Multiple Imputation For Interval Censored Data With Auxiliary Variables

We propose a nonparametric multiple imputation scheme, NPMLE imputation, for the analysis of interval censored survival data. Features of the method are that it converts interval-censored data problems to complete data or right censored data problems to which many standard approaches can be used, and the measures of uncertainty are easily obtained. In addition to the event time of primary interest, there are frequently other auxiliary variables that are associated with the event time. For the goal of estimating the marginal survival distribution, these auxiliary variables may provide some additional information about the event time for the interval censored observations. We extend the imputation methods to incorporate information from auxiliary variables with potentially complex structures. To conduct the imputation, we use a working failure-time proportional hazards model to define an imputing risk set for each censored observations. The imputation schemes consist of using the data in the imputing risk set to create an exact event time for each interval censored observation. In simulation studies we show that the use of multiple imputation methods can improve the efficiency of estimators and reduce the effect of missing visits when compared to simpler approaches. We apply the approach to cytomegalovirus shedding data from an AIDS clinical trial, in which CD4 count is the auxiliary variable

Survival Analysis Using Auxiliary Variables Via Nonparametric Multiple Imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variable to recover information for censored observations. To conduct the imputation, we use two working survival model to define the nearest neighbor imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two nonparametric multiple imputation methods are considered: risk set imputation, and Kaplan-Meier estimator. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan-Meier imputation method correspond to the weighted Kaplan-Meier estimator. We also show that the Kaplan-Meier imputation method is robust to misspecification of either one of the two working models. In a simulation study with the time independent and time dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the efficiency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable

Survival estimation and testing via multiple imputation

Multiple imputation is a technique for handling data sets with missing values. The method fills in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a final result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle missing event times for censored observations in the context of nonparametric survival estimation and testing. Two nonparametric imputation schemes are considered. In risk set imputation the censored time is replaced by a random draw of the observed times amongst those at risk after the censoring time. In Kaplan–Meier (KM) imputation the imputed time is a draw from the estimated distribution of event times amongst those at risk after the censoring time. We show that with a large number of imputes the estimates from both methods reproduce the KM estimator. In a simulation study we show that the inclusion of a bootstrap stage in the multiple imputation algorithm gives coverage rates of confidence intervals that are comparable to that from Greenwood’s formula. Connections to the redistribute to the right algorithm are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91899/1/Taylor Stat Prob Let 2002.pd

Survival Analysis Using Auxiliary Variables via Nonparametric Multiple Imputation

We develop an approach, based on multiple imputation, that estimates the marginal survival distribution in survival analysis using auxiliary variables to recover information for censored observations. To conduct the imputation, we use two working survival models to de fine a nearest neighbour imputing risk set. One model is for the event times and the other for the censoring times. Based on the imputing risk set, two non-parametric multiple imputation methods are considered: risk set imputation, and Kaplan–Meier imputation. For both methods a future event or censoring time is imputed for each censored observation. With a categorical auxiliary variable, we show that with a large number of imputes the estimates from the Kaplan–Meier imputation method correspond to the weighted Kaplan–Meier estimator. We also show that the Kaplan–Meier imputation method is robust to mis-speci cation of either one of the two working models. In a simulation study with time independent and time-dependent auxiliary variables, we compare the multiple imputation approaches with an inverse probability of censoring weighted method. We show that all approaches can reduce bias due to dependent censoring and improve the e ciency. We apply the approaches to AIDS clinical trial data comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. Copyright 2005 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/91939/1/Hsu Stat in Med paper 2006.pd

PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition

Fingerprint recognition on mobile devices is an important method for identity verification. However, real fingerprints usually contain sweat and moisture which leads to poor recognition performance. In addition, for rolling out slimmer and thinner phones, technology companies reduce the size of recognition sensors by embedding them with the power button. Therefore, the limited size of fingerprint data also increases the difficulty of recognition. Denoising the small-area wet fingerprint images to clean ones becomes crucial to improve recognition performance. In this paper, we propose an end-to-end trainable progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a shared stage and specific multi-task stages, enabling the network to train binary and non-binary fingerprints sequentially. The binary information is regarded as guidance for output enhancement which is enriched with the ridge and valley details. Moreover, a novel residual scaling mechanism is introduced to stabilize the training process. Experiment results on the FW9395 and FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising performance on the wet-fingerprint denoising and significantly improves the fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395 dataset, the FRR of fingerprint recognition can be declined from 9.45% to 1.09%

Molecular identification for epigallocatechin-3-gallate-mediated antioxidant intervention on the H2O2-induced oxidative stress in H9c2 rat cardiomyoblasts

Epigallocatechin-3-gallate (EGCG) has been documented for its beneficial effects protecting oxidative stress to cardiac cells. Previously, we have shown the EGCG-mediated cardiac protection by attenuating reactive oxygen species and cytosolic Ca2+ in cardiac cells during oxidative stress and myocardial ischemia. Here, we aimed to seek a deeper elucidation of the molecular anti-oxidative capabilities of EGCG in an H2O2-induced oxidative stress model of myocardial ischemia injury using H9c2 rat cardiomyoblasts

Emotion and Concentration Integrated System: Applied to the Detection and Analysis of Consumer Preference

With the expansion of consumer market, the appearance becomes an important issue when consumers make decisions under the situation of similar qualities and contents. Accordingly, to attract consumers, companies cost and take much attention on product appearance. Compared to using questionnaires individually, obtaining humans’ thoughts directly from their brains can accurately grasp the actual preference of consumers, which can provide effective and precious decisions for companies. \ In this study, consumers’ brainwaves which are related to concentration and emotion are extracted by wearing a portable and wireless Electroencephalography (EEG) device. The extracted EEG data are then trained by using perceptron learning algorithm (PLA) to make the judgments of concentration and emotion work well with each subject. They are then applied to the detection and analysis of consumer preference. Finally, the questionnaires are also performed and used as the reference on training process. They are integrated with brainwaves data to create one prediction model which can improve the accuracy significantly. The Partial Least Squares is used to compare the correlation between different factors in the model, to ensure the test can accurately meet consumers’ thoughts

Epigallocatechin-3-gallate-mediated cardioprotection by Akt/GSK-3β/caveolin signalling in H9c2 rat cardiomyoblasts

Background: Epigallocatechin-3-gallate (EGCg) with its potent anti-oxidative capabilities is known for its beneficialeffects ameliorating oxidative injury to cardiac cells. Although studies have provided convincing evidence tosupport the cardioprotective effects of EGCg, it remains unclear whether EGCg affect trans-membrane signalling incardiac cells. Here, we have demonstrated the potential mechanism for cardioprotection of EGCg againstH2O2-induced oxidative stress in H9c2 cardiomyoblasts.Results: Exposing H9c2 cells to H2O2 suppressed cell viability and altered the expression of adherens and gapjunction proteins with increased levels of intracellular reactive oxygen species and cytosolic Ca2+. These detrimentaleffects were attenuated by pre-treating cells with EGCg for 30 min. EGCg also attenuated H2O2-mediated cell cyclearrest at the G1-S phase through the glycogen synthase kinase-3β (GSK-3β)/β-catenin/cyclin D1 signalling pathway.To determine how EGCg targets H9c2 cells, enhanced green fluorescence protein (EGFP) was ectopically expressedin these cells. EGFP-emission fluorescence spectroscopy revealed that EGCg induced dose-dependent fluorescencechanges in EGFP expressing cells, suggesting that EGCg signalling events might trigger proximity changes of EGFPexpressed in these cells.Proteomics studies showed that EGFP formed complexes with the 67 kD laminin receptor, caveolin-1 and -3,β-actin, myosin 9, vimentin in EGFP expressing cells. Using in vitro oxidative stress and in vivo myocardial ischemiamodels, we also demonstrated the involvement of caveolin in EGCg-mediated cardioprotection. In addition,EGCg-mediated caveolin-1 activation was found to be modulated by Akt/GSK-3β signalling in H2O2-induced H9c2cell injury.Conclusions: Our data suggest that caveolin serves as a membrane raft that may help mediate cardioprotectiveEGCg transmembrane signalling

Racial disparities in the incidence of colon cancer in patients with inflammatory bowel disease

Background: Studies have explored the relationship between inflammatory bowel disease (IBD) [ulcerative colitis (UC) and Crohn's disease (CD)] and colon cancer (CC). Additionally, racial disparities in the incidence of CC is well known. However, the impact of racial disparity in IBD patients who develop CC remains unclear. The aim of this study is to address the knowledge gap in this particular group of patients. Methods: A retrospective analysis was done using the National Inpatient Sample (NIS) database from 2011. We included patients with IBD over age >= 18 years with a diagnosis of CC. Patients were stratified by race, gender, age, presence of IBD and CC. Statistical analysis was performed to compare the groups. Results: A total of 57,542 patients were included (CD: 36,357, UC: 21,001). Of all patients with and without IBD, advanced age, Black and Asian race conferred an increased risk of developing CC, whereas female gender, Hispanic and Native American race conferred a protective effect. In patients with IBD, advanced age conferred an increased risk for developing CC while female gender conferred a protective effect. In this subset of patients, black race conferred a protective effect. Conclusions: Racial disparity exists in the overall incidence of CC and among patients with IBD who develop CC. Interestingly, black race conferred a protective effect for patients with IBD, contrary to what is seen in the general population. These findings could be attributed to the environmental factors and genetic makeup between racial groups. Further studies are warranted to better understand these disparities.Open Access JournalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]