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Using a Sample Size Calculation Framework for Clinical Prediction Models When Developing and Selecting Mapping

Yasuhiro Hagiwara1

  • 1Department of Biostatistics, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|July 20, 2023
PubMed
Summary
This summary is machine-generated.

Calculating adequate sample size is crucial for developing reliable health-related quality-of-life (HRQOL) mapping algorithms. This study proposes a framework, finding four of ten evaluated mapping studies lacked sufficient sample size for accurate predictions.

Keywords:
health utilitylinear regressionmappingpreference-based measuresample size

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Area of Science:

  • Health Economics
  • Biostatistics
  • Clinical Prediction Modeling

Background:

  • Mapping algorithms translate health-related quality-of-life (HRQOL) measures to preference-based measures (PBMs) for economic evaluations.
  • No established guidance exists for determining the necessary sample size in developing these mapping algorithms.
  • Linear regression is a common method for creating these crosswalks between different patient-reported outcome instruments.

Purpose of the Study:

  • To propose a framework for calculating the sample size required for developing and selecting mapping algorithms.
  • To assess the adequacy of sample sizes in existing mapping studies using linear regression.
  • To provide guidance for health economics researchers on sample size considerations in mapping studies.

Main Methods:

  • A framework for sample size calculation was developed based on criteria for clinical prediction models.
  • Four specific criteria were used: global shrinkage factor (≥0.9), adjusted R-squared difference (≤0.05), residual standard deviation error (≤1.1), and model intercept error (≤0.025).
  • Ten published mapping studies using linear regression were evaluated against these criteria.

Main Results:

  • The necessary information for sample size calculation was successfully extracted from ten mapping studies.
  • Four out of the ten evaluated mapping studies did not meet the proposed sample size criteria.
  • This indicates a potential underestimation of required sample sizes in current mapping research.

Conclusions:

  • Sample size is a critical factor that must be considered during the development and selection of mapping algorithms.
  • The proposed framework offers a method to evaluate sample size adequacy in existing and future mapping studies.
  • Further research should extend this framework to other regression techniques used in mapping.