Missing data handling in non-inferiority and equivalence trials: A systematic review

2018 ◽  
Author(s):  
Brooke A. Rabe ◽  
Simon Day ◽  
Mallorie H. Fiero ◽  
Melanie L. Bell
Keyword(s):  
Systematic Review ◽  
Missing Data ◽  
Data Handling ◽  
Equivalence Trials

Missing Data Handling Methods

2018 ◽  
pp. 139-185 ◽  
Author(s):  
Craig K. Enders ◽  
Amanda N. Baraldi
Keyword(s):  
Missing Data ◽  
Data Handling ◽  
Handling Methods

Classification with Incomplete Data

2010 ◽  
pp. 147-175 ◽  
Author(s):  
Pedro J. García-Laencina ◽  
Juan Morales-Sánchez ◽  
Rafael Verdú-Monedero ◽  
Jorge Larrey-Ruiz ◽  
José-Luis Sancho-Gómez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Pattern Classification ◽  
Incomplete Data ◽  
Data Handling ◽  
Advantages And Disadvantages ◽  
Word Classification ◽  
Classification Tasks ◽  
Missing Data Techniques ◽  
Fundamental Requirement

Many real-word classification scenarios suffer a common drawback: missing, or incomplete, data. The ability of missing data handling has become a fundamental requirement for pattern classification because the absence of certain values for relevant data attributes can seriously affect the accuracy of classification results. This chapter focuses on incomplete pattern classification. The research works on this topic currently grows wider and it is well known how useful and efficient are most of the solutions based on machine learning. This chapter analyzes the most popular and proper missing data techniques based on machine learning for solving pattern classification tasks, trying to highlight their advantages and disadvantages.

Missing data in randomised controlled trials evaluating palliative interventions: a systematic review and meta-analysis

The Lancet ◽  
2016 ◽  
Vol 387 ◽  
pp. S53 ◽  
Author(s):  
Jamilla A Hussain ◽  
Ian R White ◽  
Dean Langan ◽  
Miriam J Johnson ◽  
David C Currow ◽  
...  
Keyword(s):  
Systematic Review ◽  
Missing Data ◽  
Randomised Controlled Trials ◽  
Meta Analysis ◽  
Controlled Trials ◽  
Randomised Controlled

scholarly journals Reporting and handling of missing data in predictive research for prevalent undiagnosed type 2 diabetes mellitus: a systematic review

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Katya L Masconi ◽  
Tandi E Matsha ◽  
Justin B Echouffo-Tcheugui ◽  
Rajiv T Erasmus ◽  
Andre P Kengne
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Missing Data ◽  
Predictive Research
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