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Correct and logical inference on efficacy in subgroups and their mixture for binary outcomes.

Hui-Min Lin1, Haiyan Xu2, Ying Ding3

  • 1Takeda Oncology, Cambridge, Massachusetts, USA.

Biometrical Journal. Biometrische Zeitschrift
|October 25, 2018
PubMed
Summary

Relative response (RR) is appropriate for assessing targeted therapy efficacy in binary outcomes, unlike odds ratios. Calculating correlations requires careful consideration of subgroup response probabilities to avoid errors in study design.

Keywords:
binary endpointleast squares meanssubgroup mixable estimationtreatment efficacy

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Pharmacology

Background:

  • Targeted therapies require precise patient stratification using companion diagnostics.
  • Evaluating efficacy across marker-positive, marker-negative, and overall patient populations is crucial.
  • Standard efficacy measures may be unsuitable for mixture populations with binary endpoints.

Purpose of the Study:

  • To identify appropriate efficacy measures for targeted therapy trials with binary endpoints.
  • To develop a reliable method for calculating correlations between subgroup and overall efficacy estimates.
  • To inform the design of confirmatory studies using data from earlier phases.

Main Methods:

  • Demonstrated the inappropriateness of odds ratios for binary endpoints in mixture populations.
  • Proposed relative response (RR) as a suitable efficacy measure.
  • Developed a method for calculating correlations by first mixing subgroup response probabilities within treatment arms.

Main Results:

  • Relative response (RR) is an appropriate efficacy measure for binary endpoints in targeted therapy trials.
  • A safe method for calculating estimated correlations involves mixing subgroup response probabilities.
  • Ignoring the randomness of the mixing coefficient in correlation calculations can lead to significant errors.

Conclusions:

  • Relative response is recommended for evaluating targeted therapy efficacy with binary outcomes.
  • Accurate correlation estimation is vital for informing subsequent clinical trial designs.
  • Methodological rigor in statistical analysis ensures reliable interpretation of targeted therapy effectiveness.