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Performance of Two-Phase Designs for the Time-to-Event Outcome and a Case Study Assessing the Relapse Risk Associated

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Summary
This summary is machine-generated.

Comparing two-phase genomic study designs, the optimal design offers the highest power for survival analysis. Logistic regression provides similar power with significantly reduced computational costs, aiding efficient biomarker discovery.

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Area of Science:

  • Genomics
  • Biostatistics
  • Epidemiology

Background:

  • Genomic studies of time-to-event phenotypes often use two-phase designs to reduce costs.
  • Common designs include nested case-control, case-cohort, and extreme case-control.
  • A recent optimal two-phase design using maximum likelihood was proposed but lacked direct comparison.

Purpose of the Study:

  • To directly evaluate and compare typical two-phase designs, including an optimal design.
  • Assess performance based on type I error, power, effect size estimation, and computational time.
  • Provide recommendations for selecting two-phase designs and analysis methods.

Main Methods:

  • Direct evaluation of typical two-phase designs and Tao's optimal design.
  • Utilized both simulated and real data sets for analysis.
  • Assessed metrics including type I error, power, effect size estimation, and computational time.

Main Results:

  • The optimal design demonstrated the highest statistical power and accurate effect size estimation under Cox regression.
  • Logistic regression achieved comparable power to more complex methods but with substantially lower computational cost.
  • Applied methods to the MP2PRT study, reporting hazard ratios for cancer subtypes on relapse risk.

Conclusions:

  • The optimal design is recommended for maximizing power in two-phase genomic studies.
  • Logistic regression is a computationally efficient alternative offering similar power.
  • Selection criteria should consider power, effect size estimation bias, and computational efficiency.