Search results for " Imputation"
showing 10 items of 23 documents
Online Edge Flow Imputation on Networks
2022
Author's accepted manuscript © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respe…
Missing value imputation in proximity extension assay-based targeted proteomics data
2020
Targeted proteomics utilizing antibody-based proximity extension assays provides sensitive and highly specific quantifications of plasma protein levels. Multivariate analysis of this data is hampered by frequent missing values (random or left censored), calling for imputation approaches. While appropriate missing-value imputation methods exist, benchmarks of their performance in targeted proteomics data are lacking. Here, we assessed the performance of two methods for imputation of values missing completely at random, the previously top-benchmarked ‘missForest’ and the recently published ‘GSimp’ method. Evaluation was accomplished by comparing imputed with remeasured relative concentrations…
Identification of patterns og change on mongitudinal data, illustrated by two exemples : study of hospital pathways in the management of cancer. Cons…
2014
Context In healthcare domain, data mining for knowledge discovery represent a growing issue. Questions about the organisation of healthcare system and the study of the relation between treatment and quality of life (QoL) perceived could be addressed that way. The evolution of technologies provides us with efficient data mining tools and statistical packages containing advanced methods available for non-experts. We illustrate this approach through two issues: 1 / What organisation of healthcare system for cancer diseases management? 2 / Exploring in patients suffering from metastatic cancer, the relationship between health-related QoL perceived and treatment received as part of a clinical tr…
Selection bias was reduced by recontacting nonparticipants
2016
Objective One of the main goals of health examination surveys is to provide unbiased estimates of health indicators at the population level. We demonstrate how multiple imputation methods may help to reduce the selection bias if partial data on some nonparticipants are collected. Study Design and Setting In the FINRISK 2007 study, a population-based health study conducted in Finland, a random sample of 10,000 men and women aged 25–74 years were invited to participate. The study included a questionnaire data collection and a health examination. A total of 6,255 individuals participated in the study. Out of 3,745 nonparticipants, 473 returned a simplified questionnaire after a recontact. Both…
Weights and Imputations in SHARE Wave 8
2022
In this chapter, we first use the different patterns of participation to define three subsamples of primary interest for the analysis of the SHARE data collected in Wave 8: CAPI, CATI and CAPI & CATI. We then describe the procedure used to construct calibrated cross-sectional and longitudinal weights for handling, respectively, problems of unit non-response and attrition in the CAPI subsample. Afterwards, we describe the model used to obtain multiple imputations of the missing values due to item non-response in the CAPI data.
Item nonresponse and imputation strategies in SHARE Wave 5
2015
This chapter focuses on item nonresponse in the fifth wave of SHARE and the imputation strategies adopted to fill-in the missing values.
WEIGHTS AND IMPUTATIONS
2019
This chapter provides a description of the weighting and imputation strategies used to address problems of unit nonresponse, sample attrition and item nonresponse in the seventh wave of SHARE.
A Generalized Missing-Indicator Approach to Regression with Imputed Covariates
2011
We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the missing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized miss…
Air quality and integration of short-term and long-term pollutant data
2008
Modelling PM10 is an important problem in statistical methodology, above all to explain the PM10 behaviour in space and time, since it has been linked to many adverse effects on human and environmental health. But the large spatial variability of the main traffic-related pollutants, and in particular here the PM10, implies the impossibility of obtaining from the data of the fixed stations a complete pictures of the atmospheric pollution in the urban areas. Information from fixed monitoring stations (long-term measurements) are therefore integrated with the ones deriving from mobile station (short-term measurements). Short-term measurements are incomplete and so it is necessary to integrate …
Bayesian models for data missing not at random in health examination surveys
2018
In epidemiological surveys, data missing not at random (MNAR) due to survey nonresponse may potentially lead to a bias in the risk factor estimates. We propose an approach based on Bayesian data augmentation and survival modelling to reduce the nonresponse bias. The approach requires additional information based on follow-up data. We present a case study of smoking prevalence using FINRISK data collected between 1972 and 2007 with a follow-up to the end of 2012 and compare it to other commonly applied missing at random (MAR) imputation approaches. A simulation experiment is carried out to study the validity of the approaches. Our approach appears to reduce the nonresponse bias substantially…