Stacked survival models for residual lifetime data

When modelling the survival distribution of a disease for which the symptomatic progression of the associated condition is insidious, it is not always clear how to measure the failure/censoring times from some true date of disease onset. In a prevalent cohort study with follow-up, one approach for removing any potential influence from the uncertainty in the measurement of the true onset dates is through the utilization of only the residual lifetimes. As the residual lifetimes are measured from a well-defined screening date (prevalence day) to failure/censoring, these observed time durations are essentially error free. Using residual lifetime data, the nonparametric maximum likelihood estimator (NPMLE) may be used to estimate the underlying survival function. However, the resulting estimator can yield exceptionally wide confidence intervals. Alternatively, while parametric maximum likelihood estimation can yield narrower confidence intervals, it may not be robust to model misspecification. Using only right-censored residual lifetime data, we propose a stacking procedure to overcome the non-robustness of model misspecification; our proposed estimator comprises a linear combination of individual nonparametric/parametric survival function estimators, with optimal stacking weights obtained by minimizing a Brier Score loss function.

Upper estimates of heat kernels for non-local Dirichlet forms on doubling spaces

In this paper, we present a new approach to obtaining the off-diagonal upper estimate of the heat kernel for any regular Dirichlet form without a killing part on the doubling space. One of the novelties is that we have obtained the weighted L 2 {L^{2}} -norm estimate of the survival function 1 - P t B ⁢ 1 B {1-P_{t}^{B}1_{B}} for any metric ball B, which yields a nice tail estimate of the heat semigroup associated with the Dirichlet form. The parabolic L 2 {L^{2}} mean-value inequality is borrowed to use.

Promising Effects of 3-Month Period of Quercetin Phytosome® Supplementation in the Prevention of Symptomatic COVID-19 Disease in Healthcare Workers: A Pilot Study

Quercetin, for its crucial properties, fulfills the need for a multifactor action that is useful for the potential counterbalance of a COVID-19 infection. Given this background, the aim of the study was to evaluate the potential effect of 3 months’ supplementation with Quercetin Phytosome® (250 mg twice a day) as prevention against symptomatic COVID-19. In total, 120 subjects were enrolled (males, 63; females, 57; age 49 ± 12), with 60 in the supplementation group and 60 in the placebo group. No significant differences were detected between groups in terms of gender, smoking, and chronic disease. Subjects underwent rapid COVID-19 diagnostic tests every 3 weeks. During our study, 5 subjects had COVID-19, 1 out of 60 subjects in the quercetin group and 4 out of 60 in the control group. Complete clinical remission was recorded at 7 and 15 days in the quercetin and placebo groups, respectively. Analysis showed that, at 5 months, the COVID free survival function (risk of infection) was 99.8% in subjects under quercetin supplementation and 96.5% in control group. As shown by the value of EXP(B), those who had taken the supplement had a protection factor of 14% more to not contract the COVID-19 infection than that of those who had taken a placebo. Obtained results are encouraging, but further studies are required to add quercetin as regular prophylaxis.

Dependent Dirichlet Processes for Analysis of a Generalized Shared Frailty Model

Bayesian paradigm takes advantage of well-fitting complicated survival models and feasible computing in survival analysis owing to the superiority in tackling the complex censoring scheme, compared with the frequentist paradigm. In this chapter, we aim to display the latest tendency in Bayesian computing, in the sense of automating the posterior sampling, through a Bayesian analysis of survival modeling for multivariate survival outcomes with the complicated data structure. Motivated by relaxing the strong assumption of proportionality and the restriction of a common baseline population, we propose a generalized shared frailty model which includes both parametric and nonparametric frailty random effects to incorporate both treatment-wise and temporal variation for multiple events. We develop a survival-function version of the ANOVA dependent Dirichlet process to model the dependency among the baseline survival functions. The posterior sampling is implemented by the No-U-Turn sampler in Stan, a contemporary Bayesian computing tool, automatically. The proposed model is validated by analysis of the bladder cancer recurrences data. The estimation is consistent with existing results. Our model and Bayesian inference provide evidence that the Bayesian paradigm fosters complex modeling and feasible computing in survival analysis, and Stan relaxes the posterior inference.

As soon as there was life, there was danger: the deep history of survival behaviours and the shallower history of consciousness

It is often said that fear is a universal innate emotion that we humans have inherited from our mammalian ancestors by virtue of having inherited conserved features of their nervous systems. Contrary to this common sense-based scientific point of view, I have argued that what we have inherited from our mammalian ancestors, and they from their distal vertebrate ancestors, and they from their chordate ancestors, and so forth, is not a fear circuit. It is, instead, a defensive survival circuit that detects threats, and in response, initiates defensive survival behaviours and supporting physiological adjustments. Seen in this light, the defensive survival circuits of humans and other mammals can be conceptualized as manifestations of an ancient survival function—the ability to detect danger and respond to it—that may in fact predate animals and their nervous systems, and perhaps may go back to the beginning of life. Fear, on the other hand, from my perspective, is a product of cortical cognitive circuits. This conception is not just of academic interest. It also has practical implications, offering clues as to why efforts to treat problems related to fear and anxiety are not more effective, and what might make them better. This article is part of the theme issue ‘Systems neuroscience through the lens of evolutionary theory’.

Exponentiated Frechet Distribution with Application in Temperature of Assam, India Overview with New Properties and Estimation

In this paper, a new kind of distribution has suggested with the concept of exponentiate. The reliability analysis including survival function, hazard rate function, reverse hazard rate function and mills ratio has been studied here. Its quantile function and order statistics are also included. Parameters of the distribution are estimated by the method of Maximum Likelihood estimation method along with Fisher information matrix and confidence intervals have also been given. The application has been discussed with the 30 years temperature data of Silchar city, Assam, India. The goodness of fit of the proposed distribution has been compared with Frechet distribution and as a result, for all 12 months, the proposed distribution fits better than the Frechet distribution.

Bivariate Compound Exponentiated Survival Function of the Lomax Distribution: Estimation and Prediction

In this paper, bivariate compound exponentiated survival function of the Lomax distribution is constructed based on the technique considered by AL-Hussaini (2011). Some properties of the distribution are derived. Maximum likelihood estimation and prediction of the future observations are considered. Also, Bayesian estimation and prediction are studied under squared error loss function. The performance of the proposed bivariate distribution is examined using a simulation study. Finally, a real data set is analyzed under the proposed distribution to illustrate its flexibility for real-life application.

Use of Biological Treatments in Elderly Patients with Skin Psoriasis in the Real World

Biological drugs have prompted a revolution in the treatment of patients with psoriasis because of their favourable efficacy/risk profile. The aims of our study are to determine whether there is any difference in the pattern of use of biological treatments for older (65+ years) and younger patients diagnosed with plaque psoriasis by the Dermatology Service of the Hospital Universitario de Asturias (HUCA), to understand the survival of these drugs, and to identify the factors that predict the discontinuation of treatments. We report a retrospective observational hospital-based study of 300 patients registered at HUCA’s Dermatology Service who were receiving one of the following biological treatments for psoriasis on 30 November 2020: adalimumab, ustekinumab, secukinumab, or ixekizumab. The age groups were compared using Student’s t-test for quantitative variables and the chi-squared test for qualitative variables. We used the Kaplan–Meier estimator to estimate the survival function and the log-rank test to measure differences. No statistically significant differences in the frequency of use were noted between the younger and older groups, for any of the drugs studied. Survival on a drug regime, globally and individually, was similar in the two age groups. Factors predicting lower overall survival were being female, obesity, and having undergone previous biological treatment. The first three factors were influential in the under-65-year-old group, while arthritis was a significant factor for the older group.

A new measure to quantify sedentary behavior using accelerometer data: Application to the Hispanic Community Health Study/Study of Latinos

Availability of accelerometer data has made it possible to objectively and continuously monitor sedentary behavior. Various summaries of the extensive accelerometer data have been used to understand the relationship between sedentary behavior and health. However, the widely used summary measures on sedentary bouts, average bout length or its derivatives, fail to reveal patterns of accumulated sedentary behavior over time. Studies have suggested that prolonged uninterrupted sedentary behavior can be an important metric that is related to health states. Yet existing measures to capture the prolonged sedentary patterns either rely on parametric assumptions on the underlying distribution of sedentary bout length or have to categorize sedentary bout length into somewhat arbitrary categories. Gini index was also used; however, it only measures the variability in bout lengths but not the actual length. To overcome these limitations, we proposed a non-parametric weighted survival function to characterize uninterrupted sedentary behavior over time in a continuous fashion and used the area under the survival curve as a new summary measure to quantify sedentary behavior. We showed that this measure is a weighted average of bout length and contains the information on both the mean and variability of bout lengths. We demonstrated in the simulation studies that the proposed measure could better identify prolonged uninterrupted sedentary behavior and predict health outcomes. We applied this new measure and existing sedentary measures to data from the Hispanic Community Health Study/Study of Latinos to examine the association between sedentary behavior and overweight/obesity.

Mortality in rural coastal Kenya measured using the Kilifi Health and Demographic Surveillance System: a 16-year descriptive analysis

Background: The Kilifi Health and Demographic Surveillance System (KHDSS) was established in 2000 to define the incidence and prevalence of local diseases and evaluate the impact of community-based interventions. KHDSS morbidity data have been reported comprehensively but mortality has not been described. This analysis describes mortality in the KHDSS over 16 years. Methods: We calculated mortality rates from 2003–2018 in four intervals of equal duration and assessed differences in mortality across these intervals by age and sex. We calculated the period survival function and median survival using the Kaplan–Meier method and mean life expectancies using abridged life tables. We estimated trend and seasonality by decomposing a time series of monthly mortality rates. We used choropleth maps and random-effects Poisson regression to investigate geographical heterogeneity. Results: Mortality declined by 36% overall between 2003–2018 and by 59% in children aged <5 years. Most of the decline occurred between 2003 and 2006. Among adults, the greatest decline (49%) was observed in those aged 15–54 years. Life expectancy at birth increased by 12 years. Females outlived males by 6 years. Seasonality was only evident in the 1–4 year age group in the first four years. Geographical variation in mortality was ±10% of the median value and did not change over time. Conclusions: Between 2003 and 2018, mortality among children and young adults has improved substantially. The steep decline in 2003–2006 followed by a much slower reduction thereafter suggests improvements in health and wellbeing have plateaued in the last 12 years. However, there is substantial inequality in mortality experience by geographical location.