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Dynamic prediction by landmarking with data from cohort subsampling designs.

Yen Chang1, Anastasia Ivanova1, Demetrius Albanes2

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Statistical Methods in Medical Research
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

New methods enable precise health event prediction using limited cohort data. These landmarking techniques offer similar accuracy to full cohort analysis while significantly reducing data collection needs.

Keywords:
Case-cohort studyCox proportional hazards modelcohort subsampling designdynamic predictioninverse probability weightinglandmarkingnested case-control study

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Informatics

Background:

  • Longitudinal data from cohort studies and electronic health records can enhance health event prediction.
  • Landmarking is a key approach for dynamic prediction using such data.
  • Full cohort data collection is often resource-intensive, necessitating alternative strategies.

Purpose of the Study:

  • To develop and evaluate statistical methods for dynamic prediction using subsampled cohort data.
  • To adapt landmarking techniques for efficient analysis of limited data.
  • To compare the performance of new methods against traditional full cohort analysis.

Main Methods:

  • Conditional likelihood and inverse-probability weighting for landmarking with subsampled data.
  • Simulation studies to assess method applicability and predictive performance.
  • Application to nested case-control data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial.

Main Results:

  • Developed methods provide accurate dynamic prediction using only a fraction of the full cohort data.
  • The proposed techniques achieve predictive performance comparable to full cohort analyses.
  • Demonstrated the utility of these methods on real-world clinical trial data.

Conclusions:

  • Subsampling designs combined with novel statistical methods offer an efficient alternative for dynamic prediction in cohort studies.
  • These approaches reduce data collection burdens without compromising predictive accuracy.
  • The methods are applicable in settings with limited resources, enhancing the feasibility of longitudinal data analysis.