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Ignoring Non-ignorable Missingness.

Sophia Rabe-Hesketh1, Anders Skrondal2,3,4

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Summary
This summary is machine-generated.

Valid inference is possible even when data is missing, without needing the missing at random (MAR) assumption. Three methods allow standard estimators to be used when missingness is typically non-ignorable.

Keywords:
MARdata deletionm-graphmake-MARmissing dataordered factorizationprotective estimation

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Analysis

Background:

  • The missing at random (MAR) assumption is a cornerstone of statistical inference for incomplete datasets.
  • Misinterpretations of MAR can lead to unnecessarily complex analytical requirements.
  • Standard estimators are often avoided when data missingness is presumed non-ignorable.

Purpose of the Study:

  • To demonstrate that the classical missing at random (MAR) assumption is not always necessary for valid statistical inference.
  • To identify and explain strategies for handling non-ignorable missing data using standard estimation methods.
  • To clarify common misunderstandings surrounding the MAR assumption.

Main Methods:

  • Reviewing the theoretical underpinnings of missing data assumptions, particularly MAR.
  • Proposing and detailing three distinct strategies to address non-ignorable missingness: conditioning on variables, data discarding, and parameter protection.
  • Illustrating how these strategies permit the use of standard statistical estimators.

Main Results:

  • The classical missing at random (MAR) assumption is frequently not required for valid inference when ignoring the missingness mechanism.
  • Several assumptions often mistakenly linked to MAR are also unnecessary.
  • Three practical strategies enable the use of standard estimators even with data typically deemed non-ignorably missing.

Conclusions:

  • Valid statistical inference can be achieved without adhering to the strict missing at random (MAR) assumption in many scenarios.
  • Understanding and applying specific strategies can simplify the analysis of incomplete data.
  • Researchers can confidently use standard estimators by employing methods like conditioning on variables, data discarding, or parameter protection.