Search Results - Methods for Handling Missing Data in Statistical Analysis

Missing binary outcomes under covariate‐dependent missingness in cluster randomised trials by Anower Hossain, Karla Díaz-Ordaz, Jonathan Bartlett

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Random forest analysis and lasso regression outperform traditional methods in identifying missing data auxiliary variables when the MAR mechanism is nonlinear (p.s. Stop using Little’s MCAR test) by Hayes, Timothy, Baraldi, Amanda N., Coxe, Stefany

“… We propose the use of random forest analysis and lasso regression as alternative methods to select auxiliary variables, particularly in situations in which the missing data pattern is nonlinear or otherwise complex (i.e…”
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Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction by Hong, Shangzhi, Lynn, Henry S.

“…Background Missing data are common in statistical analyses, and imputation methods based on random forests (RF…”
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A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic by Cro, Suzie, Morris, Tim P., Kahan, Brennan C., Cornelius, Victoria R., Carpenter, James R.

“… International drug trial guidelines recommend trialists review plans for handling missing data in the conduct and statistical analysis, but clear recommendations are lacking…”
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Appropriate statistical methods were infrequently used in cluster-randomized crossover trials by Arnup, Sarah J., Forbes, Andrew B., Kahan, Brennan C., Morgan, Katy E., McKenzie, Joanne E.

“…; and citation searches of CRXO methodological articles were conducted to December 2014. We extracted data on design characteristics and statistical methods for sample size, data analysis, and handling of missing data…”
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Statistical Methods for Handling Missing Data in Functional Data Analysis by Zhu, Will

“…Statistical analyses of functional data have drawn increased attention in recent years, yet handling missing data remains a notable obstacle in functional data analysis…”
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A New Multiple Imputation Method for High‐Dimensional Neuroimaging Data by Lu, Tong, Kochunov, Peter, Chen, Chixiang, Huang, Hsin‐Hsiung, Hong, L. Elliot, Chen, Shuo

“… Neglecting this issue can introduce bias and lead to erroneous inferential conclusions, making it crucial to employ appropriate statistical methods for handling missing data…”
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Statistical analysis plan for the ARtificially Intelligent image fusion system versus standard treatment to guide endovascular Aortic aneurysm repair (ARIA): a multi-centre randomised controlled trial by Wafa, Hatem A., Budge, James, Carrell, Tom, Yaqub, Medeah, Waltham, Matt, Pilecka, Izabela, Kelly, Joanna, Murphy, Caroline, Palmer, Stephen, Clough, Rachel E., Wang, Yanzhong

“… This paper describes the statistical analysis plan for the study. Methods/design The ARIA trial, a phase III, multi-centre, open-label, two-armed, parallel groups…”
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Robust skew-t factor analysis models for handling missing data by Wang, Wan-Lun, Liu, Min, Lin, Tsung-I

“…) models in the presence of missing values or nonresponses. As a robust extension of the ordinary factor analysis model, the STFA model assumes a restricted version…”
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Statistical analysis and handling of missing data in cluster randomised trials: protocol for a systematic review by Fiero, Mallorie, Huang, Shuang, Bell, Melanie L

“… The current review focuses on evaluating methods used in statistical analysis and handling of missing data with respect to the primary outcome in CRTs…”
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Missing data were poorly reported and handled in randomized controlled trials with repeatedly measured continuous outcomes: a cross-sectional survey by Ren, Yan, Jia, Yulong, Huang, Yunxiang, Zhang, Yuanjin, Li, Qianrui, Yao, Minghong, Li, Ling, Li, Guowei, Yang, Min, Yan, Peijing, Wang, Yuning, Zou, Kang, Sun, Xin

“… Evidence on the reporting and handling of such outcome data is lacking, which has prevented further improvement in methods and reporting of RCTs…”
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Treatment of missing values in process data analysis by Imtiaz, S. A., Shah, S. L.

“…‐rate sampling, outliers, compressed data etc. Since most modelling and data analysis methods are developed to analyze regularly sampled and well conditioned data sets there is a need for pre‐treatment of data…”
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Statistical analysis and handling of missing data in cluster randomized trials: a systematic review by Fiero, Mallorie H., Huang, Shuang, Oren, Eyal, Bell, Melanie L.

“… The aim of this review was to evaluate approaches for handling missing data and statistical analysis with respect to the primary outcome in CRTs…”
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Statistical analysis plan for the Laser-1st versus Drops-1st for Glaucoma and Ocular Hypertension Trial (LiGHT): a multi-centre randomised controlled trial by Vickerstaff, Victoria, Ambler, Gareth, Bunce, Catey, Xing, Wen, Gazzard, Gus

“…’) and that this is associated with reduced costs and improved tolerability of treatment. This paper describes the statistical analysis plan for the study…”
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Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations by Mukherjee, Kumar, Gunsoy, Necdet B., Kristy, Rita M., Cappelleri, Joseph C., Roydhouse, Jessica, Stephenson, Judith J., Vanness, David J., Ramachandran, Sujith, Onwudiwe, Nneka C., Pentakota, Sri Ram, Karcher, Helene, Di Tanna, Gian Luca

“… Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research…”
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Missing Data in Alcohol Clinical Trials with Binary Outcomes by Hallgren, Kevin A., Witkiewitz, Katie, Kranzler, Henry R., Falk, Daniel E., Litten, Raye Z., O'Malley, Stephanie S., Anton, Raymond F.

“…). Using simulation methods, missing data were introduced under common scenarios with varying sample sizes and amounts of missing data…”
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Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials by Blette, Bryan S, Halpern, Scott D, Li, Fan, Harhay, Michael O

“… While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster…”
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How can I deal with missing data in my study? by Bennett, Derrick A.

“…) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins…”
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A general method for handling missing binary outcome data in randomized controlled trials by Jackson, Dan, White, Ian R., Mason, Dan, Sutton, Stephen

“… We develop a general method for exploring the impact of missing data in such trials, with a focus on abstinence outcomes…”
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The interpretation of long-term trials of biologic treatments for psoriasis: trial designs and the choices of statistical analyses affect ability to compare outcomes across trials by Langley, R.G., Reich, K.

“… Secondly, data from published trials of biologic therapies for psoriasis are reviewed in light of the design and analysis choices implemented in the studies…”
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