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A Prognostic Model to Predict Mortality among Non–Small-Cell Lung Cancer Patients in the U.S. Military Health System  Jie Lin, PhD, MPH, Corey A. Carter,

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Presentation on theme: "A Prognostic Model to Predict Mortality among Non–Small-Cell Lung Cancer Patients in the U.S. Military Health System  Jie Lin, PhD, MPH, Corey A. Carter,"— Presentation transcript:

1 A Prognostic Model to Predict Mortality among Non–Small-Cell Lung Cancer Patients in the U.S. Military Health System  Jie Lin, PhD, MPH, Corey A. Carter, MD, Katherine A. McGlynn, PhD, Shelia H. Zahm, ScD, Joel A. Nations, MD, William F. Anderson, MD, MPH, Craig D. Shriver, MD, Kangmin Zhu, MD, PhD, Joel A. Nations, MD, William F. Anderson, MD, MPH, Craig D. Shriver, MD, Kangmin Zhu, MD, PhD  Journal of Thoracic Oncology  Volume 10, Issue 12, Pages (December 2015) DOI: /JTO Copyright © 2015 International Association for the Study of Lung Cancer Terms and Conditions

2 FIGURE 1 Calibration plots (observed probability versus predicted probability) for different time periods: (A) 1 year; (B) 2 years; (C) 3 years; (D) 5 years. Y-axis represents observed probability. X-axis represents predicted probability. The predicted and observed probabilities of survival are graphed on the horizontal and vertical axes, respectively. The grey line indicates the reference line, on which an ideal model would lie. Solid dots indicate the predictions; X's indicate the cross-validated predictions. Vertical bars indicate 95% confidence intervals around the prediction. Journal of Thoracic Oncology  , DOI: ( /JTO ) Copyright © 2015 International Association for the Study of Lung Cancer Terms and Conditions

3 FIGURE 2 Time-dependent receiver operating characteristic curves and area under curve for different time periods: (A) 1 year; (B) 2 years; (C) 3 years; (D) 5 years. Journal of Thoracic Oncology  , DOI: ( /JTO ) Copyright © 2015 International Association for the Study of Lung Cancer Terms and Conditions

4 FIGURE 3 Survival probability by risk score groups. Risk scores were assigned to each risk factor by dividing each regression coefficient by the smallest coefficient significantly different from 0, and then rounded to the nearest integer. A risk score was assigned to each patient by summing the points for each risk factor present. Risk score groups are defined as following: group 1: risk score = 0 to 9; group 2: risk score = 10 to 14; group 3: risk score = 15 to 19; group 4: risk score = 20 to 24; group 5: risk score = 25 to 29; group 6: risk score ≥ 30. Journal of Thoracic Oncology  , DOI: ( /JTO ) Copyright © 2015 International Association for the Study of Lung Cancer Terms and Conditions


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