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PubMed Journals Articles About "Control Based Imputation Sensitivity Analyses Informative Censoring Recurrent" RSS

17:04 EDT 19th September 2018 | BioPortfolio

Control Based Imputation Sensitivity Analyses Informative Censoring Recurrent PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Control Based Imputation Sensitivity Analyses Informative Censoring Recurrent articles that have been published worldwide.

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Showing "Control based imputation sensitivity analyses informative censoring recurrent" PubMed Articles 1–25 of 52,000+

Multiple imputation of missing data in nested case-control and case-cohort studies.

The nested case-control and case-cohort designs are two main approaches for carrying out a substudy within a prospective cohort. This article adapts multiple imputation (MI) methods for handling missing covariates in full-cohort studies for nested case-control and case-cohort studies. We consider data missing by design and data missing by chance. MI analyses that make use of full-cohort data and MI analyses based on substudy data only are described, alongside an intermediate approach in which the imputation...


Imputation-Based HLA Typing with SNPs in GWAS Studies.

SNP-based imputation approaches for human leukocyte antigen (HLA) typing take advantage of the extended haplotype structure within the major histocompatibility complex (MHC) to predict classical HLA alleles using dense SNP genotypes, such as those available on chip panels of genome-wide association study (GWAS). These methods enable HLA analyses of classical alleles on existing SNP datasets genotyped in GWAS studies at no extra cost. Here, I describe the workflow of HIBAG, an imputation method with attribut...

Exploring the Effects of Early Censoring and Analysis of Clinical Trial Survival Data on Effectiveness and Cost-Effectiveness Estimation through a Case Study in Advanced Breast Cancer.

Interim analyses of clinical trial data are frequently used to provide evidence to obtain marketing authorization for new drugs. However, results from such analyses may not reflect true estimates of relative effectiveness when trial follow-up is complete. Survival results, available at 2 time points from a breast cancer clinical trial, were compared to test the hypothesis that using immature data and a widely used right-censoring rule leads to biased survival estimates. Kaplan-Meier progression-free and ove...


A heuristic method for fast and accurate phasing and imputation of single-nucleotide polymorphism data in bi-parental plant populations.

Key message New fast and accurate method for phasing and imputation of SNP chip genotypes within diploid bi-parental plant populations. This paper presents a new heuristic method for phasing and imputation of genomic data in diploid plant species. Our method, called AlphaPlantImpute, explicitly leverages features of plant breeding programmes to maximise the accuracy of imputation. The features are a small number of parents, which can be inbred and usually have high-density genomic data, and few recombinatio...

Using a Counting Process Method to Impute Censored Follow-Up Time Data.

Censoring occurs when complete follow-up time information is unavailable for patients enrolled in a clinical study. The process is considered to be informative (non-ignorable) if the likelihood function for the model cannot be partitioned into a set of response parameters that are independent of the censoring parameters. In such cases, estimated survival time probabilities may be biased, prompting the need for special statistical methods to remedy the situation. The problem is especially salient when censor...

Missing data in trial-based cost-effectiveness analysis: An incomplete journey.

Cost-effectiveness analyses (CEA) conducted alongside randomised trials provide key evidence for informing healthcare decision making, but missing data pose substantive challenges. Recently, there have been a number of developments in methods and guidelines addressing missing data in trials. However, it is unclear whether these developments have permeated CEA practice. This paper critically reviews the extent of and methods used to address missing data in recently published trial-based CEA. Issues of the He...

A Kriging based spatiotemporal approach for traffic volume data imputation.

Along with the rapid development of Intelligent Transportation Systems, traffic data collection technologies have progressed fast. The emergence of innovative data collection technologies such as remote traffic microwave sensor, Bluetooth sensor, GPS-based floating car method, and automated license plate recognition, has significantly increased the variety and volume of traffic data. Despite the development of these technologies, the missing data issue is still a problem that poses great challenge for data ...

Genotype imputation performance of three reference panels using African ancestry individuals.

Genotype imputation estimates unobserved genotypes from genome-wide makers, to increase genome coverage and power for genome-wide association studies. Imputation has been successful for European ancestry populations in which very large reference panels are available. Smaller subsets of African descent populations are available in 1000 Genomes (1000G), the Consortium on Asthma among African ancestry Populations in the Americas (CAAPA) and the Haplotype Reference Consortium (HRC). We compared the performance ...

Accuracy of genotype imputation in Labrador Retrievers.

The dog is a valuable model species for the genetic analysis of complex traits, and the use of genotype imputation in dogs will be an important tool for future studies. It is of particular interest to analyse the effect of factors like single nucleotide polymorphism (SNP) density of genotyping arrays and relatedness between dogs on imputation accuracy due to the acknowledged genetic and pedigree structure of dog breeds. In this study, we simulated different genotyping strategies based on data from 1179 Labr...

Evaluation and application of summary statistic imputation to discover new height-associated loci.

As most of the heritability of complex traits is attributed to common and low frequency genetic variants, imputing them by combining genotyping chips and large sequenced reference panels is the most cost-effective approach to discover the genetic basis of these traits. Association summary statistics from genome-wide meta-analyses are available for hundreds of traits. Updating these to ever-increasing reference panels is very cumbersome as it requires reimputation of the genetic data, rerunning the associati...

The Impact of Missing Values and Single Imputation upon Rasch Analysis Outcomes: A Simulation Study.

Imputation becomes common practice through availability of easy-to-use algorithms and software. This study aims to determine if different imputation strategies are robust to the extent and type of missingness, local item dependencies (LID), differential item functioning (DIF), and misfit when doing a Rasch analysis. Four samples were simulated and represented a sample with good metric properties, a sample with LID, a sample with DIF, and a sample with LID and DIF. Missing values were generated with increasi...

Methods for Handling Left-Censored Data in Quantitative Microbial Risk Assessment.

Data below detection limits, left-censored data, are common in environmental microbiology, and decisions in handling censored data may have implications for quantitative microbial risk assessment (QMRA). In this paper, we utilize simulated data sets informed by real-world enterovirus water data to evaluate methods for handling left-censored data. Data sets were simulated with four censoring degrees (low (10%), medium (35%), high (65%), and severe (90%)) and one real life censoring example (97%) and were inf...

Foetal ultrasound measurement imputations based on growth curves versus multiple imputation chained equation (MICE).

Ultrasound measures are valuable for epidemiologic studies of risk factors for growth restriction. Longitudinal measurements enable investigation of rates of change and identification of windows where growth is impacted more acutely. However, missing data can be problematic in these studies, limiting sample size, ability to characterise windows of vulnerability, and in some instances creating bias. We sought to compare a parametric linear mixed model (LMM) approach to multiple imputation in this setting wit...

Proportional hazard model estimation under dependent censoring using copulas and penalized likelihood.

This paper considers Cox proportional hazard models estimation under informative right censored data using maximum penalized likelihood, where dependence between censoring and event times are modelled by a copula function and a roughness penalty function is used to restrain the baseline hazard as a smooth function. Since the baseline hazard is nonnegative, we propose a special algorithm where each iteration involves updating regression coefficients by the Newton algorithm and baseline hazard by the multipli...

An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia.

The inference of genomic ancestry using ancestry informative markers (AIMs) can be useful for a range of studies in evolutionary genetics, biomedical research and forensic analyses. However, the determination of AIMs for highly admixed populations with complex ancestries has remained a formidable challenge. Given the immense genetic heterogeneity and unique population structure of the Indian subcontinent, here we sought to derive AIMs that would yield a cohesive and faithful understanding of South Asian gen...

Multivariate Global Sensitivity Analysis Based on Distance Components Decomposition.

In this article, a new set of multivariate global sensitivity indices based on distance components decomposition is proposed. The proposed sensitivity indices can be considered as an extension of the traditional variance-based sensitivity indices and the covariance decomposition-based sensitivity indices, and they have similar forms. The advantage of the proposed sensitivity indices is that they can measure the effects of an input variable on the whole probability distribution of multivariate model output w...

A Bayesian framework for health economic evaluation in studies with missing data.

Health economics studies with missing data are increasingly using approaches such as multiple imputation that assume that the data are "missing at random." This assumption is often questionable, as-even given the observed data-the probability that data are missing may reflect the true, unobserved outcomes, such as the patients' true health status. In these cases, methodological guidelines recommend sensitivity analyses to recognise data may be "missing not at random" (MNAR), and call for the development of ...

Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs.

Human decision making exhibits a mixture of model-based and model-free control. Recent evidence indicates that arbitration between these two modes of control ("metacontrol") is based on their relative costs and benefits. While model-based control may increase accuracy, it requires greater computational resources, so people invoke model-based control only when potential rewards exceed those of model-free control. We used a sequential decision task, while concurrently manipulating performance incentives, to a...

Considerations of multiple imputation approaches for handling missing data in clinical trials.

Missing data exist in all clinical trials and missing data issue is a very serious issue in terms of the interpretability of the trial results. There is no universally applicable solution for all missing data problems. Methods used for handling missing data issue depend on the circumstances particularly the assumptions on missing data mechanisms. In recent years, if the missing at random mechanism cannot be assumed, conservative approaches such as the control-based and returning to baseline multiple imputat...

Shedding light on the association between repetitive negative thinking and deficits in cognitive control - A meta-analysis.

Individuals who experience recurrent negative thoughts are at elevated risk for mood and anxiety disorders. It is thus essential to understand why some individuals get stuck in recurrent negative thinking (RNT), whereas others are able to disengage eventually. Theoretical models propose that individuals high in recurrent negative thinking suffer from deficits in controlling the contents of working memory. Empirical findings, however, are inconclusive. In this meta-analysis, we synthesize findings from 94 st...

Identifying others' informative intentions from movement kinematics.

Previous research has demonstrated that people can reliably distinguish between actions with different instrumental intentions on the basis of the kinematic signatures of these actions (Cavallo, Koul, Ansuini, Capozzi, & Becchio, 2016). It has also been demonstrated that different informative intentions result in distinct action kinematics (McEllin, Knoblich, & Sebanz, 2017). However, it is unknown whether people can discriminate between instrumental actions and actions performed with an informative intenti...

The feasibility of using low-density marker panels for genotype imputation and genomic prediction of crossbred dairy cattle of East Africa.

Cost-effective high-density (HD) genotypes of livestock species can be obtained by genotyping a proportion of the population using a HD panel and the remainder using a cheaper low-density panel, and then imputing the missing genotypes that are not directly assayed in the low-density panel. The efficacy of genotype imputation can largely be affected by the structure and history of the specific target population and it should be checked before incorporating imputation in routine genotyping practices. Here, we...

Building A Longitudinal Cohort From 9-1-1 to 1-Year Using Existing Data Sources, Probabilistic Linkage, and Multiple Imputation: A Validation Study.

To describe and validate construction of a population-based, longitudinal cohort of injured older adults from 911 call to 1-year follow-up using existing data sources, probabilistic linkage, and multiple imputation.

Sensitivity Analysis for Not-at-Random Missing Data in Trial-Based Cost-Effectiveness Analysis: A Tutorial.

Cost-effectiveness analyses (CEA) of randomised controlled trials are a key source of information for health care decision makers. Missing data are, however, a common issue that can seriously undermine their validity. A major concern is that the chance of data being missing may be directly linked to the unobserved value itself [missing not at random (MNAR)]. For example, patients with poorer health may be less likely to complete quality-of-life questionnaires. However, the extent to which this occurs cannot...

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed ...


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