scholarly journals Recursive Partitioning Methods for Data Imputation in the Context of Item Response Theory: A Monte Carlo Simulation

2018 ◽  
Vol 39 (1) ◽  
pp. 88-117 ◽  
Author(s):  
Julianne M. Edwards ◽  
W. Holmes Finch
Keyword(s):  
Missing Data ◽  
Item Response Theory ◽  
Multiple Imputation ◽  
Item Response ◽  
Missing Values ◽  
Recursive Partitioning ◽  
Data Imputation ◽  
Response Theory ◽  
Imputation Methods ◽  
Missing Responses

AbstractMissing data is a common problem faced by psychometricians and measurement professionals. To address this issue, there are a number of techniques that have been proposed to handle missing data regarding Item Response Theory. These methods include several types of data imputation methods - corrected item mean substitution imputation, response function imputation, multiple imputation, and the EM algorithm, as well as approaches that do not rely on the imputation of missing values - treating the item as not presented, coding missing responses as incorrect, or as fractionally correct. Of these methods, even though multiple imputation has demonstrated the best performance in prior research, higher MAE was still present. Given this higher model parameter estimation MAE for even the best performing missing data methods, this simulation study’s goal was to explore the performance of a set of potentially promising data imputation methods based on recursive partitioning. Results of this study demonstrated that approaches that combine multivariate imputation by chained equations and recursive partitioning algorithms yield data with relatively low estimation MAE for both item difficulty and item discrimination. Implications of these findings are discussed.

scholarly journals Comparison of Selected Multiple Imputation Methods for Continuous Variables – Preliminary Simulation Study Results

2019 ◽  
Vol 6 (339) ◽  
pp. 73-98
Author(s):  
Małgorzata Aleksandra Misztal
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Imputation Accuracy ◽  
Imputation Method ◽  
Data Sets ◽  
Continuous Variables ◽  
Imputation Methods ◽  
Study Results ◽  
Almost All

The problem of incomplete data and its implications for drawing valid conclusions from statistical analyses is not related to any particular scientific domain, it arises in economics, sociology, education, behavioural sciences or medicine. Almost all standard statistical methods presume that every object has information on every variable to be included in the analysis and the typical approach to missing data is simply to delete them. However, this leads to ineffective and biased analysis results and is not recommended in the literature. The state of the art technique for handling missing data is multiple imputation. In the paper, some selected multiple imputation methods were taken into account. Special attention was paid to using principal components analysis (PCA) as an imputation method. The goal of the study was to assess the quality of PCA‑based imputations as compared to two other multiple imputation techniques: multivariate imputation by chained equations (MICE) and missForest. The comparison was made by artificially simulating different proportions (10–50%) and mechanisms of missing data using 10 complete data sets from the UCI repository of machine learning databases. Then, missing values were imputed with the use of MICE, missForest and the PCA‑based method (MIPCA). The normalised root mean square error (NRMSE) was calculated as a measure of imputation accuracy. On the basis of the conducted analyses, missForest can be recommended as a multiple imputation method providing the lowest rates of imputation errors for all types of missingness. PCA‑based imputation does not perform well in terms of accuracy.

Multiple Imputation for Missing Values in Homicide Incident Data: An Evaluation Using Unique Test Data

2018 ◽  
Vol 22 (4) ◽  
pp. 391-409
Author(s):  
John M. Roberts ◽  
Aki Roberts ◽  
Tim Wadsworth
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Actual Data ◽  
Regression Coefficients ◽  
Similar Data ◽  
Missing Information ◽  
Imputation Methods ◽  
Unique Data ◽  
Incident Reports

Incident-level homicide datasets such as the Supplementary Homicide Reports (SHR) commonly exhibit missing data. We evaluated multiple imputation methods (that produce multiple completed datasets, across which imputed values may vary) via unique data that included actual values, from police agency incident reports, of seemingly missing SHR data. This permitted evaluation under a real, not assumed or simulated, missing data mechanism. We compared analytic results based on multiply imputed and actual data; multiple imputation rather successfully recovered victim–offender relationship distributions and regression coefficients that hold in the actual data. Results are encouraging for users of multiple imputation, though it is still important to minimize the extent of missing information in SHR and similar data.

scholarly journals Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

2018 ◽  
Author(s):  
Kieu Trinh Do ◽  
Simone Wahl ◽  
Johannes Raffler ◽  
Sophie Molnos ◽  
Michael Laimighofer ◽  
...  
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Statistical Power ◽  
Missing Values ◽  
Biological Evaluation ◽  
List Type ◽  
Robust Performance ◽  
Metabolomics Data ◽  
Imputation Methods ◽  
Biochemical Pathways

AbstractBACKGROUNDUntargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in epidemiological studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation.METHODSWe investigated patterns of missing data in an MS-based metabolomics experiment of serum samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approaches to real metabolomics data. We examined the ability of each method to reconstruct biochemical pathways from data-driven correlation networks, and the ability of the method to increase statistical power while preserving the strength of established genetically metabolic quantitative trait loci.RESULTSRun day-dependent LOD-based missing data accounts for most missing values in the metabolomics dataset. Although multiple imputation by chained equations (MICE) performed well in many scenarios, it is computationally and statistically challenging. K-nearest neighbors (KNN) imputation on observations with variable pre-selection showed robust performance across all evaluation schemes and is computationally more tractable.CONCLUSIONMissing data in untargeted MS-based metabolomics data occur for various reasons. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection since it showed robust results in all evaluation schemes.Key messagesUntargeted MS-based metabolomics data show missing values due to both batch-specific LOD-based and non-LOD-based effects.Statistical evaluation of multiple imputation methods was conducted on both simulated and real datasets.Biological evaluation on real data assessed the ability of imputation methods to preserve statistical inference of biochemical pathways and correctly estimate effects of genetic variants on metabolite levels.KNN-based imputation on observations with variable pre-selection and K = 10 showed robust performance for all data scenarios across all evaluation schemes.

scholarly journals Comparison of Performance of Recursive Partitioning Models and Parametric Imputation Models Based On Predictive Mean Matching In Multiple Imputation By Chained Equations, in Order to Impute The Missing Values of The Binary Outcome, in The Presence of An Interaction Effect

2021 ◽  
Author(s):  
Sara Javadi ◽  
Abbas Bahrampour ◽  
Mohammad Mehdi Saber ◽  
Mohammad Reza Baneshi
Keyword(s):  
Multiple Imputation ◽  
Interaction Effect ◽  
Coverage Probability ◽  
Missing Values ◽  
Recursive Partitioning ◽  
Data Generation ◽  
Imputation Model ◽  
Imputation Methods ◽  
Partitioning Methods ◽  
Predictive Mean Matching

Abstract Background: Among the new multiple imputation methods, Multiple Imputation by Chained ‎Equations (MICE) is a ‎popular ‎approach for implementing multiple imputations because of its ‎flexibility. Our main focus in this study ‎is to ‎compare the performance of parametric ‎imputation models based on predictive mean matching and ‎recursive partitioning methods ‎in multiple imputation by chained equations in the ‎presence of interaction in the ‎data.Methods: We compared the performance of parametric and tree-based imputation methods via simulation using two data generation models. For each combination of data generation model and imputation method, the following steps were performed: data generation, removal of observations, imputation, logistic regression analysis, and calculation of bias, Coverage Probability (CP), and Confidence Interval (CI) width for each coefficient Furthermore, model-based and empirical SE, and estimated proportion of the variance attributable to the missing data (λ) were calculated.Results: ‎We have shown by simulation that to impute a binary response in ‎observations involving an ‎interaction, manually interring the interaction term into the imputation model in the ‎predictive mean matching ‎model improves the performance of the PMM method compared to the recursive partitioning models in ‎ ‎multiple imputation by chained equations.‎ The parametric method in which we entered the interaction model into the imputation model (MICE-‎‎‎Interaction) led to smaller bias, slightly higher coverage probability for the interaction effect, but it ‎had ‎slightly ‎wider confidence intervals than tree-based imputation (especially classification and ‎regression ‎trees). Conclusions: The application of MICE-Interaction led to better performance than ‎recursive ‎partitioning methods in MICE, although ‎the user is interested in estimating the interaction and does not ‎know ‎enough about the structure of the observations, recursive partitioning methods can be ‎suggested to impute ‎the ‎missing values.

scholarly journals Characterizing and Managing Missing Structured Data in Electronic Health Records

2017 ◽  
Author(s):  
Brett K. Beaulieu-Jones ◽  
Daniel R. Lavage ◽  
John W. Snyder ◽  
Jason H. Moore ◽  
Sarah A Pendergrass ◽  
...  
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Structured Data ◽  
Health Record ◽  
Data Types ◽  
Health Records ◽  
Imputation Methods ◽  
Electronic Health ◽  
Evaluation Of Methods

ABSTRACTMissing data is a challenge for all studies; however, this is especially true for electronic health record (EHR) based analyses. Failure to appropriately consider missing data can lead to biased results. Here, we provide detailed procedures for when and how to conduct imputation of EHR data. We demonstrate how the mechanism of missingness can be assessed, evaluate the performance of a variety of imputation methods, and describe some of the most frequent problems that can be encountered. We analyzed clinical lab measures from 602,366 patients in the Geisinger Health System EHR. Using these data, we constructed a representative set of complete cases and assessed the performance of 12 different imputation methods for missing data that was simulated based on 4 mechanisms of missingness. Our results show that several methods including variations of Multivariate Imputation by Chained Equations (MICE) and softImpute consistently imputed missing values with low error; however, only a subset of the MICE methods were suitable for multiple imputation. The analyses described provide an outline of considerations for dealing with missing EHR data, steps that researchers can perform to characterize missingness within their own data, and an evaluation of methods that can be applied to impute clinical data. While the performance of methods may vary between datasets, the process we describe can be generalized to the majority of structured data types that exist in EHRs and all of our methods and code are publicly available.

scholarly journals Controlled Sampling Approach in Improving Multiple Imputation for Missing Seasonal Rainfall Data

2021 ◽  
Author(s):  
Siti Nur Zahrah Amin Burhanuddin ◽  
Sayang Mohd Deni ◽  
Norshahida Shaadan
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
Missing Values ◽  
Rainfall Data ◽  
Original Structure ◽  
Seasonal Patterns ◽  
Block Bootstrap ◽  
Data Imputation ◽  
Bootstrap Approach ◽  
Sampling Approach

Abstract Missingness in rainfall data is one of the well-known and challenging issues faced by meteorologists and researchers from all over the world. The problem would affect the quality of the data which is very important in representing the actual meteorological characteristics of a particular location. Therefore, the missing data should be properly treated in order to provide good quality dataset for the public domain. In furtherance of ensuring the accuracy of imputed missing data, the original structure of the rainfall data series must be specifically preserved when the data are having seasonal patterns. Most of the environmental datasets are generally characterized by outliers and seasonal patterns. These characteristics have certainly affected the performance of missing data imputation methods. The problem of missing data can be treated, but a specific structured approach must be employed when involving dataset that contains outliers and seasonal patterns. This study has highlighted and discussed the structured and comprehensive procedures on how to tackle the problem of missing data by emphasizing on controlled sampling approach for their implementation. The missing values were estimated by using multiple imputation based on block bootstrap approach associated with normal ratio methods compared to the conventional sampling (i.e. general bootstrap approach). The analysis and experimentation are illustrated using several datasets obtained for several locations in Peninsular Malaysia. The block bootstrap approach has revealed its advantage of preserving time series structure in its process and successfully improved the estimates of missing rainfall data imputation.

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