On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223-229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496-509, 1999) and also time-dependent solutions of pseudo-value regression equation

Return to the workforce following first hospitalization for heart failure: a Danish nationwide cohort study

Background: Return to work is important financially, as a marker of functional status and for self-esteem in patients developing chronic illness. We examined return to work after first heart failure (HF) hospitalization. Methods: By individual-level linkage of nationwide Danish registries, we identified 21455 patients of working age (18-60 years) with a first HF hospitalization in the period of 1997-2012. Of these 11880 (55%) were in the workforce prior to HF hospitalization and comprised the study population. We applied logistic regression to estimate odds ratios (OR) for associations between age, sex, length of hospital stay, level of education, income, comorbidity and return to work. Results: One year after first HF hospitalization, 8040 (67.7%) returned to the workforce, 2981 (25.1%) did not, 805 (6.7%) died and 54 (0.5%) emigrated. Predictors of return to work included younger age (18-30 vs. 51-60 years, OR 3.12; 95% CI 2.42-4.03), male sex (OR 1.22 [1.18-1.34]) and level of education (long-higher vs. basic school OR 2.06 [1.63-2.60]). Conversely, hospital stay >7 days (OR 0.56 [0.51-0.62]) and comorbidity including history of stroke (OR 0.55 [0.45-0.69]), chronic kidney disease (OR 0.46 [0.36-0.59]), chronic obstructive pulmonary disease (OR 0.62 [0.52-0.75]), diabetes (OR 0.76 [0.68-0.85]) and cancer (OR 0.49 [0.40-0.61]) were all significantly associated with lower chance of return to work. Conclusions: Patients in the workforce prior to HF hospitalization had low mortality but high risk of detachment from the workforce one year later. Young age, male sex, and higher level of education were predictors of return to work

Green Bay\u27s first radio station is 65 years old

Article from the Green Bay Press Gazette discussing the sixty-fifth anniversary of WHBY radio

Area’s ‘Marconi’ is dead at 96

Editorial on the Pioneer in radio, Cletus Collom. A review of his achievements and the founding of WHBY

Sex, Labor, And The American Way: Detroit Aesthetic In Mid-Twentieth-Century Literature

The essay analyzes Sinclair Lewis short fiction in If I Were Boss, U.S.A. by John Dos Passos, Let Us Now Praise Famous Men by James Agee and Walker Evans, and Last Exit to Brooklyn by Hubert Selby, Jr. The primary literature is juxtaposed with a study of visual texts and historic research with a locational and thematic basis in Detroit. Ford Times and early automobile advertisements, Diego Rivera\u27s mural Detroit Industry, photographs of the Sojourner Truth housing project riots, and the accounts of gay union workers comprise a framework for each of the central texts. Detroit aesthetic is gritty, realist, and shaped by and in the defiance of the organizational logic of the Ford Motor Company. This aesthetic is observable in the following ways: 1) Form-- publishing format is shaped by commercial concerns that sometimes determined the content and distribution methods of the work. Some texts are self-reflexive about their own consumption. The narratives are of each are crafted in distinct components that often resist the temptation to be read as working together like a well-oiled machine. 2) Subject--labor, production, consumption, and advertising are all recurring motifs the authors use figuratively and literally. 3) Language--the wording and punctuation represent the fast-paced modern dialect; the assemblage of new signifiers do not line up with the objects they traditionally signified in. 4) Gender, sexuality, and reproduction--control and order rein in desire and sexuality. Women in the workforce cause traditional gender codes to be redesigned, resulting in fear of the loss of efficiency. Masculine identity is as equally shaped by capitalism as women\u27s roles are. Production and consumption are tied to sexual reproduction in different ways in each text

Investigating the prediction ability of survival models based on both clinical and omics data: two case studies

In biomedical literature numerous prediction models for clinical outcomes have been developed based either on clinical data or, more recently, on high-throughput molecular data (omics data). Prediction models based on both types of data, however, are less common, although some recent studies suggest that a suitable combination of clinical and molecular information may lead to models with better predictive abilities. This is probably due to the fact that it is not straightforward to combine data with different characteristics and dimensions (poorly characterized high dimensional omics data, well-investigated low dimensional clinical data). In this paper we analyze two publicly available datasets related to breast cancer and neuroblastoma, respectively, in order to show some possible ways to combine clinical and omics data into a prediction model of time-to-event outcome. Different strategies and statistical methods are exploited. The results are compared and discussed according to different criteria, including the discriminative ability of the models, computed on a validation dataset

The state learner -- a super learner for right-censored data

In survival analysis, prediction models are needed as stand-alone tools and in applications of causal inference to estimate nuisance parameters. The super learner is a machine learning algorithm which combines a library of prediction models into a meta learner based on cross-validated loss. In right-censored data, the choice of the loss function and the estimation of the expected loss need careful consideration. We introduce the state learner, a new super learner for survival analysis, which simultaneously evaluates libraries of prediction models for the event of interest and the censoring distribution. The state learner can be applied to all types of survival models, works in the presence of competing risks, and does not require a single pre-specified estimator of the conditional censoring distribution. We establish an oracle inequality for the state learner and investigate its performance through numerical experiments. We illustrate the application of the state learner with prostate cancer data, as a stand-alone prediction tool, and, for causal inference, as a way to estimate the nuisance parameter models of a smooth statistical functional