Model Selection Procedure Research Articles

Sparsified simultaneous confidence intervals for high-dimensional linear models

AbstractStatistical inference of the high-dimensional regression coefficients is challenging because the uncertainty introduced by the model selection procedure is hard to account for. Currently, the inference of the model and the inference of the coefficients are separately sought. A critical question remains unsettled; that is, is it possible to embed the inference of the model into the simultaneous inference of the coefficients? If so, then how to properly design a simultaneous inference tool with desired properties? To this end, we propose a notion of simultaneous confidence intervals called the sparsified simultaneous confidence intervals (SSCI). Our intervals are sparse in the sense that some of the intervals’ upper and lower bounds are shrunken to zero (i.e., [0, 0]), indicating the unimportance of the corresponding covariates. These covariates should be excluded from the final model. The rest of the intervals, either containing zero (e.g., $$[-1,1]$$ [ - 1 , 1 ] or [0, 1]) or not containing zero (e.g., [2, 3]), indicate the plausible and significant covariates, respectively. The SSCI intuitively suggests a lower-bound model with significant covariates only and an upper-bound model with plausible and significant covariates. The proposed method can be coupled with various selection procedures, making it ideal for comparing their uncertainty. For the proposed method, we establish desirable asymptotic properties, develop intuitive graphical tools for visualization, and justify its superior performance through simulation and real data analysis.

Improving equivalent scores for clinical neuropsychology: a new method for regression model selection.

Equivalent Scores (ES) represent a statistical gold standard to obtain thresholds for clinical inference from normative data. The procedure to obtain ES requires a preliminary mandatory step: to perform a regression model selection to obtain adjusted scores that take into account age, education, and sex. The current article, starting from theoretical considerations, focuses on this step and proposes a new and improved regression model selection method. Results from data simulation show that the newly proposed method outperforms the current one on a wide range of simulation parameters and conditions, leading to better performance in classifying impaired or unimpaired performances, and more precise ES. The article is associated with an online app and R code to allow to easily apply the method to other normative data. This new model selection procedure can be easily incorporated also with other regression-based norm approaches.

Human serum albumin: prediction model and reference values for preterm and term neonates.

Human serum albumin (HSA) concentrations may alter HSA-bound drug distribution. This study aims to describe longitudinal real-world HSA trends, and to develop a prediction model for HSA concentrations using a large neonatal cohort. Patients admitted to the neonatal intensive care unit of the University Hospitals Leuven (postnatal age (PNA) ≤28days) were retrospectively included. Using linear mixed models, covariate effects on HSA were explored. A multivariable prediction model was developed (backward model selection procedure, 1% significance level). In total, 848 neonates were included [median(interquartile range) gestational age (GA) 35(32-38)weeks, birth weight (BW) 2400(1640-3130)grams]. Median HSA concentration was 32.3(28.7-35.6)g/L. Longitudinal analyses demonstrated increasing HSA concentrations with PNA and GA for most GA groups. Univariable analyses revealed significant associations of HSA with PNA, GA, BW, current weight, total and direct bilirubin, total plasma proteins, respiratory support, mechanical ventilation, sepsis, ibuprofen use, and C-reactive protein (p-values < 0.05). A high-performance (R2 = 76.3%) multivariable HSA prediction model was developed, and PNA- and GA-dependent HSA centiles were provided. Population-specific HSA centiles and an accurate neonatal HSA prediction model were developed, incorporating both maturational and non-maturational covariates. These results can enhance future clinical care and pharmacokinetic analyses to improve pharmacotherapy of HSA-bound drugs in neonates, respectively. To improve future pharmacokinetic modeling initiatives, a high-performance human serum albumin (HSA) prediction model was developed for (pre)term neonates, using a large, single-center cohort of real-world data. This prediction model integrates both maturational and non-maturational covariates, resulting in accurate HSA predictions in neonates. Additionally, HSA centiles based on postnatal and gestational age were developed, which can be easily applied in clinical practice when interpreting HSA concentrations of neonates. In general, unbound drug fractions are higher in neonates compared to older populations. To improve pharmacotherapy of HSA-bound drugs in neonates, the obtained results can be integrated in future pharmacokinetic-pharmacodynamic analyses.

Seasonality of primary production explains the richness of pioneering benthic communities

A pattern of increasing species richness from the poles to the equator is frequently observed in many animal taxa. Ecological limits, determined by the abiotic conditions and biotic interactions within an environment, are one of the major factors influencing the geographical distribution of species diversity. Energy availability is often considered a crucial limiting factor, with temperature and productivity serving as empirical measures. However, these measures may not fully explain the observed species richness, particularly in marine ecosystems. Here, through a global comparative approach and standardised methodologies, such as Autonomous Reef Monitoring Structures (ARMS) and DNA metabarcoding, we show that the seasonality of primary production explains sessile animal richness comparatively or better than surface temperature or primary productivity alone. A Hierarchical Generalised Additive Model (HGAM) is validated, after a model selection procedure, and the prediction error is compared, following a cross-validation approach, with HGAMs including environmental variables commonly used to explain animal richness. Moreover, the linear effect of production magnitude on species richness becomes apparent only when considered jointly with seasonality, and, by identifying world coastal areas characterized by extreme values of both, we postulate that this effect may result in a positive relationship in environments with lower seasonality.

Challenging the Atacama desert: Agronomic and water conditions for pre-Hispanic maize agriculture in hyper arid environments inferred by δ18O isotopes

During the Formative Period (ca.2400–950 years BP), pre-Hispanic farmers in Pampa del Tamarugal developed a complex agricultural system in the hyper-arid Atacama Desert in Tarapacá, northern Chile. This system involved numerous agricultural fields congregated near the Tarapacá Valley’s perennial stream and the Guatacondo ravine’s ephemeral stream. Well-established villages such as Caserones, Pircas, Ramaditas, and Guatacondo accompanied these developments. However, the importance of understanding the water sources has been neglected despite the efforts to understand the relationships between pre-Hispanic agriculture and their living environment. Thus, we presumed the use of local water sources without a clear understanding of their exploitation methods or the associated technological implications. Furthermore, there is limited research on groundwater use in Tarapacá. This research aims to understand water sources used in the cultivation of maize (Zea mays) using δ18O isotope values obtained from pre-Hispanic maize kernels in Tarapacá. We compared these values with published δ18O values of water sources and applied a generalized linear model (GLM) with a Gaussian distribution, performing a Tukey’s post hoc test for multiple comparisons of means with heteroscedasticity-consistent covariance estimation. The best-fit model was identified using a stepwise model selection procedure based on the Bayesian information criterion (BIC). Our results indicate that mean δ18O values of organic matter from maize kernels range from 24.73 ‰ to 31.65 ‰. The best performing model on δ18O only included Group (BIC=298.2) as the explanatory variable as Period, Site, and Weight had no effects. These findings point towards a significant statistical relationship between the δ18O values of organic matter derived from maize kernels and the specific geographic regions they originate from. These values also show an enrichment of δ18O isotope in Tarapacá samples, except for Pica 8. These results indicate diverse agricultural strategies that utilized different water sources including perennial flow in the Tarapacá River, ephemeral runoff at Guatacondo, and groundwater in the Pampa del Tamarugal. Also, due to the enrichment of δ18O, we suggest that the circular structures found among the fields were used as water reservoirs leading to the observed enrichment of δ18O isotope values. We discuss the role of ancient agriculture technology in water management, the role of maize, cultural strategies, and the relationships with their environment. In conclusion, pre-Hispanic farmers managed limited water sources successfully despite intermittent drought for hundreds of years until the 1970 s, when agriculture was abandoned due to multiple factors.

Bayes factors for longitudinal model assessment via power posteriors

Bayes factor, defined as the ratio of the marginal likelihood functions of two competing models, is the natural Bayesian procedure for model selection. Marginal likelihoods are usually computationally demanding and complex. This scenario is particularly cumbersome in linear mixed models (LMMs) because the marginal likelihood functions involve integrals of large dimensions determined by the number of parameters and the number of random effects, which in turn increase with the number of individuals in the sample. Power posterior is an attractive proposal in the context of the Markov chain Monte Carlo algorithms that allows expressing marginal likelihoods as one-dimensional integrals over the unit range. This paper explores the use of power posteriors in LMMs and discusses its behavior through three simulation studies and a real data set on European sardine landings in the Mediterranean Sea.

A method of moments approach to asymptotically unbiased Synthetic Controls

A common approach to constructing a Synthetic Control unit is to fit on the outcome variable and covariates in pre-treatment time periods, but it has been shown by Ferman and Pinto (2021) that this approach does not provide asymptotic unbiasedness when the fit is imperfect and the number of controls is fixed. Many related panel methods have a similar limitation when the number of units is fixed. I introduce and evaluate a new method in which the Synthetic Control is constructed using a General Method of Moments approach where units not being included in the Synthetic Control are used as instruments. I show that a Synthetic Control Estimator of this form will be asymptotically unbiased as the number of pre-treatment time periods goes to infinity, even when pre-treatment fit is imperfect and the number of units is fixed. Furthermore, if both the number of pre-treatment and post-treatment time periods go to infinity, then averages of treatment effects can be consistently estimated. I provide a model selection procedure for deciding whether a unit should be used as an instrument or as a control. I also conduct simulations and an empirical application to compare the performance of this method with existing approaches in the literature.

Model selection for long-term load forecasting under uncertainty

Purpose The purpose of this study is to propose a systematic model selection procedure for long-term load forecasting (LTLF) for ex-ante and ex-post cases considering uncertainty in exogenous predictors. Design/methodology/approach The different variants of regression models, namely, Polynomial Regression (PR), Generalised Additive Model (GAM), Quantile Polynomial Regression (QPR) and Quantile Spline Regression (QSR), incorporating uncertainty in exogenous predictors like population, Real Gross State Product (RGSP) and Real Per Capita Income (RPCI), temperature and indicators of breakpoints and calendar effects, are considered for LTLF. Initially, the Backward Feature Elimination procedure is used to identify the optimal set of predictors for LTLF. Then, the consistency in model accuracies is evaluated using point and probabilistic forecast error metrics for ex-ante and ex-post cases. Findings From this study, it is found PR model outperformed in ex-ante condition, while QPR model outperformed in ex-post condition. Further, QPR model performed consistently across validation and testing periods. Overall, QPR model excelled in capturing uncertainty in exogenous predictors, thereby reducing over-forecast error and risk of overinvestment. Research limitations/implications These findings can help utilities to align model selection strategies with their risk tolerance. Originality/value To propose the systematic model selection procedure in this study, the consistent performance of PR, GAM, QPR and QSR models are evaluated using point forecast accuracy metrics Mean Absolute Percentage Error, Root Mean Squared Error and probabilistic forecast accuracy metric Pinball Score for ex-ante and ex-post cases considering uncertainty in the considered exogenous predictors such as RGSP, RPCI, population and temperature.

Types of childhood maltreatment as predictors of posttraumatic stress disorder severity and complex posttraumatic stress disorder in patients with substance use disorders

ABSTRACT Background: Childhood maltreatment (CM) can be divided into: emotional abuse (EA), physical abuse (PA), sexual abuse (SA), emotional neglect (EN), and physical neglect (PN). CM is associated with (Complex)Posttraumatic stress disorder (PTSD/CPTSD) and substance use disorder (SUD). Objective: This cross-sectional study examined the relationships between CM-subtypes with PTSD-severity and CPTSD in patients with SUD-PTSD. Method: Participants (N = 209) were treatment-seeking SUD-PTSD patients who completed the Childhood Trauma Questionnaire-short form, the Clinician-Administered PTSD Scale for DSM-5 and the International Trauma Questionnaire. Regression analyses and a model selection procedure to select an optimal model were used to examine CM-subtypes as predictors of (C)PTSD, adjusted for sex and age. Results: Total CM and all CM-types significantly predicted PTSD-severity in the univariate regression analysis, with EA begin the strongest predictor. In the multiple regression only SA predicted PTSD-severity. Subsequently, model selection indicated that the optimal model to predict PTSD-severity included EA and SA. In the univariate analyses total CM, EA, and PN significantly predicted CPTSD-classification, and total CM and all CM-types significantly predicted CPTSD-severity. In the multiple regression for CPTSD-classification only EA and PA were significant predictors and for CPTSD-severity EA, PA and SA were significant predictors. In post-hoc multiple regression analyses, only EA was a significant predictor of CPTSD-classification and CPTSD-severity. Finally, in the model selection the most parsimonious model only included EA for both CPTSD-classification and CPTSD-severity. Sex was not a moderator in the relationship between CM and PTSD, nor in CM and CPTSD. Conclusions: These findings indicate that for SUD-PTSD patients, several CM-types have predictive value for (C)PTSD-severity, however SA and especially EA appear to contribute to these complaints. Since EA does not constitute an A-criterion, it is generally more overlooked in PTSD treatment. Its impact should therefore be underlined, and clinicians should be attentive to EA in their treatment.

Mathematical modelling, selection and hierarchical inference to determine the minimal dose in IFNα therapy against myeloproliferative neoplasms.

Myeloproliferative neoplasms (MPN) are blood cancers that appear after acquiring a driver mutation in a hematopoietic stem cell. These hematological malignancies result in the overproduction of mature blood cells and, if not treated, induce a risk of cardiovascular events and thrombosis. Pegylated IFN$\alpha $ is commonly used to treat MPN, but no clear guidelines exist concerning the dose prescribed to patients. We applied a model selection procedure and ran a hierarchical Bayesian inference method to decipher how dose variations impact the response to the therapy. We inferred that IFN$\alpha $ acts on mutated stem cells by inducing their differentiation into progenitor cells; the higher the dose, the higher the effect. We found that the treatment can induce long-term remission when a sufficient (patient-dependent) dose is reached. We determined this minimal dose for individuals in a cohort of patients and estimated the most suitable starting dose to give to a new patient to increase the chances of being cured.

Forecasting US Inflation Trends: Insights from Time Series Analysis

Abstract: In our research, we dive into the world of forecasting US inflation using the ARIMA (Auto-Regressive Integrated Moving Average) model. We crafted a predictive framework by meticulously analyzing data, conducting tests like the Augmented Dickey-Fuller, and crunching metrics such as RMSE and MSE. Our meticulous model selection procedures involve carefully examining many ARIMA configurations to identify the ideal parameters, ensuring robustness and accuracy in forecasting inflation trends over the study period. Our findings show that the ARIMA (0,1,2) model outperforms others, offering reliable forecasts. These results not only showcase the model's effectiveness but also provide valuable insights for both policymakers and market players. By understanding potential inflationary trends, they can confidently navigate risks and make informed decisions, ultimately fostering stability and growth in the economy.

Impact of environmental factors on Biomphalaria pfeifferi vector capacity leading to human infection by Schistosoma mansoni in two regions of western Côte d'Ivoire

AbstractBackgroundIntestinal schistosomiasis remains a worrying health problem, particularly in western Côte d'Ivoire, despite control efforts. It is therefore necessary to understand all the factors involved in the development of the disease, including biotic and abiotic factors. The aim of this study was to examine the factors that could support the maintenance of the intermediate host and its vectorial capacity in western Côte d'Ivoire.MethodsData on river physicochemical, microbiological, and climatic parameters, the presence or absence of snails with Schistosoma mansoni, and human infections were collected between January 2020 and February 2021. Spearman rank correlation tests, Mann–Whitney, analysis of variance (ANOVA), and an appropriate model selection procedure were used to analyze the data.ResultsThe overall prevalence of infected snails was 56.05%, with infection reaching 100% in some collection sites and localities. Of 26 sites examined, 25 contained thermophilic coliforms and 22 contained Escherichia coli. Biomphalaria pfeifferi was observed in environments with lower land surface temperature (LST) and higher relative air humidity (RAH), and B. pfeifferi infection predominated in more acidic environments. Thermal coliforms and E. coli preferred higher pH levels. Lower maximum LST (LST_Max) and higher RAH and minimum LST (LST_Min) were favorable to E. coli, and lower LST_Max favored coliforms. The presence of B. pfeifferi was positively influenced by water temperature (T °C), LST_Min, RAH, and precipitation (Pp) (P < 0.05) and negatively influenced by pH, total dissolved solids (TDS), electrical conductivity (EC), LST_Max, and mean land surface temperature (LST). The parameters pH, TDS, EC, LST_Min, LST, and Pp had a positive impact on snail infection, while LST_Max had a negative impact on infection. Only pH had a positive effect on coliform and E. coli abundance. Of the 701 people examined for human schistosomiasis, 73.13% were positive for the point-of-care circulating cathodic antigen (POC-CCA) test and 12.01% for the Kato–Katz (KK) test. A positive correlation was established between human infections and the abundance of Biomphalaria (r2 = 0.879, P = 0.04959).ConclusionsThe results obtained reflect the environmental conditions that are conducive to the maintenance of S. mansoni infection in this part of the country. To combat this infection as effectively as possible, it will be necessary not only to redouble efforts but also to prioritize control according to the level of endemicity at the village level.Graphical

Area-Based Hedonic Pricing of Urban Green Amenities in Beijing: A Spatial Piecewise Approach

AbstractThis study explores a spatial piecewise approach for the hedonic valuation of the area of urban green space at different distances from a property, using a rich census dataset collected from Beijing. We explore three novel empirical strategies that improve the identification of the spatial boundary or threshold distance within which green space is capitalised into housing prices. We first delineated a series of concentric circles surrounding each property and measured the area of green space within each doughnut-shaped ring. We next estimated the hedonic price using three methods. The first is a regression spline model combined with a machine learning type of model selection procedure which objectively selects the exact location of the threshold distance that optimises the model’s predictive performance. The second is a novel matching algorithm that minimises covariate imbalance for a continuous treatment variable (i.e., the area of green space) to provide stronger causal evidence on the hedonic prices of green space at different distances. The third is a spatial difference-in-differences approach that further accounts for endogeneity bias associated with unobserved factors. For our dataset, we found that housing prices are more likely to be affected by green space within a 1 km radius.

Factors Associated with Persistent Efficacy of Abrocitinib without Flare: A Multivariable Analysis of the JADE-REGIMEN Study

Introduction: JADE REGIMEN (NCT03627767) was conducted to evaluate the feasibility of continual, reduced dose or withdrawal of abrocitinib after induction of response in patients with moderate-to-severe atopic dermatitis (AD). This post hoc analysis evaluated patient factors associated with a higher probability of persistent clinical response with abrocitinib, without flare, during a 40-week maintenance period.Methods: Data were analyzed from patients who responded to abrocitinib 200 mg induction therapy (12 weeks) and were randomly assigned to receive abrocitinib (200 or 100 mg) or placebo in the maintenance period (40 weeks). A multivariable logistic regression model with fixed and random effects was fit to determine factors associated with not experiencing flare by week 52. Fixed effects (factors) considered were randomly allocated treatment, age (&lt;18 vs ≥18 years), race, weight, prior use of systemic agents, AD duration, onset of response in induction (early vs late), Investigator’s Global Assessment score at randomization, body surface area (BSA, ≤50% vs &gt;50%) at baseline, and percentage change in Eczema Area and Severity Index (EASI) at randomization. Backward elimination and stepwise model selection procedures were applied with entry and exit criteria based on a P value threshold of 5%.Results:1233 patients received abrocitinib 200 mg in the induction period. By week 12, 798 (64.7%) achieved a response and were randomly assigned to receive maintenance treatment for 40 weeks. In total, 356 patients (44.6%) experienced a protocol-defined flare: 16.5% (200 mg), 39.6% (100 mg), and 77.5% (placebo). After model selection, the analysis identified continuation of treatment with abrocitinib 200 mg (odds ratio [OR], 19.51; 95% CI, 11.28-33.75) or reduced-dose abrocitinib 100 mg (5.27; 3.29-8.46) as the greatest predictors of not experiencing flare during maintenance. Not having previously used systemic agents (OR, 1.53; 95% CI, 1.09-2.14), lower baseline BSA (1.55; 1.10-2.17), and greater percentage reduction of EASI during induction (1.35; 1.15-1.59) were also associated with not experiencing flare.Conclusions: Multivariable analysis of JADE REGIMEN indicated that maintenance treatment with abrocitinib in patients with AD reduced the risk for flare in a dose-dependent manner. Other factors associated with maintaining response to treatment without flare included no previous exposure to systemic agents, lower extent of BSA involvement at baseline, and greater percent change in EASI during induction. These findings may assist clinicians with abrocitinib dosing decisions in the future.

The Relationship Between Transfusion in Cardiac Surgery Patients and Adverse Outcomes

ObjectivesTo understand if RBC transfusions are independently associated with a risk of mortality, prolonged intubation, or infectious, cardiac, or renal morbid outcomes. DesignRetrospective review. SettingSingle-institution, university hospital. Participants2458 Patients undergoing CABG and/or valvular surgery from July 2014 through January 2018. InterventionsNo interventions were done. Measurements and Main ResultsOur primary outcome was the occurrence of the adverse event, prolonged intubation. Infectious, cardiac, and renal composite outcomes were also defined. These composites, along with mortality, were individually analyzed and then combined to form the “any adverse events” composite. Preoperative demographic and intraoperative parameters were analyzed as univariate risk factors for adverse outcomes. Logistic regression was used to screen variables with a p-value criterion of p < 0.05 for entry into the model selection procedure. Backward selection algorithm was used with variable entry and retention criteria of p < 0.05 to select the final multivariable model. Multivariable logistic regression models were used to determine whether there was an association between volume of RBC transfusion and the defined adverse event after adjusting for covariates. A p-value<0.01 was considered statistically significant in the final model of each aim to adjust for multiple comparison. The final logistic models for each of the following outcomes indicates increased risk of that outcome per each additional unit of RBC transfused. For prolonged intubation the OR=1.493, p<0.0001, for the infectious composite outcome, the OR= 1.358, p<0.0001, for adverse renal outcomes, the OR=1.247, p<0.0001, and for any adverse event, the OR=1.467, p<0.0001. ConclusionsWe demonstrate a strong independent association between RBC transfusion volume and adverse outcomes after cardiac surgery. Efforts should be undertaken such as preoperative anemia management and control of coagulopathy in order to minimize the need for RBC transfusion.

Properties of k-record posteriors for the Weibull model

Bayesian inference is one of the most important issues under the model selection procedures in statistics. This paper performed a Bayesian analysis of posteriors using a well-known Weibull model with high fitting performance. We proved that there exist necessary and sufficient conditions for the priors to yield proper posteriors and finite posterior moments based on record data. Moreover, we found a significant implication through different well-known objective priors. Finally, for illustration purposes, a Monte Carlo simulation procedure is done in the paper. The results indicate that the developed inference may have a significant contribution within Bayesian analysis through applications across different areas.

Model selection over partially ordered sets

In problems such as variable selection and graph estimation, models are characterized by Boolean logical structure such as the presence or absence of a variable or an edge. Consequently, false-positive error or false-negative error can be specified as the number of variables/edges that are incorrectly included or excluded in an estimated model. However, there are several other problems such as ranking, clustering, and causal inference in which the associated model classes do not admit transparent notions of false-positive and false-negative errors due to the lack of an underlying Boolean logical structure. In this paper, we present a generic approach to endow a collection of models with partial order structure, which leads to a hierarchical organization of model classes as well as natural analogs of false-positive and false-negative errors. We describe model selection procedures that provide false-positive error control in our general setting, and we illustrate their utility with numerical experiments.

Why Do Bi-Factor Models Outperform Higher-Order g Factor Models? A Network Perspective.

Bi-factor models of intelligence tend to outperform higher-order g factor models statistically. The literature provides the following rivalling explanations: (i) the bi-factor model represents or closely approximates the true underlying data-generating mechanism; (ii) fit indices are biased against the higher-order g factor model in favor of the bi-factor model; (iii) a network structure underlies the data. We used a Monte Carlo simulation to investigate the validity and plausibility of each of these explanations, while controlling for their rivals. To this end, we generated 1000 sample data sets according to three competing models-a bi-factor model, a (nested) higher-order factor model, and a (non-nested) network model-with 3000 data sets in total. Parameter values were based on the confirmatory analyses of the Wechsler Scale of Intelligence IV. On each simulated data set, we (1) refitted the three models, (2) obtained the fit statistics, and (3) performed a model selection procedure. We found no evidence that the fit measures themselves are biased, but conclude that biased inferences can arise when approximate or incremental fit indices are used as if they were relative fit measures. The validity of the network explanation was established while the outcomes of our network simulations were consistent with previously reported empirical findings, indicating that the network explanation is also a plausible one. The empirical findings are inconsistent with the (also validated) hypothesis that a bi-factor model is the true model. In future model selection procedures, we recommend that researchers consider network models of intelligence, especially when a higher-order g factor model is rejected in favor of a bi-factor model.

Emotional Coping Strategies for Informal Caregivers of Hospitalized Patients: A Study of Distress and Overload.

Informal caregivers constitute a vulnerable population group that has forsaken their activities and life projects with the sole purpose of caring for a dependent individual without receiving any form of compensation in return. Emotionally, this population has been neglected by both the state and family members, exacerbating distress levels among them. The objective is to analyze the impact of emotional coping strategies on the experiences of distress and emotional burden among adult informal caregivers tending to hospitalized individuals. The role of emotional coping strategies in caregiver burden and psychological distress was examined in 460 informal adult caregivers from Guayaquil, Ecuador, who participated in an online survey. The instruments used included the Depression, Anxiety, and Stress Scale (DASS 21), the Zarit Burden Inventory (ZBI), and the Emotional Coping Questionnaire (Cuestionario de Afrontamiento Emocional). The analysis involved a multiple regression to iteratively select variables, aiming to build a simpler and more explanatory model. Employing multiple regression analysis with a stepwise model selection procedure, it was found that 16.5% of the variance in depression, 19.5% of the variance in anxiety, 19.8% of the variance in stress, and 32% of the variance in burden were predicted by specific coping strategies. Additionally, the impact of burden was estimated by the participants' gender, with greater burden observed among males due to societal role expectations. This study underscores the importance of emotional coping in the experience of informal caregivers and suggests that psychological interventions should focus on developing adaptive strategies to manage the stress and emotional burden associated with caregiving.

Comparison between inverse-probability weighting and multiple imputation in Cox model with missing failure subtype.

Identifying and distinguishing risk factors for heterogeneous disease subtypes has been of great interest. However, missingness in disease subtypes is a common problem in those data analyses. Several methods have been proposed to deal with the missing data, including complete-case analysis, inverse-probability weighting, and multiple imputation. Although extant literature has compared these methods in missing problems, none has focused on the competing risk setting. In this paper, we discuss the assumptions required when complete-case analysis, inverse-probability weighting, and multiple imputation are used to deal with the missing failure subtype problem, focusing on how to implement these methods under various realistic scenarios in competing risk settings. Besides, we compare these three methods regarding their biases, efficiency, and robustness to model misspecifications using simulation studies. Our results show that complete-case analysis can be seriously biased when the missing completely at random assumption does not hold. Inverse-probability weighting and multiple imputation estimators are valid when we correctly specify the corresponding models for missingness and for imputation, and multiple imputation typically shows higher efficiency than inverse-probability weighting. However, in real-world studies, building imputation models for the missing subtypes can be more challenging than building missingness models. In that case, inverse-probability weighting could be preferred for its easy usage. We also propose two automated model selection procedures and demonstrate their usage in a study of the association between smoking and colorectal cancer subtypes in the Nurses' Health Study and Health Professional Follow-Up Study.