Time-varying Effects Research Articles

Overlooked carbon sinks in the house building industry and the influential factors

The study aims to establish a carbon sink measurement model for the house building industry and analyze the time-varying characteristics of influential factors. China's house building industry exhibited an increasing trend in carbon sink, from 0.336 billion tons in 2000 to 234.1 million tons in 2020. This study defines the carbon sink coefficient of the house building industry to understand its carbon sink capacity. From 2000 to 2020, the average carbon dioxide absorption per square meter of floor space annually, throughout its life cycle, is 2.09 kg. Factors influencing the carbon sink were analyzed using Logarithmic Mean Divisia Index (LMDI) model. The most influential factor was resource consumption intensity, followed by value dependence level and output scale effect. Employing Time-Varying Parameter Vector Autoregression (TVP-VAR), this study examines the impact of macro factors on carbon sinks at various lead times and specific points. Resource consumption intensity exhibits significant time-varying effects. Value dependence level negatively influences, primarily in the medium and long terms, while the output scale effect consistently shows a positive influence, mainly in the long term. Scenario forecasting suggests that by 2035, under low-carbon transition scenarios, carbon sinks could increase by 8.28% and 17.26% compared to baseline and high-carbon crude scenarios, respectively. To foster this growth, prolonging the service life of houses, enhancing construction site management and waste treatment, and employing higher value-added production factors are recommended.

Caregiving Appraisals and Emotional Valence: Moderating Effects of Activity Participation.

The present study examines the extent to which a two-factor model of affect explains how caregiving appraisals experienced by caregivers influence their own well-being. We used data from three waves of Nation Study of Caregiving (NSOC) to conduct latent growth curve models with the time-varying predictors to investigate the effect of between-person (BP) and within-person (WP) caregiving appraisals on positive and negative affect. Furthermore, we simultaneously modeled WP differences in activity participation and affective experience with multilevel modeling. Then, we tested the moderating effect of activity participation in the association between WP caregiving appraisals and emotional valence. We found that BP and WP caregiving negative appraisal also contribute to caregiver positive affect similar to that of negative affect. Time-varying effects of negative appraisals and emotional valence are consistent with the two-factor model. Future longitudinal investigations could target WP and BP activity participation to alleviate caregiving cognitive appraisal among caregivers.

Time varying characteristics of factors affecting carbon price in emission trading scheme in China: Evidence from SV-TVP-VAR approach

Analysis of the factors influencing the price of carbon emissions trading in China and its time-varying characteristics is essential for the smooth operation of the carbon trading system. We analyse the time-varying effects of public concern, degree of carbon regulation, crude oil price, international carbon price and interest rate level on China’s carbon price through SV-TVP-VAR model. Among them, the quantification of public concern and the degree of carbon emission regulation is based on microblog text and government decisions. The results show that all the factors influencing carbon price are significantly time-varying, with the shocks of each factor on carbon price rising before 2019 and turning significantly thereafter. The short-term shock effect of each factor is more significant compared to the medium- and long-term, and the effect almost disappears at a lag of six months. Thanks to public environmental awareness, low-carbon awareness and the progress of carbon market management mechanisms, public concern has had the most significant impact on carbon price since 2019. With the promulgation of relevant management measures for the carbon market, relevant regulations on carbon emission accounting, financing constraints, and carbon emission quota allocation for emission-controlled enterprises have become increasingly mature, and carbon price signals are more sensitive to market information. The above findings provide substantial empirical evidence for all stakeholders in the market, who need to recognize that the impact of non-structural factors on the price of carbon varies over time. Government intervention also serves as a key aspect of carbon emission control and requires the introduction of relevant constraints and incentives. In particular, emission-controlling firms need to focus on the policy direction of the carbon market, and focus on the impact of Internet public opinion on business production while reducing carbon allowance demand and energy dependence.

Age-varying associations of parent-adolescent relationship and school connectedness with adolescent self-compassion: Differences by gender.

Parent-adolescent relationships and school connectedness are critical promotive factors for adolescent self-compassion. However, little is known about how the magnitude of the associations between these protective factors and self-compassion changes across continuous age groups and whether gender differences exist in the age-varying associations. This study aims to investigate (1) the age-varying associations of parent-adolescent relationships and school connectedness with self-compassion using time-varying effect modeling and (2) how their age-varying associations may differ by gender. A total of 14,776 adolescents aged 10-18 (mean age = 13.53 ± 2.08, 52.3% males) from Shenzhen, China participated in this study. All adolescents completed the online questionnaires in the school computer rooms. The results showed that both parent-adolescent relationships and school connectedness were positively associated with adolescent self-compassion, and the magnitudes fluctuated with age. Specifically, the association between parent-adolescent relationships and self-compassion peaked for adolescents in early adolescence, with the key timing for girls (age group of 10 years old) being slightly earlier than boys (age group of 11 years old). The association between school connectedness and self-compassion was stronger for boys in the early years of adolescence (in the age group of 12.7 years), while stronger for girls during middle adolescence (in the age group of 14.0 years). Overall, girls were more sensitive to connections with parents and schools than boys during most age groups. The findings demonstrated differential key timing for boys and girls regarding family- and school-based intervention to cultivate self-compassion and highlighted the importance of maintaining strong connections with families and schools for cultivating adolescent self-compassion, particularly for girls.

Estimating spatially varying health effects of wildland fire smoke using mobile health data

Abstract Wildland fire smoke exposures are an increasing threat to public health, highlighting the need for studying the effects of protective behaviours on reducing health outcomes. Emerging smartphone applications provide unprecedented opportunities to deliver health risk communication messages to a large number of individuals in real-time and subsequently study the effectiveness, but also pose methodological challenges. Smoke Sense, a citizen science project, provides an interactive smartphone app platform for participants to engage with information about air quality, and ways to record their own health symptoms and actions taken to reduce smoke exposure. We propose a doubly robust estimator of the structural nested mean model that accounts for spatially and time-varying effects via a local estimating equation approach with geographical kernel weighting. Moreover, our analytical framework also handles informative missingness by inverse probability weighting of estimating functions. We evaluate the method using extensive simulation studies and apply it to Smoke Sense data to increase the knowledge base about the relationship between health preventive measures and health-related outcomes. Our results show that the protective behaviours’ effects vary over space and time and find that protective behaviours have more significant effects on reducing health symptoms in the Southwest than the Northwest region of the U.S.

The uneven impact of Medicaid expansion on rural and urban Black, Latino/a, and White mortality.

To determine the differential impact of Medicaid expansion on all-cause mortality between Black, Latino/a, and White populations in rural and urban areas, and assess how expansion impacted mortality disparities between these groups. We employ a county-level time-varying heterogenous treatment effects difference-in-difference analysis of Medicaid expansion on all-cause age-adjusted mortality for those 64 years of age or younger from 2009 to 2019. For all counties within the 50 US States and the District of Columbia, we use restricted-access vital statistics data to estimate Average Treatment Effect on the Treated (ATET) for all combinations of racial and ethnic group (Black, Latino/a, White), rurality (rural, urban), and sex. We then assess aggregate ATET, as well as how the ATET changed as time from expansion increased. Medicaid expansion led to a reduction in all-cause age-adjusted mortality for urban Black populations, but not rural Black populations. Urban White populations experienced mixed effects dependent on years after expansion. Latino/a populations saw no appreciable impact. While no effect was observed for rural Black and Latino/a populations, rural White all-cause age-adjusted mortality unexpectedly increased due to Medicaid expansion. These effects reduced rural- and urban-specific Black-White mortality disparities but did not shrink the rural-urban mortality gap. The mortality-reducing impact of Medicaid expansion has been uneven across racial and ethnic groups and rural-urban status; suggesting that many populations-particularly rural individuals-are not seeing the same benefits as others. It is imperative that states work to ensure Medicaid expansion is being appropriately implemented in rural areas.

Experimental study on the hydrodynamic of foldable-wing unmanned aerial-underwater vehicles during water-exit process

The Foldable-Wing Unmanned Aerial-Underwater Vehicle (F-UAUV) demands swift movement over water surfaces at significant pitch angles during the water-exit procedure, a phase highly influenced by strong, non-linear, time-varying hydrodynamic effects. To tackle these complexities, we devised a model experiment scheme mirroring the actual water-exit process. Crafted through structural drainage comparisons, high-fidelity models were developed, complemented by a test platform featuring a double track for accurate evaluations. Subsequent water-exit tests, conducted at various speeds and angles, unveiled a consistent increase in water-exit resistance with speed, peaking at the moment of exit. Concurrently, resultant torque escalated inversely with both speed and angle. Notably, the heightened resistance due to structural drainage exhibited a tendency to stabilize during the latter half of the water-exit process. Utilizing the foundational formula for resistance in a single medium, we proposed an enhanced formula for predicting maximum water-exit resistance, a tool instrumental in facilitating F-UAUV design and powertrain selection. This paper offers an exhaustive analysis of hydrodynamic influences during the water-exit process, serving as a pivotal reference for Unmanned Aerial-Underwater Vehicles development.

Microseismic comprehensive evaluation method for coal burst: a case study in the Zhaolou Coal Mine

To explore the multiparameter precursor characteristics of pre- and post-coal burst. Based on a coal burst of LW 1305 in the Zhaolou Coal Mine, an early warning method combining stress‒strain curve and microseismic multiparameter is proposed. The research results show that coal burst was induced by the intrinsic static high-stress concentration and the strong external impact loading generated by fracturing of the key stratum. The precursors mainly characterize the enhancement trend of the S value, the sudden and sharp increase in the A(t) value, the continuous and abnormal decrease in the b value, the increasing absolute value of Z sharply and larger than 2, the continuous and abnormal decrease in the Qt value, and the dominant frequency moving to the low-frequency band. Essentially, many micro-fissures inside the key stratum initiated, converged and connected to form macro-fractures, which was verified by the attenuation rate of the K value. Considering the time-varying effect of the overlying stratum movement, the curves of the six parameters agree well with those of stress vs. strain, which indicates that it is reasonable to take the observed zone as a whole system to investigate the variation in the multiple parameters and fracturing of the key stratum. The research results can be applied to the monitoring, early warning and control of coal burst so that effective safety measures can be taken in real time.

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale.

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor. To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale. A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78. Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

Designing cancer screening trials for reduction in late-stage cancer incidence

ABSTRACT Before implementing a biomarker test for early cancer detection into routine clinical care, the test must demonstrate clinical utility, that is, the test results should lead to clinical actions that positively affect patient-relevant outcomes. Unlike therapeutical trials for patients diagnosed with cancer, designing a randomized controlled trial (RCT) to demonstrate the clinical utility of an early detection biomarker with mortality and related endpoints poses unique challenges. The hurdles stem from the prolonged natural progression of the disease and the lack of information regarding the time-varying screening effect on the target asymptomatic population. To facilitate the study design of screening trials, we propose using a generic multistate disease history model and derive model-based effect sizes. The model links key performance metrics of the test, such as sensitivity, to primary endpoints like the incidence of late-stage cancer. It also incorporates the practical implementation of the biomarker-testing program in real-world scenarios. Based on the chronological time scale aligned with RCT, our method allows the assessment of study powers based on key features of the new program, including the test sensitivity, the length of follow-up, and the number and frequency of repeated tests. The calculation tool from the proposed method will enable practitioners to perform realistic and quick evaluations when strategizing screening trials for specific diseases. We use numerical examples based on the National Lung Screening Trial to demonstrate the method.

Effect of Blanching Time Variation on Nutritional and Functional Quality Attributes of Pea Pods

Effect of Blanching Time Variation on Nutritional and Functional Quality Attributes of Pea Pods

Micromechanical and chemical characteristics of interfacial transition zone in alkali-activated slag concrete containing lightweight iron-rich aggregates

This study explored the time-varying alkali-activation effects of the lightweight aggregates (LWA) on the interfacial transition zone (ITZ) of the alkali-activated slag concrete with different curing ages, molar ratios, and alkali concentrations from 28 to 300 d. The improved micromechanical performance of the ITZ at 28 d stemmed from the early-age internal curing effect of the porous LWA. However, the post-28 d internal curing effect diminished, with the alkali-activation dominating the performance of the ITZ from 28 to 300 d. Although alkali-activated cement possessed early strength characteristics, the deconvolved phase results illustrated that alkali activation persisted in the ITZ between the LWA and cement from 28 to 300 d, leading to a high proportion of calcium silica aluminate hydrate. As a marker, Fe from the lightweight iron-rich aggregates mitigated into the alkali-activated cement, demonstrating the alkali-activated reaction between the LWA and alkali-activated slag matrix. Molecular dynamics simulations further characterized that the low content of Fe did not decrease the overall strength of the ITZ. The interfacial interaction energy revealed the alkali-activation between the LWA and cement compensated for the weakening of mechanical properties caused by Fe ions on the ITZ.

Surtvep: An R package for estimating time-varying effects

surtvep: An R package for estimating time-varying effects

Acute kidney injury in Staphylococcus aureus bacteraemia: a recurrent events analysis

ObjectivesTo estimate risk factors for acute kidney injury (AKI) and the effect of AKI on mortality in Staphylococcus aureus bacteraemia, while taking into account recurrent AKI episodes, competing risks, time-varying variables, and time-varying effects. MethodsWe performed an unplanned analysis using data from a multicentre cohort study of patients with Staphylococcus aureus bacteraemia (SAB). The primary outcome was cumulative incidence of AKI, according to Kidney Disease Improving Global Outcomes definitions. ResultsWe included 453 patients in this study of whom 194 (43%) patients experienced one or more AKI episodes. Age (hazard ratio (HR) 1.013, 95% CI 1.001–1.024), Charlson comorbidity index (HR 1.07, 95% CI 1.01–1.14), prior chronic kidney disease (HR 1.76, 95% CI 1.28–2.42), septic shock (HR 3.28, 95% CI 2.31–4.66), persistent bacteraemia (HR 1.53, 95% CI 1.08–2.17), and vancomycin therapy (HR 1.80, 95% CI 1.05–3.09) were independently associated with AKI, but flucloxacillin, cefazolin, rifampicin, and aminoglycoside therapy were not. After adjustment for confounders and immortal time bias, AKI was associated with an increased risk of 90-day mortality (HR 4.26, 95% CI 2.91–6.23). DiscussionThe incidence of AKI in SAB is high and a substantial proportion of patients develop recurrent episodes of AKI after recovery. AKI is specifically linked to the use of vancomycin and not to anti-staphylococcal penicillins. The clinical outcome of patients with SAB complicated by AKI is worse than previously estimated.

Sex differences in age-varying trends of depressive symptoms, substance use, and their associations among South Korean adults: A Time-Varying Effect Modeling (TVEM) analysis of a nationwide sample

BackgroundThis study investigated sex differences in the age-varying trends of depressive symptoms, substance use, and their relationships throughout the adult lifespan. Using a nationwide sample from South Korea, this study aimed to confirm existing patterns and identify unique characteristics specific to the South Korean context. MethodTime-Varying Effect Modeling (TVEM) was applied to data from 17,484 participants (9987 women and 7497 men) in the Korea National Health and Nutrition Examination Survey. ResultsConsistent with global trends, the results revealed a higher prevalence of depressive symptoms among women and a greater prevalence of substance use among men. However, the findings also illuminated unique patterns within the South Korean context. Substance use among South Korean men peaked during their 40s, whereas South Korean women consistently exhibited lower rates of substance use. Additionally, a stronger association between depressive symptoms and substance use was identified in women compared to men. LimitationsThe study used cross-sectional data, limiting the analysis of temporal dynamics between depressive symptoms and substance use. Additionally, TVEM cannot distinguish between aging and cohort effects. Furthermore, the assessments of depressive symptoms and substance use were based on self-report. Finally, the study did not include adolescents or sex and gender minorities in its sample. ConclusionsThese results emphasize the need for targeted interventions, particularly among women in their 20s and older adulthood, where there is a heightened co-occurrence of depressive symptoms and substance use. These findings also highlight the importance of sex- and culture-sensitive approaches tailored to the South Korean context.

Dynamic Prediction of Overall Survival for Patients with Osteosarcoma: A Retrospective Analysis of the EURAMOS-1 Clinical Trial Data.

Current prediction models for patients with ostosarcoma are restricted to predictions from a single, static point in time, such as diagnosis or surgery. These approaches discard information which becomes available during follow-up and may have an impact on patient's prognosis. This study aims at developing a dynamic prediction model providing 5-year overall survival (OS) predictions from different time points during follow-up. The developed model considers relevant baseline prognostic factors, accounting for where appropriate time-varying effects and time-varying intermediate events such as local recurrence (LR) and new metastatic disease (NM). A landmarking approach is applied to 1965 patients with high-grade resectable osteosarcoma from the EURAMOS-1 trial (NCT00143030). Results show that LR and NM negatively affected 5-year OS (HRs: 2.634, 95% CI 1.845-3.761; 8.558, 95% CI 7.367-9.942, respectively). Baseline factors with strong prognostic value (HRs > 2) included poor histological response (≥10% viable tumor), axial tumor location, and the presence of lung metastases. The effect of poor versus good histological response changed over time, becoming non-significant from 3.25 years post-surgery onwards. This time-varying effect, as well as the strong impact of disease-related time-varying variables, show the importance of including updated information collected during follow-up in the model to provide more accurate survival predictions.

The relationship between renewable energy attention and volatility: A HAR model with markov time-varying transition probability

This study investigated whether a renewable energy attention (REA) index, based on natural language processing, Google search volume data, and dimensionality reduction methodology, can predict crude oil volatility. Considering the possible non-linear and time-varying effects, we adopted the time-varying transition probability Markov switching heterogeneous autoregressive-realized volatility (TVTP-MS-HAR-realized volatility (RV)) model. To further represent the impacts of REA, we developed an asymmetric TVTP-MS-HAR-RV model based on this model framework, i.e., the ASTVTP-MS-HAR-RV model. According to the results, the in-sample estimates indicated that West Texas Intermediate (WTI) volatility is more affected by negative REA shocks than by positive ones. Moreover, REA predicted WTI volatility better during low-volatility periods than in high ones. According to the out-of-sample findings, the ASTVTP-MS-HAR-RV-F model outperformed other competing models, indicating that time-varying transition probabilities and REA information can significantly improve volatility forecasting performance.

The time-varying effects of geopolitical risk on mutual fund risk taking.

Based on a time-varying parameter vector autoregression model with stochastic volatility (TVP-VAR-SV), this paper investigates the dynamic effects of geopolitical risk on mutual fund risk taking in China across three-time horizons and at three selected time points. Overall, the impulse responses are time-varying and we find a negative effect of geopolitical risk on mutual fund risk taking until 2015, with the short-term effect being the most pronounced, suggesting that when professional investors such as mutual fund managers are faced with the stock valuation uncertainty due to a geopolitical shock, they choose to reduce market risk exposures. After 2015, the short-term effect begins to diminish and gradually turns positive, which could be explained by the fact that with the increasing abundance and diversification of investment instruments, fund managers have more effective investment tools and more sophisticated trading strategies to hedge against geopolitical risk, rather than reducing market risk exposure. Further, we explore the heterogeneous effects of eight types of geopolitical risk and three types of mutual fund. The results indicate that the effect of geopolitical actions is stronger than that of geopolitical threats, while the effect of narrow geopolitical risk is stronger than that of broad geopolitical risk. Moreover, we find that the response of the risk taking of growth funds to the geopolitical risk is weaker than that of balanced and income funds.

Partisan conflict, trade policy uncertainty, and the energy market

This paper investigates the time-varying effects of partisan conflict (PC) and trade policy uncertainty (TPU) on the energy market from January 2000 to July 2021 by employing a time-varying parameter vector autoregressive model with stochastic volatility. The results show that the pass-through effects of the PC and TPU shocks on energy return and volatility varied over time and are different across energy sub-sectors. PC and TPU had a greater impact on energy market return in the short-term, whereas the impact on energy market volatility was spread across relatively long periods. Generally, the impact of TPU on energy return and volatility was greater than that of PC at different lag periods and different points. Further, under different energy frameworks, the trend of the time-varying relationship between PC and TPU was roughly the same, indicating the robustness of our results.

The impact of metastatic sites on survival Rates and predictors of extended survival in patients with metastatic pancreatic cancer

Background objectivesThe aim of this study was to determine the role of site-specific metastatic patterns over time and assess factors associated with extended survival in metastatic PDAC. Half of all patients with pancreatic ductal adenocarcinoma (PDAC) present with metastatic disease. The site of metastasis plays a crucial role in clinical decision making due to its prognostic value. MethodsWe examined 56,757 stage-IV PDAC patients from the National Cancer Database (2016–2019), categorizing them by metastatic site: multiple, liver, lung, brain, bone, carcinomatosis, or other. The site-specific prognostic value was assessed using log-rank tests while time-varying effects were assessed by Aalen's linear hazards model. Factors associated with extended survival (>3years) were assessed with logistic regression. ResultsMedian overall survival (mOS) in patients with distant lymph node-only metastases (9.0 months) and lung-only metastases (8.1 months) was significantly longer than in patients with liver-only metastases (4.6 months, p < 0.001). However, after six months, the metastatic site lost prognostic value. Logistic regression identified extended survivors (3.6 %) as more likely to be younger, Hispanic, privately insured, Charlson-index <2, having received chemotherapy, or having undergone primary or distant site surgery (all p < 0.001). ConclusionWhile synchronous liver metastases are associated with worse outcomes than lung-only and lymph node-only metastases, this predictive value is diminished after six months. Therefore, treatment decisions beyond this time should not primarily depend on the metastatic site. Extended survival is possible in a small subset of patients with favorable tumor biology and good conditional status, who are more likely to undergo aggressive therapies.