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Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study.

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Summary
This summary is machine-generated.

Choosing between single-center and multi-center data for prognostic models depends on study goals. Multi-center data minimize prediction error, while single-center data improve validation success in low-signal or small-sample scenarios.

Keywords:
Feature selectionOmics dataPredictive modelPredictive performanceStudy designValidation

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Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • Prognostic models using high-dimensional omics data are crucial for predicting radio-therapeutic success.
  • Model development requires independent discovery and validation datasets, which can be single-center or multi-center.
  • Multi-center data offer broader generalizability but introduce heterogeneity; single-center data are less heterogeneous but prone to site-specific biases.

Purpose of the Study:

  • To investigate the impact of using single-center versus multi-center data for discovery and validation sets in prognostic modeling.
  • To assess how data characteristics like signal strength, number of informative features, and sample size influence this impact.

Main Methods:

  • A simulation study was conducted to evaluate predictive performance using various combinations of in silico single-center and multi-center data.
  • Standard bioinformatics workflows including batch correction, feature selection, and parameter estimation were emulated.
  • Model quality was assessed using false discovery rate, prediction error, chance of successful validation, and model calibration.

Main Results:

  • Prognostic models trained on multi-center data consistently showed lower prediction error compared to single-center models.
  • However, single-center discovery sets demonstrated superior performance in terms of false discovery rate and validation success when signal strength and sample size were low.
  • Validation data set choice primarily influenced the precision of the prediction error estimator, with multi-center data yielding more precise estimates.

Conclusions:

  • The choice of data source (single-center vs. multi-center) for discovery and validation should align with specific study objectives.
  • For minimizing prediction error, multi-center discovery data are recommended.
  • For improving false discovery rate and validation success in low-signal or small-sample settings, single-center discovery data are preferable.