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Bayesian additional evidence for decision making under small sample uncertainty.

Arjun Sondhi1, Brian Segal2,3, Jeremy Snider2

  • 1Flatiron Health, Inc., 233 Spring St, New York, NY, 10013, USA. arjun.sondhi@flatiron.com.

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|October 25, 2021
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Summary
This summary is machine-generated.

Statistical inference with small datasets is uncertain. Bayesian Additional Evidence (BAE) quantifies this uncertainty, helping researchers interpret results and decide on future research directions for precision oncology.

Keywords:
BayesianReal worldReal world evidenceSmall sample size

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Area of Science:

  • Biostatistics
  • Precision Oncology
  • Statistical Inference

Background:

  • Small datasets in precision oncology lead to low statistical power and high uncertainty.
  • Standard inferential measures struggle to draw strong conclusions or assess future research utility from small sample sizes.
  • Quantifying uncertainty in results from small datasets is crucial for reliable interpretation.

Purpose of the Study:

  • To introduce a novel method, Bayesian Additional Evidence (BAE), for interpreting statistical results from small datasets.
  • To provide a framework for assessing the credibility of significant and non-significant findings.
  • To guide researchers in making informed decisions about further investigations based on initial analyses.

Main Methods:

  • Developed Bayesian Additional Evidence (BAE), a method to determine necessary additional evidence for credibility.
  • BAE quantifies evidence needed to shift non-significant results to credible or significant results to non-credible.
  • The method does not require a prior distribution; tipping points are compared to effect size ranges.

Main Results:

  • Applied BAE to a comparative effectiveness analysis with a hazard ratio of 0.31 (95% CI: 0.09, 1.1).
  • The BAE tipping point was calculated as 0.54, indicating a hazard ratio of 0.54 or lower would yield posterior credibility.
  • The findings suggest the association is worthy of further research, considering effect sizes and existing supportive evidence.

Conclusions:

  • The Bayesian Additional Evidence (BAE) method offers a valuable framework for interpreting results from small datasets.
  • BAE assists researchers in navigating uncertainty and making decisions about continuing investigations.
  • The method is applicable to time-to-event outcomes and other normally-distributed estimators (binary, continuous).