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Combining multiple imputation with internal model validation in clinical prediction modeling: a systematic

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Most clinical prediction modeling studies use multiple imputation (MI) before internal model validation (IMV) due to simplicity. MI during IMV is more complex but may offer benefits for larger studies with higher missing data rates.

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Area of Science:

  • Clinical prediction modeling
  • Statistical methodology
  • Data science

Background:

  • Missing data is a significant challenge in clinical research, impacting the reliability of prediction models.
  • Multiple imputation (MI) and internal model validation (IMV) are crucial techniques for addressing missing data and ensuring model robustness.
  • Limited guidance exists on the optimal combination of MI and IMV strategies in clinical prediction modeling (CPM).

Purpose of the Study:

  • To investigate the current practices of combining MI with IMV in CPM studies.
  • To identify challenges in balancing predictive performance, methodological complexity, and resource demands.
  • To analyze the different strategies employed for MI and IMV integration.

Main Methods:

  • Systematic literature search of PubMed, Web of Science, and MathSciNet for CPM studies using MI and validation.
  • Categorization of studies based on the timing of MI relative to IMV: MI-prior-IMV vs. MI-during-IMV.
  • Description of strategy choices based on key study parameters, including MI methods and IMV techniques.

Main Results:

  • 108 studies were included, with 85% employing MI-prior-IMV.
  • MI-during-IMV studies featured larger sample sizes and higher missing data rates.
  • Multiple imputation by chained equations (MICE) was the predominant MI method; bootstrap and cross-validation were common IMV techniques in MI-during-IMV.

Conclusions:

  • MI-prior-IMV is prevalent due to its implementation simplicity.
  • MI-during-IMV, though more complex, is associated with larger datasets and higher missingness.
  • Further research is needed to systematically evaluate trade-offs and provide guidance on optimal MI-IMV strategies.