SCCM Resource Library-Methods For Minimizing Bias From Missing Data In Critical Care Research

Video Transcription

Video Summary

In this webcast, Dr. Douglas Lansdell discusses methods for minimizing bias from missing data in critical care research. He emphasizes the importance of rigor, reproducibility, and transparency when dealing with missing data. Dr. Lansdell suggests strategies to avoid missing data, such as using automated data extraction, consistent coding, and shortening instruments. When missing data cannot be avoided, he recommends using prediction or multiple imputation methods. These approaches involve using observed data to predict missing values or creating multiple imputed datasets to estimate the variability of results. Dr. Lansdell also discusses different types of missing data mechanisms, including missing completely at random, missing at random, and missing not at random. He highlights the implications and limitations of each approach under different missing data mechanisms. Finally, he stresses the importance of considering the amount of missing data and how it may affect the results, as well as being transparent about the assumptions and methods used in the analysis. Overall, Dr. Lansdell provides valuable insights and practical recommendations for addressing missing data in critical care research.

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.

Meta Tag

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

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