Missing data is an inevitable aspect of every empirical research. Researchers developed several techniques to handle missing data to avoid information loss and biases. Over the past 50 years, these methods have become more and more efficient and also more complex. Building on previous review studies, this paper aims to analyze what kind of missing data handling methods are used among various scientific disciplines. For the analysis, we used nearly 50.000 scientific articles that were published between 1999 and 2016. JSTOR provided the data in text format. Furthermore, we utilized a text-mining approach to extract the necessary information from our corpus. Our results show that the usage of advanced missing data handling methods such as Multiple Imputation or Full Information Maximum Likelihood estimation is steadily growing in the examination period. Additionally, simpler methods, like listwise and pairwise deletion, are still in widespread use.

Replication and simulation reproduction materials for the article "The MIDAS Touch: Accurate and Scalable Missing-Data Imputation with Deep Learning." Please see the README file for a summary of the contents and the Replication Guide for a more detailed description. Article abstract: Principled methods for analyzing missing values, based chiefly on multiple imputation, have become increasingly popular yet can struggle to handle the kinds of large and complex data that are also becoming common. We propose an accurate, fast, and scalable approach to multiple imputation, which we call MIDAS (Multiple Imputation with Denoising Autoencoders). MIDAS employs a class of unsupervised neural networks known as denoising autoencoders, which are designed to reduce dimensionality by corrupting and attempting to reconstruct a subset of data. We repurpose denoising autoencoders for multiple imputation by treating missing values as an additional portion of corrupted data and drawing imputations from a model trained to minimize the reconstruction error on the originally observed portion. Systematic tests on simulated as well as real social science data, together with an applied example involving a large-scale electoral survey, illustrate MIDAS's accuracy and efficiency across a range of settings. We provide open-source software for implementing MIDAS.

1. NCCTG Lung cancer datasetSurvival in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities.2.CNV measurements of CNV of GBM This dataset records the information about copy number variation of Glioblastoma (GBM).Abstract:In biology and medicine, conservative patient and data collection malpractice can lead to missing or incorrect values in patient registries, which can affect both diagnosis and prognosis. Insufficient or biased patient information significantly impedes the sensitivity and accuracy of predicting cancer survival. In bioinformatics, making a best guess of the missing values and identifying the incorrect values are collectively called “imputation”. Existing imputation methods work by establishing a model based on the data mechanism of the missing values. Existing imputation methods work well under two assumptions: 1) the data is missing completely at random, and 2) the percentage of missing values is not high. These are not cases found in biomedical datasets, such as the Cancer Genome Atlas Glioblastoma Copy-Number Variant dataset (TCGA: 108 columns), or the North Central Cancer Treatment Group Lung Cancer (NCCTG) dataset (NCCTG: 9 columns). We tested six existing imputation methods, but only two of them worked with these datasets: The Last Observation Carried Forward (LOCF) and K-nearest Algorithm (KNN). Predictive Mean Matching (PMM) and Classification and Regression Trees (CART) worked only with the NCCTG lung cancer dataset with fewer columns, except when the dataset contains 45% missing data. The quality of the imputed values using existing methods is bad because they do not meet the two assumptions.In our study, we propose a Restricted Boltzmann Machine (RBM)-based imputation method to cope with low randomness and the high percentage of the missing values. RBM is an undirected, probabilistic and parameterized two-layer neural network model, which is often used for extracting abstract information from data, especially for high-dimensional data with unknown or non-standard distributions. In our benchmarks, we applied our method to two cancer datasets: 1) NCCTG, and 2) TCGA. The running time, root mean squared error (RMSE) of the different methods were gauged. The benchmarks for the NCCTG dataset show that our method performs better than other methods when there is 5% missing data in the dataset, with 4.64 RMSE lower than the best KNN. For the TCGA dataset, our method achieved 0.78 RMSE lower than the best KNN.In addition to imputation, RBM can achieve simultaneous predictions. We compared the RBM model with four traditional prediction methods. The running time and area under the curve (AUC) were measured to evaluate the performance. Our RBM-based approach outperformed traditional methods. Specifically, the AUC was up to 19.8% higher than the multivariate logistic regression model in the NCCTG lung cancer dataset, and the AUC was higher than the Cox proportional hazard regression model, with 28.1% in the TCGA dataset.Apart from imputation and prediction, RBM models can detect outliers in one pass by allowing the reconstruction of all the inputs in the visible layer with in a single backward pass. Our results show that RBM models have achieved higher precision and recall on detecting outliers than other methods.

A common problem in clinical trials is the missing data that occurs when patients do not complete the study and drop out without further measurements. Missing data cause the usual statistical analysis of complete or all available data to be subject to bias. There are no universally applicable methods for handling missing data. We recommend the following: (1) Report reasons for dropouts and proportions for each treatment group; (2) Conduct sensitivity analyses to encompass different scenarios of assumptions and discuss consistency or discrepancy among them; (3) Pay attention to minimize the chance of dropouts at the design stage and during trial monitoring; (4) Collect post-dropout data on the primary endpoints, if at all possible; and (5) Consider the dropout event itself an important endpoint in studies with many.

When dealing with missing data in clinical trials, it is often convenient to work under simplifying assumptions, such as missing at random (MAR), and follow up with sensitivity analyses to address unverifiable missing data assumptions. One such sensitivity analysis, routinely requested by regulatory agencies, is the so-called tipping point analysis, in which the treatment effect is re-evaluated after adding a successively more extreme shift parameter to the predicted values among subjects with missing data. If the shift parameter needed to overturn the conclusion is so extreme that it is considered clinically implausible, then this indicates robustness to missing data assumptions. Tipping point analyses are frequently used in the context of continuous outcome data under multiple imputation. While simple to implement, computation can be cumbersome in the two-way setting where both comparator and active arms are shifted, essentially requiring the evaluation of a two-dimensional grid of models. We describe a computationally efficient approach to performing two-way tipping point analysis in the setting of continuous outcome data with multiple imputation. We show how geometric properties can lead to further simplification when exploring the impact of missing data. Lastly, we propose a novel extension to a multi-way setting which yields simple and general sufficient conditions for robustness to missing data assumptions.

Dataset

This dataset was created by Safacan Metin

Contents

Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

Electronic health records (EHRs) have been widely adopted in recent years, but often include a high proportion of missing data, which can create difficulties in implementing machine learning and other tools of personalized medicine. Completed datasets are preferred for a number of analysis methods, and successful imputation of missing EHR data can improve interpretation and increase our power to predict health outcomes. However, use of the most popular imputation methods mainly require scripting skills, and are implemented using various packages and syntax. Thus, the implementation of a full suite of methods is generally out of reach to all except experienced data scientists. Moreover, imputation is often considered as a separate exercise from exploratory data analysis, but should be considered as art of the data exploration process. We have created a new graphical tool, ImputEHR, that is based on a Python base and allows implementation of a range of simple and sophisticated (e.g., gradient-boosted tree-based and neural network) data imputation approaches. In addition to imputation, the tool enables data exploration for informed decision-making, as well as implementing machine learning prediction tools for response data selected by the user. Although the approach works for any missing data problem, the tool is primarily motivated by problems encountered for EHR and other biomedical data. We illustrate the tool using multiple real datasets, providing performance measures of imputation and downstream predictive analysis.

Fossil-based estimates of diversity and evolutionary dynamics mainly rely on the study of morphological variation. Unfortunately, organism remains are often altered by post-mortem taphonomic processes such as weathering or distortion. Such a loss of information often prevents quantitative multivariate description and statistically controlled comparisons of extinct species based on morphometric data. A common way to deal with missing data involves imputation methods that directly fill the missing cases with model estimates. Over the last several years, several empirically determined thresholds for the maximum acceptable proportion of missing values have been proposed in the literature, whereas other studies showed that this limit actually depends on several properties of the study dataset and of the selected imputation method, and is by no way generalizable. We evaluate the relative performances of seven multiple imputation techniques through a simulation-based analysis under three distinct patterns of missing data distribution. Overall, Fully Conditional Specification and Expectation-Maximization algorithms provide the best compromises between imputation accuracy and coverage probability. Multiple imputation (MI) techniques appear remarkably robust to the violation of basic assumptions such as the occurrence of taxonomically or anatomically biased patterns of missing data distribution, making differences in simulation results between the three patterns of missing data distribution much smaller than differences between the individual MI techniques. Based on these results, rather than proposing a new (set of) threshold value(s), we develop an approach combining the use of multiple imputations with procrustean superimposition of principal component analysis results, in order to directly visualize the effect of individual missing data imputation on an ordinated space. We provide an R function for users to implement the proposed procedure.

This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response.

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple i mputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, since these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also made it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing strategies. These developments also made it possible to implement the methods introduced here in freely available open source software, Amelia II: A Program for Missing Data, that is considerably more reliable than existing strategies. See also: Missing Data

Penalized regression methods are used in many biomedical applications for variable selection and simultaneous coefficient estimation. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors. This article considers a general class of penalized objective functions which, by construction, force selection of the same variables across imputed datasets. By pooling objective functions across imputations, optimization is then performed jointly over all imputed datasets rather than separately for each dataset. We consider two objective function formulations that exist in the literature, which we will refer to as “stacked” and “grouped” objective functions. Building on existing work, we (i) derive and implement efficient cyclic coordinate descent and majorization-minimization optimization algorithms for continuous and binary outcome data, (ii) incorporate adaptive shrinkage penalties, (iii) compare these methods through simulation, and (iv) develop an R package miselect. Simulations demonstrate that the “stacked” approaches are more computationally efficient and have better estimation and selection properties. We apply these methods to data from the University of Michigan ALS Patients Biorepository aiming to identify the association between environmental pollutants and ALS risk. Supplementary materials for this article are available online.

Missing values in proteomic data sets have real consequences on downstream data analysis and reproducibility. Although several imputation methods exist to handle missing values, no single imputation method is best suited for a diverse range of data sets, and no clear strategy exists for evaluating imputation methods for clinical DIA-MS data sets, especially at different levels of protein quantification. To navigate through the different imputation strategies available in the literature, we have established a strategy to assess imputation methods on clinical label-free DIA-MS data sets. We used three DIA-MS data sets with real missing values to evaluate eight imputation methods with multiple parameters at different levels of protein quantification: a dilution series data set, a small pilot data set, and a clinical proteomic data set comparing paired tumor and stroma tissue. We found that imputation methods based on local structures within the data, like local least-squares (LLS) and random forest (RF), worked well in our dilution series data set, whereas imputation methods based on global structures within the data, like BPCA, performed well in the other two data sets. We also found that imputation at the most basic protein quantification levelfragment levelimproved accuracy and the number of proteins quantified. With this analytical framework, we quickly and cost-effectively evaluated different imputation methods using two smaller complementary data sets to narrow down to the larger proteomic data set’s most accurate methods. This acquisition strategy allowed us to provide reproducible evidence of the accuracy of the imputation method, even in the absence of a ground truth. Overall, this study indicates that the most suitable imputation method relies on the overall structure of the data set and provides an example of an analytic framework that may assist in identifying the most appropriate imputation strategies for the differential analysis of proteins.

Dataset

This dataset was created by Deep Jani

Released under Data files © Original Authors

Contents

Dataset Details

  1.Dataset Loading:

Initially, we load the Drug Review Dataset from the UC Irvine Machine Learning Repository. This dataset contains patient reviews of different drugs, along with the medical condition being treated and the patients' satisfaction ratings.

  2.Data Preprocessing:

The dataset is preprocessed to ensure data integrity and consistency. We handle missing values and ensure that each patient ID is unique across the dataset.

  3.Text… See the full description on the dataset page: https://huggingface.co/datasets/Mouwiya/drug-reviews.

The data set corresponds the data presented in the data paper : “How to use scale invariant properties of imperviousness in urban areas to handle missing data ?“ which has been submitted to Water Resources Research ” (https://agupubs.onlinelibrary.wiley.com/journal/19447973).

It corresponds to :

- the rainfall data collected on 2019-06-02 with 5 min and 30 s time steps by a disdrometer installed on the roof of Ecole des Ponts ParisTech building.

- land use distribution for the Jouy-en-Josas catchment (1 = forest, 2= road, 3=Grass, 4=building, 5=Gully, 6=missing data), with pixel size of 10 m and 2 m.

More details can be found in the file and in the paper.

Consisting of six multi-label datasets from the UCI Machine Learning repository.

Each dataset contains missing values which have been artificially added at the following rates: 5, 10, 15, 20, 25, and 30%. The “amputation” was performed using the “Missing Completely at Random” mechanism.

File names are represented as follows:

   amp_DB_MR.arff

where:

   DB = original dataset;


   MR = missing rate.

For more details, please read:

IEEE Access article (in review process)

Replication Data for: A GMM Approach for Dealing with Missing Data on Regressors

Global health indicators such as infant and maternal mortality are important for informing priorities for health research, policy development, and resource allocation. However, due to inconsistent reporting within and across nations, construction of comparable indicators often requires extensive data imputation and complex modeling from limited observed data. We draw on Ahmed et al.’s 2012 paper – an analysis of maternal deaths averted by contraceptive use for 172 countries in 2008 – as an exemplary case of the challenge of building reliable models with scarce observations. The authors’ employ a counterfactual modeling approach using regression imputation on the independent variable which assumes no estimation uncertainty in the final model and does not address the potential for scattered missingness in the predictor variables. We replicate their results and test the sensitivity of their published estimates to the use of an alternative method for imputing missing data, multiple imputation. We also calculate alternative estimates of standard errors for the model estimates that more appropriately account for the uncertainty introduced through data imputation of multiple predictor variables. Based on our results, we discuss the risks associated with the missing data practices employed and evaluate the appropriateness of multiple imputation as an alternative for data imputation and uncertainty estimation for models of global health indicators.

This data publication provides access to (1) an archive of maps and statistics on MISR L1B2 GRP data products updated as described in Verstraete et al. (2020, https://doi.org/10.5194/essd-2019-210), (2) a user manual describing this archive, (3) a large archive of standard (unprocessed) MISR data files that can be used in conjunction with the IDL software repository published on GitHub and available from https://github.com/mmverstraete (Verstraete et al., 2019, https://doi.org/10.5281/zenodo.3519989), (4) an additional archive of maps and statistics on MISR L1B2 GRP data products updated as described for eight additional Blocks of MISR data, spanning a broader range of climatic and environmental conditions (between Iraq and Namibia), and (5) a user manual describing this second archive. The authors also make a self-contained, stand-alone version of that processing software available to all users, using the IDL Virtual Machine technology (which does not require an IDL license) from Verstraete et al., 2020: http://doi.org/10.5880/fidgeo.2020.011. (1) The compressed archive 'L1B2_Out.zip' contains all outputs produced in the course of generating the various Figures of the manuscript Verstraete et al. (2020b). Once this archive is installed and uncompressed, 9 subdirectories named Fig-fff-Ttt_Pxxx-Oyyyyyy-Bzzz are created, where fff, tt, xxx, yyyyyy and zzz stand for the Figure number, an optional Table number, Path, Orbit and Block numbers, respectively. These directories contain collections of text, graphics (maps and scatterplots) and binary data files relative to the intermediary, final and ancillary results generated while preparing those Figures. Maps and scatterplots are provided as graphics files in PNG format. Map legends are plain text files with the same names as the maps themselves, but with a file extension '.txt'. Log files are also plain text files. They are generated by the software that creates those graphics files and provide additional details on the intermediary and final results. The processing of MISR L1B2 GRP data product files requires access to cloud masks for the same geographical areas (one for each of the 9 cameras). Since those masks are themselves derived from the L1B2 GRP data and therefore also contain missing data, the outcomes from updating the RCCM data products, as described in Verstraete et al. (2020, https://doi.org/10.5194/essd-12-611-2020), are also included in this archive. The last 2 subdirectories contain the outcomes from the normal processing of the indicated data files, as well as those generated when additional missing data are artificially inserted in the input files for the purpose of assessing the performance of the algorithms. (2) The document 'L1B2_Out.pdf' provides the User Manual to install and explore the compressed archive 'L1B2_Out.zip'. (3) The compressed archive 'L1B2_input_68050.zip' contains MISR L1B2 GRP and RCCM data for the full Orbit 68050, acquired on 3 October 2012, as well as the corresponding AGP file, which is required by the processing system to update the radiance product. This archive includes data for a wide range of locations, from Russia to north-west Iran, central and eastern Iraq, Saudi Arabia, and many more countries along the eastern coast of the African continent. It is provided to allow users to analyze actual data with the software package mentioned above, without needing to download MISR data from the NASA ASDC web site. (4) The compressed archive 'L1B2_Suppl.zip' contains a set of results similar to the archive 'L1B2_Out.zip' mentioned above, for four additional sites, spanning a much wider range of geographical, climatic and ecological conditions: these are covering areas in Iraq (marsh and arid lands), Kenya (agriculture and tropical forests), South Sudan (grasslands) and Namibia (coastal desert and Atlantic Ocean). Two of them involve largely clear scenes, and the other two include clouds. The last case also includes a test to artificially introduce missing data over deep water and clouds, to demonstrate the performance of the procedure on targets other than continental areas. Once uncompressed, this new archive expands into 8 subdirectories and takes up 1.8 GB of disk space, providing access to about 2,900 files. (5) The companion user manual L1B2_Suppl.pdf, describing how to install, uncompress and explore those additional files.