A robust imputation method for missing responses and covariates in sample selection models

Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputation techniques, especially in the absence of exclusion restrictions and deviation from normality. Motivated by the 2003-2004 NHANES data, where previous authors have studied the effect of socio-economic status on blood pressure with missing data on income variable, we proposed the use of a robust imputation technique based on the selection-t sample selection model. The imputation method, which is developed within the frequentist framework, is compared with competing alternatives in a simulation study. The results indicate that the robust alternative is not susceptible to the absence of exclusion restrictions - a property inherited from the parent selection-t model - and performs better than models based on the normal assumption even when the data is generated from the normal distribution. Applications to missing outcome and covariate data further corroborate the robustness properties of the proposed method. We implemented the proposed approach within the MICE environment in R Statistical Software

Assessing calibration in an external validation study

Evaluating a prediction model using a separate dataset from which the model was developed is a crucial step in assessing its predictive performance, often referred to as external validation. The recent study by Tetrault and colleagues modified their previous prediction model by omitting one of the predictors and then re-fitting the model on the original development data from 12 sites from North America. The modified prediction model was subsequently evaluated on a larger international cohort from the AOSpine CSM-I trial. Whilst it is encouraging to see authors carrying out such external validation studies, there are concerns in the analysis which need highlighting

A robust imputation method for missing responses and covariates in sample selection models

Sample selection arises when the outcome of interest is partially observed in a study. Although sophisticated statistical methods in the parametric and non-parametric framework have been proposed to solve this problem, it is yet unclear how to deal with selectively missing covariate data using simple multiple imputation techniques, especially in the absence of exclusion restrictions and deviation from normality. Motivated by the 2003-2004 NHANES data, where previous authors have studied the effect of socio-economic status on blood pressure with missing data on income variable, we proposed the use of a robust imputation technique based on the selection-t sample selection model. The imputation method, which is developed within the frequentist framework, is compared with competing alternatives in a simulation study. The results indicate that the robust alternative is not susceptible to the absence of exclusion restrictions - a property inherited from the parent selection-t model - and performs better than models based on the normal assumption even when the data is generated from the normal distribution. Applications to missing outcome and covariate data further corroborate the robustness properties of the proposed method. We implemented the proposed approach within the MICE environment in R Statistical Software

Motivation and Overview of Hydrological Ensemble Post-processing

In this introduction to this chapter on hydrologic post-processing, we discuss the different but complementary directives that the “art” of post-processing must satisfy: the particular directive defined by specific applications and user needs; versus the general directive of making any ensemble member indistinguishable from the observations. Also discussed are the features of hydrologic post-processing that are similar and separate from meteorological post-processing, providing a tie-in to early chapters in this handbook. We also provide an overview of the different aspects the practitioner should keep in mind when developing and implementing algorithms to adequately “correct and calibrate” ensemble forecasts: when forecast uncertainties should be characterized separately versus maintaining a “lumped” approach; additional aspects of hydrological ensembles that need to be maintained to satisfy additional user requirements, such as temporal covariability in the ensemble time series, an overview of the different post-processing approaches being used in practice and in the literature, and concluding with a brief overview of more specific requirements and challenges implicit in the “art” of post-processing

Prognostic biomarkers for oral tongue squamous cell carcinoma : a systematic review and meta-analysis

Background: Identifying informative prognostic biomarkers for oral tongue squamous cell carcinoma (OTSCC) is of great importance in order to better predict tumour behaviour and to guide treatment planning. Here, we summarise existing evidence regarding immunohistochemical prognostic biomarkers for OTSCC. Methods: A systematic search of the literature was performed using the databases of Scopus, Ovid Medline, Web of Science and Cochrane Library. All studies which had investigated the prognostic significance of immunohistochemical biomarkers in OTSCC during the period from 1985 to 2015 were retrieved. For the five most often evaluated biomarkers a random-effects meta-analysis on overall survival was performed, including those studies that provided the necessary statistical results. Results: A total of 174 studies conducted during the last three decades were found, and in these 184 biomarkers were evaluated for the prognostication of OTSCC. The five biomarkers most frequently assessed were p53, Ki-67, p16, VEGFs and cyclin D1. In the meta-analyses, the most promising results of the prognostic power for OTSCC were obtained for cyclin D1. For studies of VEGF A and C the results were equivocal, but the pooled analysis of VEGF A separately showed it to be a useful prognosticator for OTSCC. There was no sufficient evidence to support p53, Ki-67 and p16 as prognostic biomarkers for OTSCC. Limitations in the quality of the published studies (e.g., small cohorts, lack of compliance with REMARK guidelines) are widespread. Conclusions: Numerous biomarkers have been presented as useful prognosticators for OTSCC, but the quality of the conduct and reporting of original studies is overall unsatisfactory which does not allow reliable conclusions. The value of two biomarkers (VEGFA and cyclin D1) should be validated in a multicentre study setting following REMARK guidelines.Peer reviewe

Neutrophil gelatinase-associated lipocalin for assessment of acute kidney injury in cirrhosis. A prospective study

Kidney biomarkers appear to be useful in differential diagnosis between Acute Tubular Necrosis (ATN) and other types of AKI in cirrhosis, particularly hepatorenal syndrome (HRS-AKI). Distinction is important because treatment is different. However, kidney biomarkers are still not used in clinical practice. Aim of the current study was to investigate the accuracy of several biomarkers in differential diagnosis of AKI and in predicting kidney outcome and patient survival