Estimating effects of time-varying exposures on mortality risk.

Administrative databases have become an increasingly popular data source for population-based health research. We explore how mortality risk is associated with some health service utilization process via linked administrative data. A generalized Cox regression model is proposed using a time-dependent stratification variable to summarize lifetime service utilization. Recognizing the service utilization over time as an internal covariate in the survival analysis, conventional likelihood methods are inapplicable. We present an estimating function based procedure for estimating model parameters, and provide a testing procedure for updating the stratification levels. The proposed approach is examined both asymptotically and numerically via simulation. We motivate and illustrate the proposed approach using an on-going program pertaining to opioid agonist treatment (OAT) management for individuals identified with opioid use disorders. Our analysis of the OAT data indicates that the OAT effect on mortality risk decreases in successive OAT attempts, in which two risk classes based on an individual's treatment episode number are established: one with 1-3 OAT episodes, and the other with 4+ OAT episodes.

A log-binomial Bayesian geoadditive semiparametric analysis of geographical inequalities in caesarean births in Ghana.

BACKGROUND: Caesarean section is a clinical intervention aimed to save the lives of women and their newborns. In Ghana, studies have reported inequalities in use among women of different socioeconomic backgrounds. However, geographical differentials at the district level where health interventions are implemented, have not been systematically studied. This study examined geographical inequalities in caesarean births at the district level in Ghana. The study investigated how pregnancy complications and birth risks, access to health care and affluence correlate with geographical inequalities in caesarean section uptake. METHODS: The data for the analysis was derived from the 2017 Ghana Maternal Health Survey. The log-binomial Bayesian Geoadditive Semiparametric regression technique was used to examine the extent of geographical clustering in caesarean births at the district level and their spatial correlates. RESULTS: In Ghana, 16.0% (95% CI = 15.3, 16.8) of births were via caesarean section. Geospatial analysis revealed a strong spatial dependence in caesarean births, with a clear north-south divide. Low frequencies of caesarean births were observed among districts in the northern part of the country, while those in the south had high frequencies. The predominant factor associated with the spatial differentials was affluence rather than pregnancy complications and birth risk and access to care. CONCLUSIONS: Strong geographical inequalities in caesarean births exist in Ghana. Targeted and locally relevant interventions including health education and policy support are required at the district level to address the overuse and underuse of caesarean sections, to correspond to the World Health Organisation recommended optimal threshold of 10% to 15%.

Association of dietary mineral mixture with depressive symptoms: A combination of Bayesian approaches.

The relationships between mixtures of multiple minerals and depression have not been explored. Therefore, we analyzed the relationship between the mixture of nine dietary minerals [calcium (Ca), phosphorus, magnesium (Mg), iron (Fe), zinc, copper (Cu), sodium, potassium (K), and selenium (Se)] and depressive symptoms in the general population. We screened 20,342 participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. We fitted the general linear regression, Bayesian kernel machine regression (BKMR), and Bayesian semiparametric regression models to explore associations and interactions. We obtained the relative importance of dietary minerals by calculating posterior inclusion probabilities (PIPs). The dietary intakes of minerals were obtained using the 24-h dietary recall interview, and depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9). The linear analysis showed that nine minerals were negatively associated with PHQ-9 scores. The BKMR analysis showed a negative association between the dietary mineral mixture and PHQ-9 scores, with Se having the largest PIP at 1.0000, followed by K (0.7784). We also observed potential interactions between Ca and Fe, Se and Fe, and K and Mg. Among them, the interaction of Ca and Fe had the largest PIP of 0.986. In addition, the overall effect was more pronounced in females than males, and Cu's PIP (0.8376) was higher in females. Two sensitivity analyses showed that our results were robust. Our study provides a basis for formulating nutritional intervention programs for depression in the future.

Comparative efficacy of three Bayesian variable selection methods in the context of weight loss in obese women.

The use of high-dimensional data has expanded in many fields, including in clinical research, thus making variable selection methods increasingly important compared to traditional statistical approaches. The work aims to compare the performance of three supervised Bayesian variable selection methods to detect the most important predictors among a high-dimensional set of variables and to provide useful and practical guidelines of their use. We assessed the variable selection ability of: (1) Bayesian Kernel Machine Regression (BKMR), (2) Bayesian Semiparametric Regression (BSR), and (3) Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) regression on simulated data of different dimensions and under three scenarios with disparate predictor-response relationships and correlations among predictors. This is the first study describing when one model should be preferred over the others and when methods achieve comparable results. BKMR outperformed all other models with small synthetic datasets. BSR was strongly dependent on the choice of its own intrinsic parameter, but its performance was comparable to BKMR with large datasets. BLASSO should be preferred only when it is reasonable to hypothesise the absence of synergies between predictors and the presence of monotonous predictor-outcome relationships. Finally, we applied the models to a real case study and assessed the relationships among anthropometric, biochemical, metabolic, cardiovascular, and inflammatory variables with weight loss in 755 hospitalised obese women from the Follow Up OBese patients at AUXOlogico institute (FUOBAUXO) cohort.

[Expected education deficiencies from Covid-related school-lockdowns in spring 2020-empirical evidence on family-education resources using nonlinear regression]. / Erwartete Schulprobleme als Folge der Corona-Schulschließungen im Frühjahr 2020 ­ Empirische Evidenz zur Bedeutung familialer Ressourcen mittels nichtlinearer Modellierung.

In light of the Covid-19-related school lockdowns in Germany in 2020 schools, families and the students were faced with the major challenge to continue instruction at home. This paper examines the expectations of the parents that their children will experience school-related problems as a result to the lockdown-induced homeschooling within the next six months. For our explorative analysis, we choose a nonlinear regression approach. In the course of this, we introduce nonlinear models and highlight their added value compared to methods commonly used in empirical educational research. For the analysis we combine data from the National Educational Panel Study (NEPS) with additional data sources like the COVID-19-Dashboard of the Robert-Koch-Institut (RKI). Our results show that parental expectations of future school problems were particularly prevalent among those parents whose children had low reading competencies and low diligence as an aspect of school effort. In addition, we find a relationship between a lower occupational status (ISEI) and higher parental expectations of school-related problems. Furthermore, parents' short-term and long-term concerns about Covid-19 show a positive association, making school problems more likely in the eyes of the parents. The purpose of this paper, in addition to applying and explaining nonlinear models for the first time in empirical educational research, is to analyze expectations regarding problems of homeschooling in the first lockdown from a parents' perspective and to explore variables that influence these parental expectations.

Does climate change affect the transmission of COVID-19? A Bayesian regression analysis.

Aim: Coronavirus is an airborne and infectious disease and it is crucial to check the impact of climatic risk factors on the transmission of COVID-19. The main objective of this study is to determine the effect of climate risk factors using Bayesian regression analysis. Methods: Coronavirus disease 2019, due to the effect of the SARS-CoV-2 virus, has become a serious global public health issue. This disease was identified in Bangladesh on March 8, 2020, though it was initially identified in Wuhan, China. This disease is rapidly transmitted in Bangladesh due to the high population density and complex health policy setting. To meet our goal, The MCMC with Gibbs sampling is used to draw Bayesian inference, which is implemented in WinBUGS software. Results: The study revealed that high temperatures reduce confirmed cases and deaths from COVID-19, but low temperatures increase confirmed cases and deaths. High temperatures have decreased the proliferation of COVID-19, reducing the virus's survival and transmission. Conclusions: Considering only the existing scientific evidence, warm and wet climates seem to reduce the spread of COVID-19. However, more climate variables could account for explaining most of the variability in infectious disease transmission.

Boosting multivariate structured additive distributional regression models.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

Marginal proportional hazards models for clustered interval-censored data with time-dependent covariates.

The Botswana Combination Prevention Project was a cluster-randomized HIV prevention trial whose follow-up period coincided with Botswana's national adoption of a universal test and treat strategy for HIV management. Of interest is whether, and to what extent, this change in policy modified the preventative effects of the study intervention. To address such questions, we adopt a stratified proportional hazards model for clustered interval-censored data with time-dependent covariates and develop a composite expectation maximization algorithm that facilitates estimation of model parameters without placing parametric assumptions on either the baseline hazard functions or the within-cluster dependence structure. We show that the resulting estimators for the regression parameters are consistent and asymptotically normal. We also propose and provide theoretical justification for the use of the profile composite likelihood function to construct a robust sandwich estimator for the variance. We characterize the finite-sample performance and robustness of these estimators through extensive simulation studies. Finally, we conclude by applying this stratified proportional hazards model to a re-analysis of the Botswana Combination Prevention Project, with the national adoption of a universal test and treat strategy now modeled as a time-dependent covariate.

Diagnostic evaluation of pharmacokinetic features of functional markers.

The dynamicity of functional (curve) markers from modern clinical studies offers deeper insights into complex disease physiology. A frequent clinical practice is to examine various 'pharmacokinetic features' of functional markers (definite integral, maximum value, time to maximum, etc.) that reflect important physiological underpinnings. For instance, the current diagnostic procedure for kidney obstruction is to examine several pharmacokinetic features of renogram curves characterizing renal function. Motivated by such clinical practices, we develop a statistical framework for evaluating diagnostic accuracy of pharmacokinetic features using area under the receiver operating characteristic curve (AUC). The major challenge is that functional markers are observed at discrete time points with measurement error. To address this challenge, we develop a two-stage non-parametric AUC estimator based on summary functionals providing unified representation of various pharmacokinetic features and study its asymptotic properties. We also propose a sensible adaptation of a semiparametric regression model that can describe heterogeneity of AUC across different subpopulations, while appropriately handling discreteness and noise in observed functional markers. Here, a novel data-driven approach that balances between bias and efficiency of the regression coefficient estimates is introduced. Finally, the framework is applied to rigorously evaluate pharmacokinetic features of renogram curves potentially useful for detecting kidney obstruction.

Penalized splines model to estimate time-varying reproduction number for Covid-19 in India: A Bayesian semi-parametric approach.

Statistical modelling is pivotal in assessing intensity of a stochastic processes. Novel Corona virus disease demanded proactive measures to understand the severity of disease spread and to plan its control accordingly. We propose estimation of reproduction number as a crucial factor to monitor the random dynamics of Covid-19 in India. In the present paper, semi-parametric regression based on penalized splines embedded under Bayesian formulation is utilised to estimate reproduction number while incorporating effects of underreporting and delay in reporting for the actual number of daily occurrences. Monte Carlo Markov Chain approximations are utilised to perform simulation study and thereby to assess the impact of the reporting probability and misspecification of delay pattern on potential for further substance of the pandemic. For a cycle of reporting on weekly basis, the proposed penalized spline Bayesian framework fits closest to the empirical data drawn for a two-day delay in reporting with approximately half of the actual cases being reported. The present paper is a contribution towards estimation of the true daily reproduction number of Covid-19 incidences in its next generation cycle.

Effects of climate variables on the COVID-19 mortality in Bangladesh.

Infectious diseases such as severe acute respiratory syndrome (SARS) and influenza are influenced by weather conditions. Climate variables, for example, temperature and humidity, are two important factors in the severity of COVID-19's impact on the human respiratory system. This study aims to examine the effects of these climate variables on COVID-19 mortality. The data are collected from March 08, 2020, to April 30, 2022. The parametric regression under GAM and semiparametric regression under GAMLSS frameworks are used to analyze the daily number of death due to COVID-19. Our findings revealed that temperature and relative humidity are commencing to daily deaths due to COVID-19. A positive association with COVID-19 daily death counts was observed for temperature range and a positive association for humidity. In addition, one-unit increase in daily temperature range was only associated with a 1.08% (95% CI: 1.06%, 1.10%), and humidity range was only associated with a 1.03% (95% CI: 1.02%, 1.03%) decrease in COVID-19 deaths. A flexible regression model within the framework of Generalized Additive Models for Location Scale and Shape is used to analyze the data by adjusting the time effect. We used two adaptable predictor models, such as (i) the Fractional polynomial model and (ii) the B-spline smoothing model, to estimate the systematic component of the GAMLSS model. According to both models, high humidity and temperature significantly (and drastically) lessened the severity of COVID-19 death. The findings on the epidemiological trends of the COVID-19 pandemic and weather changes may interest policymakers and health officials.

Early life environments and cognition in adulthood: New evidence using a semiparametric approach and quantile regression.

Theories and empirical evidence document the importance of early life environmental factors on later life cognition. A next question is how and in what dimension associations between early life environments and later life cognition vary. Using data from the UK Biobank in conjunction with time-place-specific infant mortality rates, we assessed heterogeneous and non-linear associations between early life conditions and later life cognition. We found that the association between the infant mortality rate and later life cognition increased once the UK achieved very low infant mortality rates, suggesting that additional decreases in infant mortality rates in an industrialized society continue to improve later life cognition. We also found that infant mortality rates have stronger effects at upper quantiles of the cognition distribution. This implies that adverse early life environments may have an important role for an early manifestation of cognitive aging.

Selective inference for effect modification via the lasso.

Effect modification occurs when the effect of the treatment on an outcome varies according to the level of other covariates and often has important implications in decision-making. When there are tens or hundreds of covariates, it becomes necessary to use the observed data to select a simpler model for effect modification and then make valid statistical inference. We propose a two-stage procedure to solve this problem. First, we use Robinson's transformation to decouple the nuisance parameters from the treatment effect of interest and use machine learning algorithms to estimate the nuisance parameters. Next, after plugging in the estimates of the nuisance parameters, we use the lasso to choose a low-complexity model for effect modification. Compared to a full model consisting of all the covariates, the selected model is much more interpretable. Compared to the univariate subgroup analyses, the selected model greatly reduces the number of false discoveries. We show that the conditional selective inference for the selected model is asymptotically valid given the rate assumptions in classical semiparametric regression. Extensive simulation studies are conducted to verify the asymptotic results and an epidemiological application is used to demonstrate the method.

Adaptive monitoring of coral health at Scott Reef where data exhibit nonlinear and disturbed trends over time.

Time series data are often observed in ecological monitoring. Frequently, such data exhibit nonlinear trends over time potentially due to complex relationships between observed and auxiliary variables, and there may also be sudden declines over time due to major disturbances. This poses substantial challenges for modeling such data and also for adaptive monitoring. To address this, we propose methods for finding adaptive designs for monitoring in such settings. This work is motivated by a monitoring program that has been established at Scott Reef; a coral reef off the Western coast of Australia. Data collected for monitoring the health of Scott Reef are considered, and semiparametric and interrupted time series modeling approaches are adopted to describe how these data vary over time. New methods are then proposed that enable adaptive monitoring designs to be found based on such modeling approaches. These methods are then applied to find future monitoring designs at Scott Reef where it was found that future information gain is expected to be similar across a variety of different sites, suggesting that no particular location needs to be prioritized at Scott Reef for the next monitoring phase. In addition, it was found that omitting some sampling sites/reef locations was possible without substantial loss in expected information gain, depending upon the disturbances that were observed. The resulting adaptive designs are used to form recommendations for future monitoring in this region, and for reefs where changes in the current monitoring practices are being sought. As the methods used and developed throughout this study are generic in nature, this research has the potential to improve ecological monitoring more broadly where complex data are being collected over time.

Bayesian analysis for partly linear Cox model with measurement error and time-varying covariate effect.

The Cox proportional hazards model is commonly used to estimate the association between time-to-event and covariates. Under the proportional hazards assumption, covariate effects are assumed to be constant in the follow-up period of study. When measurement error presents, common estimation methods that adjust for an error-contaminated covariate in the Cox proportional hazards model assume that the true function on the covariate is parametric and specified. We consider a semiparametric partly linear Cox model that allows the hazard to depend on an unspecified function of an error-contaminated covariate and an error-free covariate with time-varying effect, which simultaneously relaxes the assumption on the functional form of the error-contaminated covariate and allows for nonconstant effect of the error-free covariate. We take a Bayesian approach and approximate the unspecified function by a B-spline. Simulation studies are conducted to assess the finite sample performance of the proposed approach. The results demonstrate that our proposed method has favorable statistical performance. The proposed method is also illustrated by an application to data from the AIDS Clinical Trials Group Protocol 175.

Constrained Nonlinear and Mixed Effects Integral Differential Equation Models for Dynamic Cell Polarity Signaling.

Polar cell growth is a process that couples the establishment of cell polarity with growth and is extremely important in the growth, development, and reproduction of eukaryotic organisms, such as pollen tube growth during plant fertilization and neuronal axon growth in animals. Pollen tube growth requires dynamic but polarized distribution and activation of a signaling protein named ROP1 to the plasma membrane via three processes: positive feedback and negative feedback regulation of ROP1 activation and its lateral diffusion along the plasma membrane. In this paper, we introduce a mechanistic integro-differential equation (IDE) along with constrained semiparametric regression to quantitatively describe the interplay among these three processes that lead to the polar distribution of active ROP1 at a steady state. Moreover, we introduce a population variability by a constrained nonlinear mixed model. Our analysis of ROP1 activity distributions from multiple pollen tubes revealed that the equilibrium between the positive and negative feedbacks for pollen tubes with similar shapes are remarkably stable, permitting us to infer an inherent quantitative relationship between the positive and negative feedback loops that defines the tip growth of pollen tubes and the polarity of tip growth.

Geographical hotspots and correlates of early sexual debut among women in Ghana.

OBJECTIVES: Generalisation of sexual behaviour, including early sexual initiation, does not provide comprehensive knowledge of young people's sexual attitudes, behaviours and challenges, given the high sociocultural diversity and economic inequalities within countries. This study examines geographical hotspots of early sexual initiation, at the district level in Ghana and the factors associated with the observed spatial patterns. METHODS: Data was derived from the 2017 Ghana Maternal Health Survey, covering 21,392 women aged 15-49 years. Early sexual debut denotes first sexual intercourse before attaining the legal age of sexual consent, which in Ghana, is 16 years. The Bayesian geoadditive semiparametric regression technique was used to examine geographical hotspots and correlates of the observed spatial patterns, classified into demographic, socioeconomic and pregnancy outcome factors. RESULTS: The results show that 26.7% (95% CI = 26.1-27.3) of women had their first sexual intercourse before attaining the age of 16 years. Hotspots of early sexual debut was observed predominantly among districts along the mainstream of the Volta Lake, which are also reported hotspots of child trafficking, labour and slavery. Demographic, socioeconomic and pregnancy related factors were identified to be correlated with the observed spatial clustering. CONCLUSION: Policies and interventions such as sexual and reproductive health education should target at-risk population, simultaneously addressing other child abuses perpetuating the practice.

Ghana operates a decentralised health system, where health policies and interventions, including those for sexual and reproductive health are implemented at the district level. Yet, there are no studies that systematically identify districts where sexual behaviours, such as early sexual debut, require attention. This study uses spatial models and data from the 2017 Ghana Maternal Health Survey to identify areas (districts) with high concentration of women who initiated sex before the legal age of consent. Early sexual debut refers to first sexual intercourse before attainment of the legal age (16 years) of sexual consent. Early sexual initiation has been associated with adverse sexual and reproductive health outcomes such as unwanted pregnancies and STIs. The results show that about one in four women reported having early sexual intercourse. High early sexual intercourse was observed to be particularly concentrated among districts along the mainstream of the Volta Lake. With regards to the spatial correlates, for the districts in the Oti region, high early sexual debut was associated with low educational attainment and inability to read. For those in the Bono East and Eastern regions, women who had early sexual debut were more likely to have had a miscarriage, abortion or stillbirth. Younger women, those in co-habiting relationships and those not in union were more likely to have had early sexual debut in the districts in the Ashanti, Central and Northern regions. The findings call for intensification of sexual and reproductive health education in districts along the mainstream of the Volta Lake.

Variable selection in semiparametric regression models for longitudinal data with informative observation times.

A common issue in longitudinal studies is that subjects' visits are irregular and may depend on observed outcome values which is known as longitudinal data with informative observation times (follow-up). Semiparametric regression modeling for this type of data has received much attention as it provides more flexibility in studying the association between regression factors and a longitudinal outcome. An important problem here is how to select relevant variables and estimate their coefficients in semiparametric regression models when the number of covariates at baseline is large. The current penalization procedures in semiparametric regression models for longitudinal data do not account for informative observation times. We propose a variable selection procedure that is suitable for the estimation methods based on pseudo-score functions. We investigate the asymptotic properties of penalized estimators and conduct simulation studies to illustrate the theoretical results. We also use the procedure for variable selection in semiparametric regression models for the STAR*D dataset from a multistage randomized clinical trial for treating major depressive disorder.

Semiparametric multiple inflation count model with application to a smoking cessation study.

Count data are observed by practitioners across various fields. Often, a substantially large proportion of one or some values causes extra variation and may lead to a particular case of mixed structured data. In these cases, a standard count model may lead to poor inference of the parameters involved because of its inability to account for extra variation. Furthermore, we hypothesize a possible nonlinear relationship of a continuous covariate with the logarithm of the mean count and with the probability of belonging to an inflated category. We propose a semiparametric multiple inflation Poisson (MIP) model that considers the two nonlinear link functions. We develop a sieve maximum likelihood estimator (sMLE) for the regression parameters of interest. We establish the asymptotic behavior of the sMLE. Simulations are conducted to evaluate the performance of the proposed sieve MIP (sMIP). Then, we illustrate the methodology on data from a smoking cessation study. Finally, some remarks and opportunities for future research conclude the article.

The Combined Effect of Environmental Policies on China's Renewable Energy Development: A Multi-Perspective Study Based on Semiparametric Regression Model.

Based on a sample of 92 listed renewable energy enterprises in China from 2007-2017, this paper empirically examines the nonlinear effect of environmental policies on renewable energy investments using a semiparametric regression model. Environmental policies are divided into three groups in terms of pre-control, in-process governance, and post-accounting-the groups being green supervision and public regulations, green standardized regulations, and green accounting regulations-and this paper explores the differences in the effects of environmental policies at different stages. The results indicate that the relationship between environmental policies and renewable energy development has been unstable, following a "W-shaped" pattern. Green supervision and public regulations can greatly enhance investments in the renewable energy industry, with an estimated coefficient of 10.8173. Green standardized regulations have a similar "W-shaped" impact on renewable energy development. However, the nonlinear impact of green accounting regulations on renewable energy development fails the significance test. In addition, the effect of environmental policies on investment in the solar energy industry is positive, with a coefficient of 1.0697. The positive effect of environmental policies on investments in the renewable energy industry is reflected mainly in medium-, small-, and micro-sized enterprises. These findings contribute to the literature on the effectiveness of environmental policies by putting a set of environmental policies into a unified framework to explore their combined effects.