Prognostic Model for Predicting Survival in Men With Hormone-Refractory Metastatic Prostate Cancer

DOI Web Site 被引用文献12件
  • Susan Halabi
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Eric J. Small
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Philip W. Kantoff
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Michael W. Kattan
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Ellen B. Kaplan
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Nancy A. Dawson
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Ellis G. Levine
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Brent A. Blumenstein
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...
  • Nicholas J. Vogelzang
    From the Department of Biostatistics and Bioinformatics and CALGB Statistical Center, Duke University Medical Center, Durham, NC; Urologic Oncology Program, University of California at San Francisco, San Francisco, CA; The Lank Center for Genitourinary Oncology, Department of Adult Oncology, Dana-Farber Cancer Institute, Boston MA; Memorial Sloan-Kettering Cancer Center, New York, and Department of Medicine, Roswell Park Cancer Institute, Buffalo, NY; University of Maryland, Baltimore, MD; and Section of...

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<jats:p> Purpose: To develop and validate a model that can be used to predict the overall survival probability among metastatic hormone-refractory prostate cancer patients (HRPC). </jats:p><jats:p> Patients and Methods: Data from six Cancer and Leukemia Group B protocols that enrolled 1,101 patients with metastatic hormone-refractory adenocarcinoma of the prostate during the study period from 1991 to 2001 were pooled. The proportional hazards model was used to develop a multivariable model on the basis of pretreatment factors and to construct a prognostic model. The area under the receiver operating characteristic curve (ROC) was calculated as a measure of predictive discrimination. Calibration of the model predictions was assessed by comparing the predicted probability with the actual survival probability. An independent data set was used to validate the fitted model. </jats:p><jats:p> Results: The final model included the following factors: lactate dehydrogenase, prostate-specific antigen, alkaline phosphatase, Gleason sum, Eastern Cooperative Oncology Group performance status, hemoglobin, and the presence of visceral disease. The area under the ROC curve was 0.68. Patients were classified into one of four risk groups. We observed a good agreement between the observed and predicted survival probabilities for the four risk groups. The observed median survival durations were 7.5 (95% confidence interval [CI], 6.2 to 10.9), 13.4 (95% CI, 9.7 to 26.3), 18.9 (95% CI, 16.2 to 26.3), and 27.2 (95% CI, 21.9 to 42.8) months for the first, second, third, and fourth risk groups, respectively. The corresponding median predicted survival times were 8.8, 13.4, 17.4, and 22.80 for the four risk groups. </jats:p><jats:p> Conclusion: This model could be used to predict individual survival probabilities and to stratify metastatic HRPC patients in randomized phase III trials. </jats:p>

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