Xiaofei Wang1, Lin Gu2, Ying Zhang2, Daniel J Sargent3, William Richards4, Apar Kishor Ganti5, Jeffery Crawford6, Harvey Jay Cohen6, Thomas Stinchcombe7, Everett Vokes8, Herbert Pang9. 1. Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States. Electronic address: xiaofei.wang@duke.edu. 2. Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States. 3. Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, United States. 4. Brigham and Women's Hospital, Boston, MA, United States. 5. Department of Internal Medicine, VA Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Lincoln, NE, United States. 6. Department of Medicine, Duke University Medical Center, Durham, NC, United States. 7. Department of Medicine, University of North Carolina, Chapel Hill, NC, United States. 8. Department of Medicine, University of Chicago, Chicago, IL, United States. 9. Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
Abstract
PURPOSE: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. PATIENTS AND METHODS: The validation cohort included 2060 stage I and 1611 stage IV NSCLC patients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. RESULTS: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference=0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference=0.015 and 0.033, respectively. CONCLUSION: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.
PURPOSE: Prognostic models have been proposed to predict survival for non-small-cell lung cancer (NSCLC). It is important to evaluate whether these models perform better than performance status (PS) alone in stage- and age-specific subgroups. PATIENTS AND METHODS: The validation cohort included 2060 stage I and 1611 stage IV NSCLCpatients from 23CALGB studies. For stage I, Blanchon (B), Chansky (C) and Gail (G) models were evaluated along with the PS only model. For stage IV, Blanchon (B) and Mandrekar (M) models were compared with the PS only model. The c-index was used to assess the concordance between survival and risk scores. The c-index difference (c-difference) and the integrated discrimination improvement (IDI) were used to determine the improvement of these models over the PS only model. RESULTS: For stage I, B and PS have better survival separation. The c-index for B, PS, C and G are 0.61, 0.58, 0.57 and 0.52, respectively, and B performs significantly better than PS with c-difference=0.034. For stage IV, B, M and PS have c-index 0.61, 0.64 and 0.60, respectively; B and M perform significantly better than PS with c-difference=0.015 and 0.033, respectively. CONCLUSION: Although some prognostic models have better concordance with survival than the PS only model, the absolute improvement is small. More accurate prognostic models should be developed; the inclusion of tumor genetic variants may improve prognostic models.
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