Methods For Minimizing Bias From Missing Data In Critical Care Research
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Video Summary
Asset Subtitle
Research, 2021
Asset Caption
Missing data is a common, yet often overlooked, source of bias that plagues both observational and randomized studies. Failure to appropriately account for missing data can create selection bias that may lead to spurious, unreplicable findings. This webcast from Discovery, the Critical Care Research Network, will describe the biases that can be created by missing data and provide an overview of effective methodology for reducing missing data bias.
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Content Type Webcast
Knowledge Area Research
Knowledge Level Intermediate
Knowledge Level Advanced
Membership Level Select
Membership Level Professional
Membership Level Associate
Tag Research
Year 2021
Keywords
missing data
bias
critical care research
rigor
reproducibility
transparency
prediction methods
multiple imputation
missing data mechanisms