The onset of several silent, chronic diseases such as diabetes can be detected only through diagnostic tests. Due to cost considerations, self-reported outcomes are routinely collected in lieu of expensive diagnostic tests in large-scale prospective investigations such as the Women’s Health Initiative. However, self-reported outcomes are subject to imperfect sensitivity and specificity. Using a semiparametric likelihood-based approach, we present time to event models to estimate the association of one or more covariates with a error-prone, self-reported outcome. We present simulation studies to assess the effect of error in self-reported outcomes with regard to bias in the estimation of the regression parameter of interest. We apply the proposed methods to prospective data from 152,830 women enrolled in the Women’s Health Initiative to evaluate the effect of statin use with the risk of incident diabetes mellitus among postmenopausal women. The current analysis is based on follow-up through 2010, with a median duration of follow-up of 12.1 years. The methods proposed in this paper are readily implemented using our freely available R software package icensmis, which is available at the Comprehensive R Archive Network (CRAN) website.

We present an ensemble tree-based algorithm for variable selection in high dimensional datasets, in settings where a time-to-event outcome is observed with error. The proposed methods are motivated by self-reported outcomes collected in large-scale epidemiologic studies, such as the Women’s Health Initiative. The proposed methods equally apply to imperfect outcomes that arise in other settings such as data extracted from electronic medical records. To evaluate the performance of our proposed algorithm, we present results from simulation studies, considering both continuous and categorical covariates. We illustrate this approach to discover single nucleotide polymorphisms that are associated with incident Type II diabetes in the Women’s Health Initiative. A freely available R package icRSF (R Core Team, 2018; Xu et al., 2018) has been developed to implement the proposed methods.

BackgroundWe evaluate the combined effect of the presence of elevated depressive symptoms and antidepressant medication use with respect to risk of type 2 diabetes among approximately 120,000 women enrolled in the Women’s Health Initiative (WHI), and compare several different statistical models appropriate for causal inference in non-randomized settings.MethodsData were analyzed for 52,326 women in the Women’s Health Initiative Clinical Trials (CT) Cohort and 68,169 women in the Observational Study (OS) Cohort after exclusions. We included follow-up to 2005, resulting in a median duration of 7.6 years of follow up after enrollment. Results from three multivariable Cox models were compared to those from marginal structural models that included time varying measures of antidepressant medication use, presence of elevated depressive symptoms and BMI, while adjusting for potential confounders including age, ethnicity, education, minutes of recreational physical activity per week, total energy intake, hormone therapy use, family history of diabetes and smoking status.ResultsOur results are consistent with previous studies examining the relationship of antidepressant medication use and risk of type 2 diabetes. All models showed a significant increase in diabetes risk for those taking antidepressants. The Cox Proportional Hazards models using baseline covariates showed the lowest increase in risk , with hazard ratios of 1.19 (95 % CI 1.06 – 1.35) and 1.14 (95 % CI 1.01 – 1.30) in the OS and CT, respectively. Hazard ratios from marginal structural models comparing antidepressant users to non-users were 1.35 (95 % CI 1.21 – 1.51) and 1.27 (95 % CI 1.13 – 1.43) in the WHI OS and CT, respectively – however, differences among estimates from traditional Cox models and marginal structural models were not statistically significant in both cohorts. One explanation suggests that time-dependent confounding was not a substantial factor in these data, however other explanations exist. Unadjusted Cox Proportional Hazards models showed that women with elevated depressive symptoms had a significant increase in diabetes risk that remained after adjustment for confounders. However, this association missed the threshold for statistical significance in propensity score adjusted and marginal structural models.ConclusionsResults from the multiple approaches provide further evidence of an increase in risk of type 2 diabetes for those on antidepressants.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The aim of this paper is to examine the evolution and development of the selection process and methods used by the Chinese government for appointing public officials. Design/methodology/approach -The paper adopts an approach combining literature and document reviews with discussion with field experts. Findings -China has a long history of selecting the most able individuals for government officials. During the political turmoil of the twentieth century, this was abandoned for ideological reasons. Current selection criteria and process are increasingly based on solid psychology and management approaches.Research limitations/implications -This paper is an overview of developments in Chinese government official selection approaches and process. Practical implications -It may serve as a baseline for future research and practice on exploring sound and institutionalized selection methods and processes. Originality/value -This is an initial attempt to explore senior Chinese officials' selection process.

Test purpose: The CATTI aims to measure competence in translation and interpreting (including simultaneous and consecutive interpreting 2 ) between Chinese and seven foreign languages: English, Japanese, French, Arabic, Russian, German, or Spanish. The test is intended to cover a wide range of domains including business, government, academia, and media, though it is not designed to assess literary translation.Length and administration: The CATTI test battery is divided into four levels of Senior, I, II, and III, from highest to lowest. The total test time for translation proficiency is 120 minutes; for interpreting proficiency, 60 minutes; for translation practice, 180 minutes; for interpreting practice at Levels I and II, 60 minutes; and for interpreting practice at Level III, 30 minutes. There is no translation or interpreting practice at the Senior level. The CATTI is administered by China Foreign Languages Publishing Administration (CFLPA) under the guidance of the Ministry of Human Resources and Social Security of the People's Republic of China. CATTI English translation Levels II and III were the first to be piloted in December 2003. Since 2011, all levels have been implemented. The test is presently administered twice annually, in May and November. Test takers can choose to take either the translation or interpreting parts, or both. During the test, candidates are allowed to bring one English-Chinese and one Chinese-English paper dictionary, but the use of electronically assisted devices is not allowed.