Article
Health Care Sciences & Services
Lauren J. Beesley, Irina Bondarenko, Michael R. Elliot, Allison W. Kurian, Steven J. Katz, Jeremy M. G. Taylor
Summary: This paper describes how to generalize the sequential regression multiple imputation procedure to handle non-random missingness when missingness may depend on other variables. The method reduces bias in the final analysis compared to standard techniques, using approximation strategies involving inclusion of an offset in the imputation model.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2021)
Article
Mathematics
Fangfang Li, Hui Sun, Yu Gu, Ge Yu
Summary: This paper proposes a noise-aware missing data multiple imputation algorithm NPMI for static data. Different multiple imputation models are proposed according to the missing mechanism of data. The method to determine the imputation order of multivariablesmissing is given. Experiments on real and synthetic datasets verify the accuracy and efficiency of the proposed algorithm.
Article
Urology & Nephrology
Katrina Blazek, Anita van Zwieten, Valeria Saglimbene, Armando Teixeira-Pinto
Summary: Health data often have missing values, and utilizing multiple imputation techniques can help reduce bias and maintain sample size. Correct specification of the imputation model is crucial for the validity of analyses. Considerations such as missing mechanism, imputation method, and result reporting are important when conducting research with multiply imputed data.
KIDNEY INTERNATIONAL
(2021)
Article
Engineering, Biomedical
Yasser Salaheldin Mohammed, Hatem Abdelkader, Pawel Plawiak, Mohamed Hammad
Summary: This study introduces a novel method that optimizes multiple regression imputation processes by combining multiple imputations with a genetic algorithm to improve fitness values for missing patient data. Significant improvements were achieved according to the experimental results.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Feng Zhao, Yan Lu, Xinning Li, Lina Wang, Yingjie Song, Deming Fan, Caiming Zhang, Xiaobo Chen
Summary: Credit risk assessment is crucial for banks in loan approval and risk management. However, missing credit risk data can significantly reduce the effectiveness of the assessment model. In this paper, a novel method named MGAIN is proposed to accurately predict missing data through subset selection and multiple imputation strategy, improving the accuracy of the imputation model.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Information Systems
Preeti Saini, Bharti Nagpal
Summary: The study focuses on imputing missing data in the Wheat crop yield Dataset to improve crop estimation or production forecasting. Different imputation techniques are explored and evaluated for their performance. The results show that the Arithmetic Average Replacement method performs well among the statistical methods, while Miss Forest and MICE methods perform well among the Machine Learning based methods.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Multidisciplinary
Han Honggui, Sun Meiting, Wu Xiaolong, Li Fangyu
Summary: This article proposes a double-cycle weighted imputation (DCWI) method to deal with multiple missing patterns in the wastewater treatment process. The method maximizes the utilization of available information to improve imputation accuracy and experimental results show its superiority over comparison methods.
SCIENCE CHINA-TECHNOLOGICAL SCIENCES
(2022)
Article
Multidisciplinary Sciences
Hannah Voss, Simon Schlumbohm, Philip Barwikowski, Marcus Wurlitzer, Matthias Dottermusch, Philipp Neumann, Hartmut Schlueter, Julia E. Neumann, Christoph Krisp
Summary: HarmonizR is an efficient tool for missing data tolerant experimental variance reduction, which does not require data imputation and can be easily adjusted for individual dataset properties and user preferences. It demonstrated successful data harmonization for different tissue preservation techniques, LC-MS/MS instrumentation setups, and quantification approaches, and outperformed data imputation methods in detecting significant proteins.
NATURE COMMUNICATIONS
(2022)
Article
Health Care Sciences & Services
Martijn W. Heymans, Jos W. R. Twisk
Summary: Proper handling of missing data is crucial, and consideration should be given to the mechanism of missing data. Multiple imputations are highly recommended for estimating missing values. It is important to prevent missing data rather than treating them.
JOURNAL OF CLINICAL EPIDEMIOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
C. G. Marcelino, G. M. C. Leite, P. Celes, C. E. Pedreira
Summary: This paper investigates the effects and possible solutions to incomplete databases in regression and provides a systematic view of how missing data may affect regression results by analyzing actual publicly available databases. The results indicate that the impact of missing data can be significant, and the K-Nearest Neighbors method performs better in regression with missing data.
APPLIED ARTIFICIAL INTELLIGENCE
(2022)
Article
Ecology
Thomas F. Johnson, Nick J. B. Isaac, Agustin Paviolo, Manuela Gonzalez-Suarez
Summary: The study evaluated the performance of approaches for handling missing values in biased datasets and found that imputation can effectively handle missing data in some conditions but is not always the best solution. None of the tested methods could effectively deal with severe biases, highlighting the importance of rigorous data checking and proposing variables to assist researchers in detecting and minimizing errors in incomplete datasets.
GLOBAL ECOLOGY AND BIOGEOGRAPHY
(2021)
Article
Computer Science, Artificial Intelligence
Manar D. Samad, Sakib Abrar, Norou Diawara
Summary: This paper proposes methods to improve the imputation accuracy of the MICE algorithm by using ensemble learning and deep neural networks. The results of extensive analyses on multiple datasets show that the proposed methods outperform other state-of-the-art imputation algorithms, leading to better imputation accuracy and classification accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Transportation Science & Technology
Tong Nie, Guoyang Qin, Jian Sun
Summary: This paper explores an innovative method for imputing missing spatiotemporal traffic data using tensors. The proposed method utilizes a nonconvex truncated Schatten p-norm (TSpN) to approximate tensor rank and combines the alternating direction method of multipliers (ADMM) with generalized soft-thresholding (GST) to derive global optimal solutions. Experimental results show that the proposed method performs well under various missing cases, even with high rates of data loss, outperforming other state-of-the-art tensor-based imputation models in almost all scenarios.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Multidisciplinary Sciences
Anny K. G. Rodrigues, Raydonal Ospina, Marcelo R. P. Ferreira
Summary: This study proposes and evaluates a Kernel Fuzzy C-means clustering algorithm with local adaptive distances in dealing with missing data, showing better performance under the Partial Distance Strategy (PDS) and Optimal Completion Strategy (OCS) for clustering.
Article
Automation & Control Systems
Hutashan Vishal Bhagat, Manminder Singh
Summary: This article introduces a novel technique for estimating missing values, which splits the dataset into complete and incomplete subsets and sets an upper limit for each class with missing data to estimate missing values more accurately. Experimental results demonstrate the efficient estimation capability of this technique in datasets with different dimensions and missing rates.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Psychology, Clinical
Kimberly C. Thomson, Helena Romaniuk, Christopher J. Greenwood, Primrose Letcher, Elizabeth Spry, Jacqui A. Macdonald, Helena M. McAnally, George J. Youssef, Jennifer McIntosh, Delyse Hutchinson, Robert J. Hancox, George C. Patton, Craig A. Olsson
Summary: For the majority of parents, perinatal depression is a continuation of mental health problems that began well before pregnancy. Strategies to promote good perinatal mental health should start before parenthood and include both men and women.
PSYCHOLOGICAL MEDICINE
(2021)
Article
Psychiatry
Meredith O'Connor, Helena Romaniuk, Sarah Gray, Galina Daraganova
Summary: This study found a continuity of internalising difficulties from childhood to adolescence, with a higher risk of adolescent internalising problems for those who had experienced internalising symptoms in childhood. Other known risk factors were also associated with adolescent internalising problems.
SOCIAL PSYCHIATRY AND PSYCHIATRIC EPIDEMIOLOGY
(2021)
Review
Health Care Sciences & Services
Robert K. Mahar, Myra B. McGuinness, Bibhas Chakraborty, John B. Carlin, Maarten J. IJzerman, Julie A. Simpson
Summary: Observational DTR models are a recent development, primarily applied in areas such as HIV/AIDS, cancer, and diabetes. Various statistical methods are used in these studies, including inverse-probability weighting, the parametric G-formula, and Q-learning. Studies are generally categorized into those focusing on real-world clinical questions and methodological developments, with the former tending to use well-established statistical methods.
BMC MEDICAL RESEARCH METHODOLOGY
(2021)
Article
Medicine, General & Internal
Heather F. Gidding, Dorothy A. Machalek, Alexandra J. Hendry, Helen E. Quinn, Kaitlyn Vette, Frank H. Beard, Hannah S. Shilling, Rena Hirani, Iain B. Gosbell, David O. Irving, Linda Hueston, Marnie Downes, John B. Carlin, Matthew V. N. O'Sullivan, Dominic E. Dwyer, John M. Kaldor, Kristine Macartney
Summary: The study estimated SARS-CoV-2-specific antibody seroprevalence in Sydney after the first epidemic wave of COVID-19, with results showing a prevalence below 1%, indicating low community transmission. Early control measures were successful in limiting the spread of COVID-19, but ongoing efforts to reduce transmission are still crucial.
MEDICAL JOURNAL OF AUSTRALIA
(2021)
Article
Pediatrics
Anne-Lise Goddings, Russell M. Viner, Lisa Mundy, Helena Romaniuk, Charlotte Molesworth, John B. Carlin, Nicholas B. Allen, George C. Patton
Summary: The study reveals that anthropometric measures are positively associated with salivary androgen concentrations in pre-adolescent children, with overweight or obese individuals showing higher testosterone and DHEA concentrations. Obese individuals are more likely to have higher androgen levels compared to normal weight individuals of the same age group.
ARCHIVES OF DISEASE IN CHILDHOOD
(2021)
Article
Mathematical & Computational Biology
Patty Chondros, Obioha C. Ukoumunne, Jane M. Gunn, John B. Carlin
Summary: Simulation was used to compare the efficiency of matched-pair design, stratified design, and simple design in cluster randomized trials. Results showed that matched-pair design was generally the most efficient when the matching correlation was moderate to strong, while stratified design and simple design were more efficient for weak matching correlations.
STATISTICS IN MEDICINE
(2021)
Article
Mathematical & Computational Biology
Cattram D. Nguyen, Margarita Moreno-Betancur, Laura Rodwell, Helena Romaniuk, John B. Carlin, Katherine J. Lee
Summary: Semi-continuous variables have unique characteristics and various methods for imputation of missing values. Direct imputation of categories or deriving categories after imputation performed well, while methods requiring rounding showed poor performance. The parameter of interest should be considered when selecting an imputation procedure.
STATISTICS IN MEDICINE
(2021)
Article
Mathematical & Computational Biology
Rushani Wijesuriya, Margarita Moreno-Betancur, John B. Carlin, Anurika P. De Silva, Katherine J. Lee
Summary: Three-level data structures in health research studies often have missing data, which are addressed with multiple imputation approaches. Various methods can be used to account for the three-level structure in substantive analysis models, particularly when interactions or quadratic effects are involved. The substantive model compatible MI has shown promise in single-level data, but there are limited approaches for incomplete three-level data.
BIOMETRICAL JOURNAL
(2022)
Article
Allergy
Katherine Y. H. Chen, Wanyu Chu, Renee Jones, Peter Vuillermin, David Fuller, David Tran, Lena Sanci, Shivanthan Shanthikumar, John Carlin, Harriet Hiscock
Summary: This study examined the rates of hospital readmission and emergency department re-presentation for asthma in Australian children. It also explored the effects of modifiable factors on hospital readmission, including the role of general practitioners and home environmental factors. The findings suggest that hospital readmissions for asthma are increasing among Australian children, and highlight the important role of general practitioners in managing pediatric asthma. There was no apparent association between hospital or home environmental factors and hospital readmissions.
Article
Cardiac & Cardiovascular Systems
Andrea Driscoll, Sharon Meagher, Rhoda Kennedy, David L. Hare, Douglas F. Johnson, Kristina Asker, Omar Farouque, Helena Romaniuk, Liliana Orellana
Summary: This study explored the impact of inpatient HF NP (heart failure nurse practitioners) service on the 12-month rehospitalization, emergency department presentations, and mortality in patients with heart failure. The results showed that patients who received HF NP service had a lower risk of rehospitalization and ED presentations, and improved referrals to a home visiting program.
EUROPEAN JOURNAL OF CARDIOVASCULAR NURSING
(2023)
Article
Allergy
Katherine Y. H. Chen, Renee Jones, Shaoke Lei, Shivanthan Shanthikumar, Lena Sanci, John Carlin, Harriet Hiscock
Summary: This study investigated primary health care utilization among 767 children with asthma and examined the effect of primary care factors on asthma hospital readmission. The results showed that primary care use by children with asthma was often irregular and lacked continuity. Increased frequency of visits was associated with reduced readmissions and emergency department presentations.
Article
Health Care Sciences & Services
Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin, Katherine J. Lee, Margarita Moreno-Betancur
Summary: Despite recent advances in causal inference methods, outcome regression remains the most widely used approach for estimating causal effects in epidemiological studies with a single-point exposure and outcome. Missing data are common in these studies, and complete-case analysis (CCA) and multiple imputation (MI) are two frequently used methods for handling them. However, it is unclear whether MI should be conducted by exposure group in observational studies.
BMC MEDICAL RESEARCH METHODOLOGY
(2023)
Article
Public, Environmental & Occupational Health
Katherine J. Lee, John B. Carlin, Julie A. Simpson, Margarita Moreno-Betancur
Summary: Researchers are advised to classify their missing data as MCAR, MAR, or MNAR when analyzing the data. However, the original classification by Rubin in the 1970s has two major problems. First, it is difficult to assess the plausibility of the MAR assumption when there are missing data in multiple variables. Second, MCAR and MAR are not necessary conditions for consistent estimation, so the classification does not determine the best approach for handling missing data.
INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
(2023)
Article
Multidisciplinary Sciences
Shaan Stephanie Naughton, Helena Romaniuk, Anna Peeters, Alexandra Chung, Alethea Jerebine, Liliana Orellana, Tara Boelsen-Robinson
Summary: This observational study assessed the introduction of a comprehensive healthy food and drink policy and its impact on business outcomes and the healthiness of purchases. The implementation of the policy resulted in a shift towards healthier purchases, with a decrease in the sales of unhealthy drinks and an increase in the sales of healthier options. The study highlights the importance of policies to improve the health of retail food environments.
Article
Critical Care Medicine
Ben Gelbart, Suzanna Vidmar, David Stephens, Daryl Cheng, Jenny Thompson, Ahuva Segal, Tali Gadish, John Carlin
Summary: Approximately one-fifth of MET events resulted in intensive care admission, and nearly half of these patients required ICT within 12 hours. Patients requiring ICT had longer duration of respiratory support, intensive care and hospital length of stay, and increased mortality. Age < 1 year and experiencing a critical event increased the risk of requiring ICT.
CRITICAL CARE AND RESUSCITATION
(2021)