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
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
Health Care Sciences & Services
Rose Sisk, Matthew Sperrin, Niels Peek, Maarten van Smeden, Glen Philip Martin
Summary: This study compares multiple imputation and regression imputation in handling missing data in clinical prediction models and finds comparable predictive performance between the two methods. Therefore, the choice of handling strategy should be based on whether missing data are allowed during model development.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2023)
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
Mathematical & Computational Biology
Chia-Rui Chang, Yue Song, Fan Li, Rui Wang
Summary: Covariate adjustment is important in analyzing data from randomized clinical trials, but missing data can be a barrier. This study reviews different covariate adjustment methods with incomplete covariate data. The researchers propose a weighting approach that combines inverse probability weighting and overlap weighting to adjust for missing outcomes and covariates, and conduct comprehensive simulation studies to evaluate the performance of the methods.
STATISTICS IN MEDICINE
(2023)
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
Health Care Sciences & Services
Ping-Tee Tan, Suzie Cro, Eleanor Van Vogt, Matyas Szigeti, Victoria R. Cornelius
Summary: Missing data is common in RCTs, and MI is widely used for analysis, while controlled MI is less frequently used mainly in sensitivity analysis. The current use and reporting of MI methods in RCTs need improvement.
BMC MEDICAL RESEARCH METHODOLOGY
(2021)
Article
Mathematical & Computational Biology
Thomas R. Sullivan, Lisa N. Yelland, Margarita Moreno-Betancur, Katherine J. Lee
Summary: Based on the findings, imputing independent and paired data separately is recommended for unbiased estimates. Ignoring clustering effects in the imputation model may be appropriate in some cases, but could lead to biased variance estimates in others.
STATISTICS IN MEDICINE
(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
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
Biochemical Research Methods
Yuan Luo
Summary: Clinical data often have missing entries, posing a challenge to deriving optimal knowledge from the data. The Data Analytics Challenge on Missing data Imputation (DACMI) provides a benchmark dataset for evaluating and advancing imputation techniques for clinical time series. Competitive machine learning and statistical models coupled with carefully engineered features show strong performance in imputation.
BRIEFINGS IN BIOINFORMATICS
(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
Mathematical & Computational Biology
Daniel Westreich, Jeffrey S. A. Stringer
Summary: Inverse probability weighting is a useful method for correcting missing data. New estimators for nonmonotone missingness, including unconstrained maximum likelihood estimator (UMLE) and constrained Bayesian estimator (CBE), were introduced in 2018. This study compares the performance of these estimators with multiple imputation (MI) in the setting of an observational study, where inverse probability of treatment weights are used to address confounding.
STATISTICS IN MEDICINE
(2023)
Article
Multidisciplinary Sciences
Yingfeng Ge, Zhiwei Li, Jinxin Zhang
Summary: The problem of missing data for dichotomous variables is common in medical research, but little research has focused on imputation methods and their performance. This study comprehensively compared the performance of eight imputation methods in different scenarios and found that missing mechanisms, value distributions, and variable correlations were the main factors affecting performance. Machine learning-based methods, especially SVM, ANN, and DT, achieved high accuracy and stable performance, making them potentially applicable for practical use.
SCIENTIFIC REPORTS
(2023)
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
Substance Abuse
Antonio Verdejo-Garcia, Tara Rezapour, Emily Giddens, Arash Khojasteh Zonoozi, Parnian Rafei, Jamie Berry, Alfonso Caracuel, Marc L. Copersino, Matt Field, Eric L. Garland, Valentina Lorenzetti, Leandro Malloy-Diniz, Victoria Manning, Ely M. Marceau, David L. Pennington, Justin C. Strickland, Reinout Wiers, Rahia Fairhead, Alexandra Anderson, Morris Bell, Wouter J. Boendermaker, Samantha Brooks, Raimondo Bruno, Salvatore Campanella, Janna Cousijn, W. Miles Cox, Andrew C. Dean, Karen D. Ersche, Ingmar Franken, Brett Froeliger, Pedro Gamito, Thomas E. Gladwin, Priscila D. Goncalves, Katrijn Houben, Joanna Jacobus, Andrew Jones, Anne M. Kaag, Johannes Lindenmeyer, Elly McGrath, Talia Nardo, Jorge Oliveira, Charlotte R. Pennington, Kelsey Perrykkad, Hugh Piercy, Claudia Rupp, Mieke H. J. Schulte, Lindsay M. Squeglia, Petra Staiger, Dan J. Stein, Jeff Stein, Maria Stein, William W. Stoops, Mary Sweeney, Katie Witkiewitz, Steven P. Woods, Richard Yi, Min Zhao, Hamed Ekhtiari
Summary: This study used a Delphi approach to reach consensus on recommendations for developing and applying cognitive training and remediation interventions for substance use disorders. Through two rounds of surveys, experts reached consensus on the targets, approaches, active ingredients, and modes of delivery for these interventions. The study indicates that intervention measures based on validated techniques and flexible delivery methods can effectively improve cognitive deficits in the treatment of substance use disorders.
Article
Substance Abuse
Victoria R. R. Votaw, Katie Witkiewitz, M. Lee Van Horn, Richard C. C. Crist, Timothy Pond, Henry R. R. Kranzler
Summary: This study aimed to investigate whether a single-nucleotide polymorphism (rs2832407) in GRIK1 moderates the effects of topiramate treatment for drinking reduction. The results showed that topiramate treatment was effective in reducing daily heavy drinking and desire to drink, and this effect was not influenced by the rs2832407*C-allele homozygotes.
Editorial Material
Substance Abuse
Henry R. R. Kranzler
ALCOHOLISM-CLINICAL AND EXPERIMENTAL RESEARCH
(2023)
Article
Psychology, Biological
Cassandra L. Boness, Katie Witkiewitz
Summary: This article discusses the overlap between alcohol use disorder treatment and etiologic maintenance mechanisms, suggesting areas for further research and potential improvements in treatment efficacy. By leveraging these overlapping processes, it may be possible to identify treatment targets and enhance the effectiveness of existing treatments.
EXPERIMENTAL AND CLINICAL PSYCHOPHARMACOLOGY
(2023)
Article
Substance Abuse
Cassandra L. L. Boness, Victoria R. R. Votaw, Meredith W. W. Francis, Ashley L. L. Watts, Sarah H. H. Sperry, Christopher S. S. Kleva, Linda Nellis, Yoanna McDowell, Antoine B. B. Douaihy, Kenneth J. J. Sher, Katie Witkiewitz
Summary: This paper discusses the evolution of conceptualizations and diagnostic criteria for alcohol use disorder (AUD) in the United States, influenced by sociopolitical factors. It provides four examples of how DSM-defined alcoholism, abuse/dependence, and AUD have been influenced by sociopolitical factors. The importance of recognizing and understanding these sociopolitical factors in the application of AUD diagnoses is emphasized. Furthermore, a roadmap is offered to improve the diagnosis of AUD, focusing on falsifiability, acknowledging researchers' assumptions about human behavior, and collaboration across subfields. Such efforts have the potential to minimize sociopolitical influences in the development of diagnostic criteria and maximize the treatment utility of diagnoses.
ADDICTION RESEARCH & THEORY
(2023)
Review
Substance Abuse
Henry R. Kranzler, Emily E. Hartwell
Summary: Chronic heavy alcohol use has negative impacts on neurotransmitter systems and causes various medical, psychiatric, and social problems. Evidence-based medications for treating alcohol use disorder (AUD) are not widely used in clinical practice. Pharmacogenetic approaches have gained interest but have not yet yielded strong enough results for routine clinical care.
ALCOHOL-CLINICAL AND EXPERIMENTAL RESEARCH
(2023)
Article
Substance Abuse
Christine Vinci, Steven K. Sutton, Min-Jeong Yang, Sana Baban, Rachel Sauls, Katie Witkiewitz, Karen O. Brandon, Marina Unrod, Thomas H. Brandon, David W. Wetter
Summary: This pilot study evaluated the feasibility and acceptability of a mindfulness-based intervention for addressing both smoking and alcohol use. The results showed that the intervention had comparable outcomes to cognitive behavioral therapy (CBT) in reducing tobacco and alcohol use. Future research should conduct large-scale trials to evaluate the efficacy of this intervention.
DRUG AND ALCOHOL DEPENDENCE
(2023)
Article
Psychology, Clinical
Kevin A. Hallgren, Gantt P. Galloway, Katie Witkiewitz, Paul Linde, Bob Nix, John E. Mendelson
Summary: This study investigated the 90-day treatment retention and changes in blood alcohol concentration (BAC) in a large cohort receiving AUD telehealth. The results showed that most patients had good retention rates during the 90-day treatment period, and there was a significant reduction in estimated daily peak BAC. This suggests that telehealth is a viable approach for promoting drinking reductions in AUD treatment.
AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE
(2023)
Article
Psychiatry
Youshu Cheng, Cecilia Dao, Hang Zhou, Boyang Li, Rachel L. Kember, Sylvanus Toikumo, Hongyu Zhao, Joel Gelernter, Henry R. Kranzler, Amy C. Justice, Ke Xu
Summary: Smoking behaviors and alcohol use disorder commonly co-occur and are moderately heritable. Previous single-trait GWAS studies have identified multiple loci for smoking and AUD. However, limited by small samples, GWASs aiming to identify loci contributing to co-occurring smoking and AUD have been less informative. Using multi-trait analysis of GWASs (MTAG) with data from the Million Veteran Program, this study identified novel loci associated with smoking initiation and cessation. Functional annotation highlighted biologically important regions contributing to smoking behaviors. MTAG results did not enhance discovery for smoking behaviors and alcohol consumption compared to single-trait GWAS. This study provides new insights into the pleiotropic effects of genetic variants on smoking behavior and AUD through the application of MTAG.
TRANSLATIONAL PSYCHIATRY
(2023)
Article
Psychology, Multidisciplinary
Katie Witkiewitz, Kevin E. Vowles
Summary: Chronic pain and substance use disorders (SUDs) are prevalent and persisting issues, with a significant proportion of the adult population being affected by them. Accessing evidence-based treatments for both conditions is challenging, but ongoing research is shedding light on the mechanisms and co-occurrence of chronic pain and substance use. Integrated behavioral treatments based on acceptance and mindfulness are being developed and tested, and there are increasing efforts in research funding, training, dissemination, and implementation of evidence-based treatments.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
(2023)
Review
Substance Abuse
Rory A. Pfund, Shelby A. King, David P. Forman, James M. Zech, Meredith K. Ginley, Samuel C. Peter, Nicholas W. McAfee, Katie Witkiewitz, James P. Whelan
Summary: The objective of this study was to examine the effect of cognitive behavioral techniques on gambling-related harms, psychological symptoms, and quality of life. The results showed that cognitive behavioral techniques significantly reduced anxiety and depression symptoms, and improved the quality of life. However, further research is needed to explore the relationships between gambling harms, psychological symptoms, and quality of life.
PSYCHOLOGY OF ADDICTIVE BEHAVIORS
(2023)
Review
Psychology, Clinical
Cassandra L. L. Boness, Victoria R. R. Votaw, Frank J. J. Schwebel, David I. K. Moniz-Lewis, R. Kathryn McHugh, Katie Witkiewitz
Summary: Cognitive behavioral therapy (CBT) is an effective treatment for substance use disorders (SUDs), particularly during the early follow-up period.
CLINICAL PSYCHOLOGY-SCIENCE AND PRACTICE
(2023)
Article
Psychology, Clinical
Zachary L. Mannes, Ofir Livne, Justin Knox, Deborah S. Hasin, Henry R. Kranzler
Summary: This study aims to examine the prevalence and clinical correlates of opioid withdrawal syndrome (OWS) among adults in the U.S. who engage in non-medical use of prescription opioids (NMOU). Results revealed that OWS is prevalent among individuals with NMOU and is associated with psychiatric disorders and opioid use disorder (OUD).
AMERICAN JOURNAL OF DRUG AND ALCOHOL ABUSE
(2023)
Article
Substance Abuse
Katie Witkiewitz, Megan Kirouac, James W. Baurley, Christopher S. McMahan
Summary: This study analyzed data from the Project MATCH and COMBINE studies to explore baseline predictors of drinking patterns. The findings suggest that prior drinking patterns are the most consistent predictors of future drinking patterns. Social network drinking, AUD severity, mental health symptoms, and constructs based on the addiction cycle were associated with patterns of drinking prior to treatment. Addiction cycle constructs, AUD severity, purpose in life, social network, legal history, craving, and motivation were associated with drinking during and following treatment.
ALCOHOL-CLINICAL AND EXPERIMENTAL RESEARCH
(2023)