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Developing prediction models when there are systematically missing predictors in individual patient data

Michael Seo1,2, Toshi A Furukawa3, Eirini Karyotaki4,5,6

  • 1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

Research Synthesis Methods
|February 9, 2023
PubMed
Summary
This summary is machine-generated.

Developing clinical prediction models with multiple studies presents challenges due to missing predictors. New ensemble and imputation methods outperform restricted approaches, especially with limited data or high heterogeneity.

Keywords:
ensemble predictive modelingindividual patient datameta-analysismultilevel modelprediction research

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Area of Science:

  • Biostatistics
  • Clinical Epidemiology
  • Health Informatics

Background:

  • Clinical prediction models are crucial in healthcare, often developed from individual patient data (IPD).
  • Leveraging IPD from multiple studies via meta-analysis enhances model power and precision.
  • Systematically missing predictors across studies complicate multi-study model development.

Purpose of the Study:

  • To describe and compare methods for developing prediction models with continuous outcomes when predictors are systematically missing across multiple studies.
  • To evaluate the performance of different approaches through simulation studies.
  • To provide practical guidance and R code for implementing these methods.

Main Methods:

  • Four approaches were compared: restricting predictors to those available in all studies, two multiple imputation techniques (one ignoring, one accounting for study clustering), and a novel multi-study ensemble method.
  • Simulations explored performance under various scenarios, including differing numbers of studies, predictor effect sizes, and heterogeneity.
  • The methods were illustrated using a real-world dataset from 12 psychotherapy trials for depression.

Main Results:

  • Multiple imputation and the proposed ensemble method demonstrated superior performance compared to the restrict predictors approach.
  • The ensemble method outperformed imputation methods in several scenarios, particularly with fewer studies, small predictor effects, and substantial heterogeneity.
  • All methods were successfully applied to the depression psychotherapy dataset, with code provided for reproducibility.

Conclusions:

  • For developing clinical prediction models with systematically missing predictors across multiple studies, ensemble and imputation methods are recommended over restricting predictors.
  • The novel multi-study ensemble approach shows particular promise, offering advantages in specific common scenarios.
  • Accessible R code is provided to facilitate the practical application of these advanced modeling techniques.