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The Cox regression analysis is used to determine the relationship between a dependent variable and covariates in survival analysis involving censored data. The proportional hazards assumption is one of the most important assumptions of Cox regression. Outliers may have a strong influence on the Cox regression model’s parameter estimates and lead to violation of the proportional hazard assumption. Therefore, having outliers in the data set is a problem for researchers. In this case, robust estimations are commonly used to infer the parameters in a more robust way. However, we explore a new approach consisting of considering an outlier as missing data and replacing it by the multiple imputation method. The aim of this study is to compare these two methods through simulation. Furthermore, an analysis of a lung cancer data set is considered for illustration. According to the results of the study carried out based on simulated data sets and a real data set, the multiple imputation method, which is a missing data analysis method, solves the problem of outliers better than the robust estimation method, as the outcome is closer to the results obtained through original data., Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

The Cox regression analysis is used to determine the relationship between a dependent variable and covariates in survival analysis involving censored data. The proportional hazards assumption is one of the most important assumptions of Cox regression. Outliers may have a strong influence on the Cox regression model’s parameter estimates and lead to violation of the proportional hazard assumption. Therefore, having outliers in the data set is a problem for researchers. In this case, robust estimations are commonly used to infer the parameters in a more robust way. However, we explore a new approach consisting of considering an outlier as missing data and replacing it by the multiple imputation method. The aim of this study is to compare these two methods through simulation. Furthermore, an analysis of a lung cancer data set is considered for illustration. According to the results of the study carried out based on simulated data sets and a real data set, the multiple imputation method, which is a missing data analysis method, solves the problem of outliers better than the robust estimation method, as the outcome is closer to the results obtained through original data., Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

Background: College students are vulnerable to suffering from anxiety and depression. Moreover, mental disorders can contribute to drug consumption or inappropriate use of prescribed drugs. Studies on this topic in Spanish college students are limited. This work analyses anxiety and depression and psychoactive drug intake pattern in the post-COVID era in college students. Methods: An online survey was conducted among college students from UCM (Spain). The survey collected data including demographic, academic student perception, GAD-7 and PHQ-9 scales, and psychoactive substances consumption. Results: A total of 6,798 students were included; 44.1% (CI95%: 42.9 to 45.3) showed symptoms of severe anxiety and 46.5% (CI95%: 45.4 to 47.8) symptoms of severe or moderately severe depression. The perception of these symptoms did not change after returning to face-to-face university classes in the post-COVID19 era. Despite the high percentage of cases with clear symptoms of anxiety and depression, most students never had a diagnosis of mental illnesses [anxiety 69.2% (CI95%: 68.1 to 70.3) and depression 78.1% (CI95%: 77.1 to 79.1)]. Regarding psychoactive substances, valerian, melatonin, diazepam, and lorazepam were the most consumed. The most worrying issue was the consumption of diazepam, 10.8% (CI95%: 9.8 to 11.8), and lorazepam, 7.7% (CI95%: 6.9 to 8.6) without medical prescription. Amongst illicit drugs, cannabis is the most consumed. Limitations: The study was based on an online survey. Conclusions: The high prevalence of anxiety and depression aligned with poor medical diagnosis and high intake of psychoactive drugs should not be underestimated. University policies should be implemented to improve the well-being of students., Observatorio del Estudiante, Universidad Complutense de Madrid, Depto. de Farmacia Galénica y Tecnología Alimentaria, Fac. de Farmacia, TRUE, pub

With a treatment policy strategy, therapies are evaluated regardless of the disturbance caused by intercurrent events (ICEs). Implementing this estimand is challenging if subjects are not followed up after the ICE. This circumstance can be dealt with using delta adjustment (DA) or reference-based (RB) imputation. In the survival field, DA and RB imputation have been researched so far using multiple imputation (MI). Here, we present a fully analytical solution. We use the illness–death multistate model with the following transitions: (a) from the initial state to the event of interest, (b) from the initial state to the ICE, and (c) from the ICE to the event. We estimate the intensity function of transitions (a) and (b) using flexible parametric survival models. Transition (c) is assumed unobserved but identifiable using DA or RB imputation assumptions. Various rules have been considered: no ICE effect, DA under proportional hazards (PH) or additive hazards (AH), jump to reference (J2R), and (either PH or AH) copy increment from reference. We obtain the marginal survival curve of interest by calculating, via numerical integration, the probability of transitioning from the initial state to the event of interest regardless of having passed or not by the ICE state. We use the delta method to obtain standard errors (SEs). Finally, we quantify the performance of the proposed estimator through simulations and compare it against MI. Our analytical solution is more efficient than MI and avoids SE misestimation—a known phenomenon associated with Rubin's variance equation., Ministerio de Ciencia, Innovación y Universidades (España), Depto. de Estadística e Investigación Operativa, Depto. de Estadística y Ciencia de los Datos, Fac. de Ciencias Matemáticas, Fac. de Estudios Estadísticos, TRUE, pub

Background College students are vulnerable to suffering from anxiety and depression. Moreover, mental disorders can contribute to drug consumption or inappropriate use of prescribed drugs. Studies on this topic in Spanish college students are limited. This work analyses anxiety and depression and psychoactive drug intake pattern in the post-COVID era in college students. Methods An online survey was conducted among college students from UCM (Spain). The survey collected data including demographic, academic student perception, GAD-7 and PHQ-9 scales, and psychoactive substances consumption. Results A total of 6,798 students were included; 44.1% (CI95%: 42.9 to 45.3) showed symptoms of severe anxiety and 46.5% (CI95%: 45.4 to 47.8) symptoms of severe or moderately severe depression. The perception of these symptoms did not change after returning to face-to-face university classes in the post-COVID19 era. Despite the high percentage of cases with clear symptoms of anxiety and depression, most students never had a diagnosis of mental illnesses [anxiety 69.2% (CI95%: 68.1 to 70.3) and depression 78.1% (CI95%: 77.1 to 79.1)]. Regarding psychoactive substances, valerian, melatonin, diazepam, and lorazepam were the most consumed. The most worrying issue was the consumption of diazepam, 10.8% (CI95%: 9.8 to 11.8), and lorazepam, 7.7% (CI95%: 6.9 to 8.6) without medical prescription. Amongst illicit drugs, cannabis is the most consumed. Limitations The study was based on an online survey. Conclusions The high prevalence of anxiety and depression aligned with poor medical diagnosis and high intake of psychoactive drugs should not be underestimated. University policies should be implemented to improve the well-being of students., Observatorio del Estudiante-UCM, Depto. de Farmacia Galénica y Tecnología Alimentaria, Fac. de Farmacia, TRUE, pub

Supplementary data to this article can be found online at https://doi.org/10.1016/j.jclinane.2021.110596, Study objective: To develop individualized dynamic predictions for the occurrence of acute kidney injury (AKI) during the first postoperative week after cardiac surgery. Design: Observational retrospective cohort study. Setting: Single university teaching hospital in Madrid, Spain. Patients: 3960 cases of major cardiac surgery performed from January 2002 to December 2013. Measurements: Baseline demographic and clinical characteristics, intraoperative risk factors, and repeated postoperative estimated glomerular filtration rates (eGFR). The primary outcome was AKI during the first postoperative week (stage 1 or higher of the Acute Kidney Injury Network). The dataset was split in two random samples (exploratory and validation). By combining time-to-event outcomes (AKI), and longitudinal data (repeated postoperative eGFR), we developed two different joint models for patients with normal and high baseline levels of serum creatinine (sCr). Main results: AKI occurred in 1105 patients (31%, 95% confidence interval [CI] 29.5-32.5) in the exploratory sample and 128 (32.2%, 95% CI 27.6-36.8) in the validation sample. For high baseline sCr patients, the risk of an AKI event was associated with the eGFR trajectory (hazard ratio [HR] 0.91, 95% CI 0.90-0.92), as well as with age, and cardiopulmonary bypass time. The normal baseline sCr model incorporated the same covariates and intraoperative transfusion. In this second model, the risk of an AKI event was associated with both the eGFR trajectory (HR 0.91, 95% CI 0.91-0.92, for the current value of eGFR), and with its slope at that point (HR 0.96, 95% CI 0.94-0.99). So AKI risk decreased when the eGFR values increased, in accordance with the speed of this rise. Internal validation showed good discrimination and calibration of both joint models. The AUCs were always higher than 0.7. Conclusions: The joint models obtained combining both patient risk factors and postoperative eGFR values, are useful to predict individualized risk of, Ministerio de Ciencia e Innovación, Spain., Depto. de Estadística y Ciencia de los Datos, Fac. de Estudios Estadísticos, TRUE, pub

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of F P F s ∈ (0, 1) as well as over a restricted range of F P F s and compare them with some existing ones through a simulation study and through some real data examples. We also show their capability to compare two diagnostic markers., Ministerio de Economía, Comercio y Empresa (España), Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

Receiver operating characteristic (ROC) curves are useful statistical tools for medical diagnostic testing. It has been proved its capability to assess diagnostic marker’s ability to distinguish between healthy and diseased subjects and to compare different diagnostic markers. In this paper we introduce non parametric ROC summary statistics to assess a ROC curve across the entire range of F P F s ∈ (0, 1) as well as over a restricted range of F P F s and compare them with some existing ones through a simulation study and through some real data examples. We also show their capability to compare two diagnostic markers., Ministerio de Economía, Comercio y Empresa (España), Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub