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A note on improved statistical approaches to account for pseudoprogression.

Nacer Abrouk1, Bryan Oronsky2, Scott Caroen1

  • 1EpicentRx Inc, 4445 Eastgate Mall, Suite 200, San Diego, CA, 92121, USA.

Cancer Chemotherapy and Pharmacology
|February 7, 2018
PubMed
Summary
This summary is machine-generated.

New statistical methods accurately distinguish true tumor growth from pseudoprogression in immuno-oncology studies. This improves the analysis of novel combination therapies, preventing premature discontinuation of effective treatments.

Keywords:
Immuno-oncologyPseudoprogressionRRx-001Tumor flare

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Area of Science:

  • Oncology
  • Immunology
  • Biostatistics

Background:

  • Immuno-oncology agents can cause apparent tumor growth due to immune infiltration, mimicking progressive disease.
  • This pseudoprogression can lead to the premature discontinuation of effective therapies.
  • Improved statistical strategies are crucial for accurate assessment of treatment response in immuno-oncology.

Purpose of the Study:

  • To evaluate novel time-to-event statistical analyses for distinguishing true progression from pseudoprogression.
  • To test the efficacy of a combination therapy (RRx-001 and anti-PD-L1) in a myeloma preclinical model using these advanced statistical methods.

Main Methods:

  • Application of time-to-event analyses, including Kaplan-Meier and Cox proportional hazards models.
  • Comparison with traditional mean-based mixed effects modeling.
  • Analysis of cumulative incidence rates and restricted mean survival times for tumor growth reduction.

Main Results:

  • Time-to-event analyses revealed statistically significant superiority of the combination therapy.
  • Nonparametric p values demonstrated significant differences in time to ≥50% tumor growth reduction.
  • Advanced methods successfully captured longitudinal data and immune infiltration resolution.

Conclusions:

  • Time-to-event statistical analyses are superior to traditional methods for analyzing immuno-oncology preclinical data.
  • These methods accurately account for pseudoprogression, enabling better assessment of therapeutic efficacy.
  • The combination of RRx-001 and anti-PD-L1 shows promise in myeloma models.