Imputed Sets Research Articles

TAVI PACER - a contemporary risk score for pacemaker implantation after TAVI

Abstract Background The need for permanent pacemaker implantation (PPM) remains one of the most frequent complications of transcatheter aortic valve implantation (TAVI). The aim of this study was to develop a novel risk score for the prediction of PPM after TAVI using contemporary transcatheter heart valves. Purpose The TAVI PACER score is designed to optimize valve selection and implantation technique based on individual patient factors to reduce the need for PPM. Methods Between January 2019 and December 2020, 1039 patients underwent TAVI at Charité Universitätsmedizin Berlin. Patients with prior pacemaker implantation or valve-in-valve procedure were excluded. We retrospectively evaluated clinical, electrocardiographic, echocardiographic, computed tomographic, and periprocedural data and applied logistic regression for predicting pacemaker implantation. For variable selection, we ran regression with a Lasso estimator for optimizing the predictive quality of the selected variables. Additionally, we used multiple imputation with 30 imputed sets to deal with missing data. Results We included 840 patients (mean age 80.2 ± 9.1 years, 50.5% female), of whom 140 patients (16.6%) needed pacemaker implantation within 30 days post TAVI. We identified 11 relevant risk factors that were included in the final score. In a first step the TAVI PACER score calculates an individual risk for pacemaker implantation using preprocedural characteristics such as grade of mitral insufficiency, nicotine use, preexisting AV Block I° and RBBB, EF, LVEDD and BMI. In a second step the influence of intraprocedural aspects are added to illustrate the extent to which the risk for pacemaker implantation can change, depending on which type of valve, implantation depth and valve perimeter are chosen. Pacemaker implantation can be predicted with a sensitivity of 60% and specificity of 82%. Conclusion The TAVI PACER Score is an intuitive and useful tool to predict the risk for pacemaker implantation after TAVI before the intervention and could thus help to reduce the rate of pacemakers implanted.

Creating a Multiply Imputed Value Set for the EQ-5D-5L in Canada: State-Level Misspecification Terms Are Needed to Characterize Parameter Uncertainty Correctly.

Parameter uncertainty in EQ-5D-5L value sets often exceeds the instrument's minimum important difference, yet this is routinely ignored. Multiple imputation (MI) accounts for parameter uncertainty in the value set; however, no valuation study has implemented this methodology. Our objective was to create a Canadian MI value set for the EQ-5D-5L, thus enabling users to account for parameter uncertainty in the value set. Using the Canadian EQ-5D-5L valuation study (N = 1,073), we first refit the original model followed by models with state-level misspecification. Models were compared based on the adequacy of 95% credible interval (CrI) coverage for out-of-sample predictions. Using the best-fitting model, we took 100 draws from the posterior distribution to create 100 imputed value sets. We examined how much the standard error of the estimated mean health utilities increased after accounting for parameter uncertainty in the value set by using the MI and original value sets to score 2 data sets: 1) a sample of 1,208 individuals from the Canadian general public and 2) a sample of 401 women with breast cancer. The selected model with state-level misspecification outperformed the original model (95% CrI coverage: 94.2% v. 11.6%). We observed wider standard errors for the estimated mean utilities on using the MI value set for both the Canadian general public (MI: 0.0091; original: 0.0035) and patients with breast cancer (MI: 0.0169; original: 0.0066). We provide 1) the first MI value sets for the EQ-5D-5L and 2) code to construct MI value sets while accounting for state-level model misspecification. Our study suggests that ignoring parameter uncertainty in value sets leads to falsely narrow SEs. Value sets for health state utility instruments are estimated subject to parameter uncertainty; this parameter uncertainty may exceed the minimum important difference of the instrument, yet it is not fully captured using current methods.This study creates the first multiply imputed value set for a multiattribute utility instrument, the EQ-5D-5L, to fully capture this parameter uncertainty.We apply the multiply imputed value set to 2 data sets from 1) the Canadian general public and 2) women with invasive breast cancer.Scoring the EQ-5D-5L using a multiply imputed value set led to wider standard error estimates, suggesting that the current practice of ignoring parameter uncertainty in the value set leads to falsely low standard errors.Our work will be of interest to methodologists and developers of the EQ-5D-5L and users of the EQ-5D-5L, such as health economists, researchers, and policy makers.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data.

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

An algorithm to compute the average-of-awards rule for claims problems with an application to the allocation of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} emissions

The set of awards vectors for a claims problem coincides with the core of the associated coalitional game. We analyze the structure of this set by defining for each group of claimants a, so called, utopia game, whose core comprises the most advantageous imputations available for the group. We show that, given a claims problem, the imputation set of the associated coalitional game can be partitioned by the cores of the utopia games. A rule selects for each claims problem a unique allocation from the set of awards vectors. The average-of-awards rule associates to each claims problem the geometric center of the corresponding set of awards vectors. Based on the decomposition of the imputation set, we obtain an interpretation of the average-of-awards rule as a point of fairness between stable and utopia imputations and provide a backward recurrence algorithm to compute it. To illustrate our analysis, we present an application to the distribution of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_2$$\\end{document} emissions.

S9 Development of a Clinical Screening Tool for Exocrine Pancreatic Insufficiency in Patients With Chronic Pancreatitis

Introduction: No accurate diagnostic tests exist for exocrine pancreatic insufficiency (EPI). As a result, EPI is misdiagnosed, including in chronic pancreatitis (CP), and there is an unmet need for clinical tools to help clinicians evaluate patients with CP at risk for EPI. The aim of this study was to generate a simple clinical tool to predict EPI in patients with CP. Methods: 49 variables from medical records of CP patients with or without EPI were entered into a Classification and Regression Tree (CART) model using the unimputed full analysis set and 4 other models (Logistic Regression with Least Absolute Shrinkage and Selection Operator regularization, Support Vector Machine, Random Forest, and Gradient Boosting Machine) using imputed full analysis set. EPI misclassification rate (mRate) served as primary metric to train the 5 prediction models. Each model’s generalizability was assessed using 5-fold cross-validation and the model that was best suited for clinician use based on mRate and other characteristics was selected. Results: Records of 274 patients with CP from 6 pancreatitis centers across the US were included. Among the 160 (58%)/114 (42%) patients with EPI/without EPI, 48%/55% were female, mean age was 55/52 years, 76%/69% were white, 29%/52% were never smokers, and 39%/53% were never alcohol users. All 5 prediction models demonstrated similar EPI mRates. The CART model was selected. The final CART decision tree includes 10 variables (in order of importance): smoking, pancreatic atrophy, steatorrhea, alcohol use, hemoglobin, serum vitamin D, pancreatic duct contour change, serum albumin, pancreatic parenchymal calcifications, and use of opioid analgesics. The mRate without and with 5-fold cross-validation of the CART was 0.153 (training error) and 0.314 (prediction error), and the area under the receiver operating characteristic curve was 0.889 and 0.682, respectively (Figure 1). Sensitivity/specificity without/with 5-fold cross validation was 0.888/0.789 and 0.794/0.535, respectively. Conclusion: A parsimonious set of 10 variables associated with EPI in CP was identified. The CART decision tree demonstrated good prediction performance for EPI and was selected as the most practical screening tool to aid clinicians in the assessment of EPI in patients with CP. Further external validation of the EPI screening tool is planned.Figure 1.: AUC-ROC of the CART model trained on the full analysis set and after 5-fold cross-validation. AUC, area under the curve; CART, classification and regression tree; CV, cross-validation; ROC, receiver operating characteristic.

Individual weighted excess and least square values

This work deals with the weighted excesses of players in cooperative games which are obtained by summing up all the weighted excesses of all coalitions to which they belong. We first show that the resulting payoff vector is the corresponding least square value by lexicographically minimizing the individual weighted excesses of players over the preimputation set, and thus give an alternative characterization of the least square values. Second, we show that these results give rise to lower and upper bounds for the core payoff vectors and, using these bounds, we show that the least square values can be seen as the center of a polyhedron defined by these bounds. This provides a second new characterization of the least square values. Third, we show that the individually rational least square value is the solution that lexicographically minimizes the individual weighted excesses of players over the imputation set.

Development of a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical variables

IntroductionWe aimed to develop a prediction model for foot ulcer recurrence in people with diabetes using easy-to-obtain clinical variables and to validate its predictive performance in order to help risk...

IDP-Core: Novel Cooperative Solution for Differential Games

IDP-core is a new cooperative solution for dynamic and differential games. A novel approach of constructing solutions for dynamic and differential games was employed in which the time consistency property was used as the main axiom property for the cooperative solution. Another new and important approach used for constructing IDP-core is the IDP dominance, which allows to select undominated imputation distribution procedures and construct the cooperative solution or imputation set. This approach shows the potential of using the time consistency property as the main axiom for solutions in various fields such as Social Choice and Mechanism Design. The overall procedure for defining the cooperative solution is also new since IDP-core was constructed using imputation distribution procedures but not by using imputations directly.

Development of a multivariable prediction model for plantar foot ulcer recurrence in high-risk people with diabetes

IntroductionForty per cent of people with diabetes who heal from a foot ulcer recur within 1 year. The aim was to develop a prediction model for plantar foot ulcer recurrence...

Looking forward approach with random horizon in cooperative differential games

In the paper authors present a new approach to determination and computation of a solution for differential games with prescribed duration in the case when players lack certain information about the dynamical system and payoff function on the whole time interval on which the game is played. At each time instant players receive information about dynamical system and payoff functions, however the duration of the period of this information is unknown and can be represented as a random variable with known parameters. At certain periods of time the information is updated. A novel solution is described as a combination of imputation sets in the truncated subgames that are analyzed using Looking Forward Approach with random horizon. A resource extraction game serves as an illustration in order to compare a cooperative trajectory, imputations, and imputation distribution procedure in the game with Looking Forward Approach and in the original game with prescribed duration. Looking Forward Approach is used for constructing game theoretical models and defining solutions for conflict-controlled processes where information about the process updates dynamically.

Missing tumor measurement (TM) data in the search for alternative TM-based endpoints in cancer clinical trials

PurposeMissing data commonly occur in cancer clinical trials (CCT) and may hinder the search for alternative trial endpoints. We consider reasons for missing tumor measurement (TM) data in CCT and how missing TM data are typically handled. We explore the potential impact of missing TM data on predictive ability of a set of TM-based endpoints. MethodsLiterature review identifies reasons for and approaches to handling missing TM data. Data from 3 actual clinical trials were used for illustration. A sensitivity analysis of the potential impact of missing TM data was performed by comparing overall survival (OS) predictive ability of alternative endpoints using observed and imputed data. ResultsReasons for missing TM data in CCT are presented, based on the literature review and the three trials. Although missing TM data impacted individual objective status (e.g. 12-week status changed for 53% of patients in one imputation set), it surprisingly only minimally impacted endpoint predictive ability (e.g. median c-indices of 500 imputed datasets ranged from 0.566 to 0.570 for N9741, 0.592–0.616 for N9841, and 0.542–0.624 for N0026). ConclusionBy understanding the reasons for missingness, we can better anticipate them and minimize their occurrence. Our preliminary analysis suggests missing TM data may not impact endpoint predictive ability, but could impact objective response status classification; however these findings require further validation. With response status accepted as an important phase II endpoint in the development of new cancer therapies (including immunotherapy), we urge that in CCT complete TM data collection and adherence to protocol-defined disease evaluation as closely as possible be a priority.

Informatics tools to assess the success of procedural harmonization in preclinical multicenter biomarker discovery study on post-traumatic epileptogenesis

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a National Institutes for Neurological Diseases and Stoke funded Centers-Without-Walls international multidisciplinary study aimed at preventing epileptogenesis. The preclinical biomarker discovery in EpiBios4Rx applies a multicenter study design to allow the number of animals that are required for adequate statistical power for the analysis to be studied in an efficient manner. Further, the use of multiple centers mimics the clinical trial situation, and therefore potentially the chance of successful clinical translation of the outcomes of the study. Its successful implementation requires harmonization of procedures and data analyses between the three contributing centers in Finland, Australia, and USA. The objective of the present analysis was to develop metrics for analysis of the success of harmonization of procedures to guide further data analyses and plan the future multicenter preclinical studies. The interim analysis of data is based on the analysis of data from 212 rats with lateral fluid-percussion injury or sham-operation included in the biomarker discovery by April 30, 2018. The details of protocols, including production of injury, post-injury follow-up, blood sampling, electroencephalogram recording, and magnetic resonance imaging have been presented in the accompanying manuscripts in this Supplement. Implementation of protocols in EpiBios4Rx project participant centers was visualized in 2D using t-distributed stochastic neighborhood embedding (t-SNE). The protocols applied to each rat were presented as feature vectors of procedure related variables (e.g., impact pressure, anesthesia time). The total number of protocol features linked to each rat was 112. The missing data was accounted in visualization by utilizing imputation and adding the number of missing values as a third dimension to 2D t-SNE plot, resulting in a 3D overview of protocol data. Intraclass correlation coefficient (ICC) using Euclidean distances and area under receiver operating characteristic curve (AUC) of k-nearest neighbor classifier (KNN) were utilized to quantify the degree of clustering by center. Both subsets of data with incomplete protocol vectors omitted and missing protocol data imputed were assessed. Our data show that a visible clustering by center was observed in all t-SNE plots, except for day 7 neuroscores. Both ICC and AUC indicated clustering by center in all protocol variable subsets, excluding unimputed day 7 neuroscores (ICC 0.04 and AUC 0.6). ICC for imputed set of all protocol related variables was 0.1 and KNN AUC 0.92. In conclusion, both ICC and AUC indicated differences in protocol between EpiBios4Rx participating centers, which needs to be taken into account in data analysis. Importantly, the majority of observed differences are recoverable as they relate to insufficient updates in record keeping. While AUC score of KNN is a more sensitive measure for protocol harmonization than ICC for data that displays complex splintered clustering, ICC and AUC provide complementary measures to assess the degree of procedural harmonization. This experience should be helpful for other groups planning such multicenter post-traumatic epileptogenesis studies in the future.

The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.

The Electronic Medical Records and Genomics (eMERGE) network is a network of medical centers with electronic medical records linked to existing biorepository samples for genomic discovery and genomic medicine research. The network sought to unify the genetic results from 78 Illumina and Affymetrix genotype array batches from 12 contributing medical centers for joint association analysis of 83,717 human participants. In this report, we describe the imputation of eMERGE results and methods to create the unified imputed merged set of genome‐wide variant genotype data. We imputed the data using the Michigan Imputation Server, which provides a missing single‐nucleotide variant genotype imputation service using the minimac3 imputation algorithm with the Haplotype Reference Consortium genotype reference set. We describe the quality control and filtering steps used in the generation of this data set and suggest generalizable quality thresholds for imputation and phenotype association studies. To test the merged imputed genotype set, we replicated a previously reported chromosome 6 HLA‐B herpes zoster (shingles) association and discovered a novel zoster‐associated loci in an epigenetic binding site near the terminus of chromosome 3 (3p29).

GIGSEA: genotype imputed gene set enrichment analysis using GWAS summary level data.

level data of GWAS becomes increasingly important in post-GWAS data mining. Here, we present GIGSEA (Genotype Imputed Gene Set Enrichment Analysis), a novel method that uses GWAS summary statistics and eQTL to infer differential gene expression and interrogate gene set enrichment for the trait-associated SNPs. By incorporating empirical eQTL of the disease relevant tissue, GIGSEA naturally accounts for factors such as gene size, gene boundary, SNP distal regulation and multiple-marker regulation. The weighted linear regression model was used to perform the enrichment test, properly adjusting for imputation accuracy, model incompleteness and redundancy in different gene sets. The significance level of enrichment is assessed by the permutation test, where matrix operation was employed to dramatically improve computation speed. GIGSEA has appropriate type I error rates, and discovers the plausible biological findings on the real data set. GIGSEA is implemented in R, and freely available at www.github.com/zhushijia/GIGSEA. Supplementary data are available at Bioinformatics online.

Characterizing the effects of missing data and evaluating imputation methods for chemical prioritization applications using ToxPi

BackgroundThe Toxicological Priority Index (ToxPi) is a method for prioritization and profiling of chemicals that integrates data from diverse sources. However, individual data sources (“assays”), such as in vitro bioassays or in vivo study endpoints, often feature sections of missing data, wherein subsets of chemicals have not been tested in all assays. In order to investigate the effects of missing data and recommend solutions, we designed simulation studies around high-throughput screening data generated by the ToxCast and Tox21 programs on chemicals highlighted by the Agency for Toxic Substances and Disease Registry’s (ATSDR) Substance Priority List (SPL), which helps prioritize environmental research and remediation resources.ResultsOur simulations explored a wide range of scenarios concerning data (0-80% assay data missing per chemical), modeling (ToxPi models containing from 160-700 different assays), and imputation method (k-Nearest-Neighbor, Max, Mean, Min, Binomial, Local Least Squares, and Singular Value Decomposition). We find that most imputation methods result in significant changes to ToxPi score, except for datasets with a small number of assays. If we consider rank change conditional on these significant changes to ToxPi score, we find that ranks of chemicals in the minimum value imputation, SVD imputation, and kNN imputation sets are more sensitive to the score changes.ConclusionsWe found that the choice of imputation strategy exerted significant influence over both scores and associated ranks, and the most sensitive scenarios were those involving fewer assays plus higher proportions of missing data. By characterizing the effects of missing data and the relative benefit of imputation approaches across real-world data scenarios, we can augment confidence in the robustness of decisions regarding the health and ecological effects of environmental chemicals

Fixed-point approaches to the proof of the Bondareva–Shapley Theorem

We provide two new proofs of the Bondareva–Shapley theorem, which states that the core of a transferable utility cooperative game has a nonempty core if and only if the game is balanced. Both proofs exploit the fixed points of self-maps of the set of imputations, applying elementary existence arguments typically associated with noncooperative games to cooperative games.

The Family of Ideal Values for Cooperative Games

In view of the nature of pursuing profit, a selfish coefficient function is employed to describe the degrees of selfishness of players in different coalitions, which is the desired rate of return to the worth of coalitions. This function brings in the concept of individual expected reward to every player. Built on different selfish coefficient functions, the family of ideal values can be obtained by minimizing deviations from the individual expected rewards. Then, we show the relationships between the family of ideal values and two other classical families of values: the procedural values and the least square values. For any selfish coefficient function, the corresponding ideal value is characterized by efficiency, linearity, an equal-expectation player property and a nullifying player punishment property, and also interpreted by a dynamic process. As two dual cases in the family of ideal values, the center of gravity of imputation set value and the equal allocation of nonseparable costs value are raised from new axiomatic angles.

Stackelberg Oligopoly TU-Games: Characterization and Nonemptiness of the Core

In this paper, we consider the dynamic setting of Stackelberg oligopoly TU-games in [Formula: see text]-characteristic function form. Any deviating coalition produces an output at a first period as a leader and then, outsiders simultaneously and independently play a quantity at a second period as followers. We assume that the inverse demand function is linear and that firms operate at constant but possibly distinct marginal costs. First, we show that the core of any Stackelberg oligopoly TU-game always coincides with the set of imputations. Second, we provide a necessary and sufficient condition, depending on the heterogeneity of firms’ marginal costs, under which the core is nonempty.

The Integer Nucleolus of Directed Simple Games: A Characterization and an Algorithm

We study the class of directed simple games, assuming that only integer solutions are admitted; i.e., the players share a resource that comes in discrete units. We show that the integer nucleolus—if nonempty—of such a game is composed of the images of a particular payoff vector under all symmetries of the game. This payoff vector belongs to the set of integer imputations that weakly preserve the desirability relation between the players. We propose an algorithm for finding the integer nucleolus of any directed simple game with a nonempty integer imputation set. The algorithm supports the parallel execution of multiple threads in a computer application. We also consider the integer prenucleolus and the class of directed generalized simple games.

Clinical remission in patients with moderate-to-severe Crohn's disease treated with filgotinib (the FITZROY study): results from a phase 2, double-blind, randomised, placebo-controlled trial

Filgotinib (GLPG0634, GS-6034) is a once-daily, orally administered, Janus kinase 1 (JAK1)-selective inhibitor. The FITZROY study examined the efficacy and safety of filgotinib for the treatment of moderate-to-severe Crohn's disease. We did a randomised, double-blind, placebo-controlled phase 2 study, which recruited patients from 52 centres in nine European countries. We enrolled eligible patients aged 18-75 years with a documented history of ileal, colonic, or ileocolonic Crohn's disease for 3 months or more before screening, as assessed by colonoscopy and supported by histology, and a Crohn's Disease Activity Index (CDAI) score during screening between 220 and 450 inclusive. Patients were randomly assigned (3:1) to receive filgotinib 200 mg once a day or placebo for 10 weeks. Patients were stratified according to previous anti-tumour necrosis factor alpha exposure, C-reactive protein concentration at screening (≤10 mg/L or >10 mg/L), and oral corticosteroid use at baseline, using an interactive web-based response system. The primary endpoint was clinical remission, defined as CDAI less than 150 at week 10. After week 10, patients were assigned based on responder status to filgotinib 100 mg once a day, filgotinib 200 mg once a day, or placebo for an observational period lasting a further 10 weeks. The filgotinib and placebo treatment groups were compared using ANCOVA models and logistic regression models containing baseline values and randomisation stratification factors as fixed effects. Analyses were done on the intention-to-treat non-responder imputation set. The trial was registered at ClinicalTrials.gov, number NCT02048618. Between Feb 3, 2014, and July 10, 2015, we enrolled 174 patients with active Crohn's disease confirmed by centrally read endoscopy (130 in the filgotinib 200 mg group and 44 in the placebo group). In the intention-to-treat population, 60 (47%) of 128 patients treated with filgotinib 200 mg achieved clinical remission at week 10 versus ten (23%) of 44 patients treated with placebo (difference 24 percentage points [95% CI 9-39], p=0·0077). In a pooled analysis of all periods of filgotinib and placebo exposure over 20 weeks, serious treatment-emergent adverse effects were reported in 14 (9%) of 152 patients treated with filgotinib and three (4%) of 67 patients treated with placebo. Filgotinib induced clinical remission in significantly more patients with active Crohn's disease compared with placebo, and had an acceptable safety profile. Galapagos.