title: Validation of survival prognostic models for non-small-cell lung cancer in stage- and age-specific groups.
abstract: OBJECTIVE
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States. Electronic address: xiaofei.wang@duke.edu.
authors: Xiaofei
authors: X
authors: Wang
authors: Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States.
authors: Lin
authors: L
authors: Gu
authors: Department of Biostatistics & Bioinformatics and Alliance Statistics and Data Center, Duke University, Durham, NC, United States.
authors: Ying
authors: Y
authors: Zhang
authors: Alliance Statistics and Data Center, Mayo Clinic, Rochester, MN, United States.
authors: Daniel J
authors: DJ
authors: Sargent
authors: Brigham and Women's Hospital, Boston, MA, United States.
authors: William
authors: W
authors: Richards
authors: Department of Internal Medicine, VA Nebraska Western Iowa Health Care System and University of Nebraska Medical Center, Lincoln, NE, United States.
authors: Apar Kishor
authors: AK
authors: Ganti
authors: Department of Medicine, Duke University Medical Center, Durham, NC, United States.
authors: Jeffery
authors: J
authors: Crawford
authors: Department of Medicine, Duke University Medical Center, Durham, NC, United States.
authors: Harvey Jay
authors: HJ
authors: Cohen
authors: Department of Medicine, University of North Carolina, Chapel Hill, NC, United States.
authors: Thomas
authors: T
authors: Stinchcombe
authors: Department of Medicine, University of Chicago, Chicago, IL, United States.
authors: Everett
authors: E
authors: Vokes
authors: 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.
authors: Herbert
authors: H
authors: Pang
mesh_terms: D000368:Aged; D002289:Carcinoma, Non-Small-Cell Lung / Q000401:mortality* / Q000473:pathology*; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging / Q000379:methods; D011379:Prognosis
keywords: Independent validation; Non-small-cell lung cancer (NSCLC); Performance status; Prognostic models