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Increasing power in screening trials by testing control-arm specimens: application to multicancer detection

Hormuzd A Katki1, Philip C Prorok2, Philip E Castle1,2

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Journal of the National Cancer Institute
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Summary

The intended effect (IE) analysis, by testing control-arm specimens, significantly boosts the power of cancer screening trials. This approach can reduce sample sizes or accelerate trials, especially for multicancer detection (MCD) tests.

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Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trials

Background:

  • Cancer screening trials traditionally require large sample sizes and long durations to show mortality reduction.
  • The intended effect (IE) analysis leverages stored control-arm specimens for enhanced statistical power.
  • This method is particularly suitable for multicancer detection (MCD) tests using stored blood samples.

Purpose of the Study:

  • To evaluate the power and sample size advantages of the IE analysis compared to standard analysis in cancer screening trials.
  • To simulate hypothetical MCD screening trials to assess the IE analysis's effectiveness.
  • To project IE analysis performance on existing trials like NLST, MINN-FOBT-A, and PLCO-CRC.

Main Methods:

  • Simulated hypothetical MCD screening trials comparing standard vs. IE analysis.
  • Applied IE analysis projections to three established screening trials.
  • Assessed power gains and sample size reductions under specific assumptions.

Main Results:

  • IE analysis significantly reduced P values for mortality across trials (6-fold for NLST, 33-fold for MINN-FOBT-A, 260,000-fold for PLCO-CRC).
  • Sample size reductions for 90% power ranged from 25% (NLST) to 63% (PLCO-CRC).
  • For MCD trials, IE analysis reduced required subjects per arm from 100,000 to 37,500-50,000.

Conclusions:

  • Testing stored control-arm specimens via IE analysis substantially increases trial power.
  • IE analysis can reduce sample size requirements or accelerate trial timelines.
  • Significant power gains are achievable for multicancer detection (MCD) tests using this approach.