from data; it estimates the structural dimension, the central subspace and a rate-optimal smoothing bandwidth parameter simultaneously from a single criterion. The methodology is formulated in a counting process framework. Further, the estimation is free of the inverse probability weighting employed in existing methods, which often leads to instability in small samples. We derive the large sample properties for the estimated central subspace with data-adaptive structural dimension and bandwidth. The estimation can be easily implemented by a forward selection algorithm, and this implementation is justified by asymptotic convexity of the criterion in working dimensions. Numerical simulations and two real examples are given to illustrate the proposed method.

Development and Validation of the Real-World Progression in Diabetes (RAPIDS) Model.

INTRODUCTION: To develop and validate the first real-world data-based type 2 diabetes progression model (RAPIDS) employing econometric techniques that can study the comparative effects of complex dynamic patterns of glucose-lowering drug use. METHODS: The US Department of Veterans Affairs (VA) electronic medical record and claims databases were used to identify over 500,000 diabetes patients in 2003 with up to 9-year follow-up. The RAPIDS model contains interdependent first-order Markov processes over quarters for each of the micro- and macrovascular events, hypoglycemia, and death, as well as predictive models for 8 biomarker levels. Model parameters varied by static demographic factors and dynamic factors, such as age, duration of diabetes, 13 possible glucose-lowering treatment combinations, any blood pressure and any cholesterol-lowering medications, and cardiovascular history. To illustrate model capabilities, a simple comparative study was set up to compare observed treatment use patterns to alternate patterns if perfect adherence is assumed following initiating the use of any of these medications. RESULTS: Data were randomly split into 307,288, 105,195, and 105,081 patients to perform estimation, out-of-sample calibration, and validation, respectively. Model predictions in the validation sample closely aligned with the observed longitudinal trajectory of biomarkers and outcomes. Perfect adherence among initiators increased proportion of days covered by only 6 months. Most of this increase came from increased adherence to monotherapies and did not lead to meaningful changes in any of the outcomes over the 9-year period. CONCLUSION: Future value of increasing medication adherence among VA patients with diabetes may lie among those who never initiate treatment or are late in initiating treatment. The first-of-its-kind real-world data-based model has the potential to carry out many complex comparative-effectiveness research (CER) studies of dynamic glucose-lowering drug regimens.

Semiparametric regression analysis of length-biased interval-censored data.

In prevalent cohort design, subjects who have experienced an initial event but not the failure event are preferentially enrolled and the observed failure times are often length-biased. Moreover, the prospective follow-up may not be continuously monitored and failure times are subject to interval censoring. We study the nonparametric maximum likelihood estimation for the proportional hazards model with length-biased interval-censored data. Direct maximization of likelihood function is intractable, thus we develop a computationally simple and stable expectation-maximization algorithm through introducing two layers of data augmentation. We establish the strong consistency, asymptotic normality and efficiency of the proposed estimator and provide an inferential procedure through profile likelihood. We assess the performance of the proposed methods through extensive simulations and apply the proposed methods to the Massachusetts Health Care Panel Study.

ESTIMATION OF A MONOTONE DENSITY IN S-SAMPLE BIASED SAMPLING MODELS.

We study the nonparametric estimation of a decreasing density function g 0 in a general s-sample biased sampling model with weight (or bias) functions wi for i = 1, …, s. The determination of the monotone maximum likelihood estimator gn and its asymptotic distribution, except for the case when s = 1, has been long missing in the literature due to certain non-standard structures of the likelihood function, such as non-separability and a lack of strictly positive second order derivatives of the negative of the log-likelihood function. The existence, uniqueness, self-characterization, consistency of gn and its asymptotic distribution at a fixed point are established in this article. To overcome the barriers caused by non-standard likelihood structures, for instance, we show the tightness of gn via a purely analytic argument instead of an intrinsic geometric one and propose an indirect approach to attain the n -rate of convergence of the linear functional ∫ wi gn.

Physical activity and epigenetic biomarkers in maternal blood during pregnancy.

AIM: Investigate associations of leisure time physical activity (LTPA) with DNA methylation and miRNAs during pregnancy. Patients & methods: LTPA, candidate DNA methylation and circulating miRNAs were measured (average 15 weeks gestation) in pregnant women (n = 92). RESULTS: Each additional hour of prepregnancy LTPA duration was associated with hypermethylation in C1orf212 (ß = 0.137, 95% CI: 0.004-0.270) and higher circulating miR-146b-5p (ß = 0.084, 95% CI: 0.017-0.151). Each additional metabolic equivalent hour of early-pregnancy LTPA energy expenditure was associated with higher circulating miR-21-3p (ß = 0.431, 95% CI: 0.089-0.772) in women carrying female offspring, and lower circulating miR-146b-5p (ß = -0.285, 95% CI: -0.528 to -0.043) and miR-517-5p (ß = -0.406, 95% CI: -0.736 to -0.076) in women carrying male offspring. CONCLUSION: Our findings suggest that LTPA may influence maternal epigenetic biomarkers, possibly in an offspring sex-specific manner.

Kernel-based covariate functional balancing for observational studies.

Covariate balance is often advocated for objective causal inference since it mimics randomization in observational data. Unlike methods that balance specific moments of covariates, our proposal attains uniform approximate balance for covariate functions in a reproducing-kernel Hilbert space. The corresponding infinite-dimensional optimization problem is shown to have a finite-dimensional representation in terms of an eigenvalue optimization problem. Large-sample results are studied, and numerical examples show that the proposed method achieves better balance with smaller sampling variability than existing methods.