Your browser doesn't support javascript.
loading
Towards Treatment Effect Interpretability: A Bayesian Re-analysis of 194,129 Patient Outcomes Across 230 Oncology Trials.
Sherry, Alexander D; Msaouel, Pavlos; Kupferman, Gabrielle S; Lin, Timothy A; Abi Jaoude, Joseph; Kouzy, Ramez; El-Alam, Molly B; Patel, Roshal; Koong, Alex; Lin, Christine; Passy, Adina H; Miller, Avital M; Beck, Esther J; Fuller, C David; Meirson, Tomer; McCaw, Zachary R; Ludmir, Ethan B.
Afiliación
  • Sherry AD; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Msaouel P; Department of Genitourinary Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Kupferman GS; Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Lin TA; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Abi Jaoude J; Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD.
  • Kouzy R; Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • El-Alam MB; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Patel R; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Koong A; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
  • Lin C; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Passy AH; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Miller AM; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Beck EJ; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Fuller CD; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Meirson T; Department of Radiation Oncology, Division of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • McCaw ZR; Davidoff Cancer Center, Rabin Medical Center-Beilinson Hospital, Petach Tikva, Israel.
  • Ludmir EB; Insitro, South San Francisco, CA, USA.
medRxiv ; 2024 Jul 24.
Article en En | MEDLINE | ID: mdl-39108512
ABSTRACT
Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MedRxiv Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos