title: Disease-specific survival for limited-stage small-cell lung cancer affected by statistical method of assessment.
abstract: BACKGROUND
  In general, prognosis and impact of prognostic/predictive factors are assessed with Kaplan-Meier plots and/or the Cox proportional hazard model. There might be substantive differences from the results using these models for the same patients, if different statistical methods were used, for example, Boag log-normal (cure-rate model), or log-normal survival analysis.
  
  
  METHODS
  Cohort of 244 limited-stage small-cell lung cancer patients, were accrued between 1981 and 1998, and followed to the end of 2005. The endpoint was death with or from lung cancer, for disease-specific survival (DSS). DSS at 1-, 3- and 5-years, with 95% confidence limits, are reported for all patients using the Boag, Kaplan-Meier, Cox, and log-normal survival analysis methods. Factors with significant effects on DSS were identified with step-wise forward multivariate Cox and log-normal survival analyses. Then, DSS was ascertained for patients with specific characteristics defined by these factors.
  
  
  RESULTS
  The median follow-up of those alive was 9.5 years. The lack of events after 1966 days precluded comparison after 5 years. DSS assessed by the four methods in the full cohort differed by 0-2% at 1 year, 0-12% at 3 years, and 0-1% at 5 years. Log-normal survival analysis indicated DSS of 38% at 3 years, 10-12% higher than with other methods; univariate 95% confidence limits were non-overlapping. Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction significantly impacted DSS. DSS assessed by the Cox and log-normal survival analysis methods for four clinical risk groups differed by 1-6% at 1 year, 15-26% at 3 years, and 0-12% at 5 years; multivariate 95% confidence limits were overlapping in all instances.
  
  
  CONCLUSIONS
  Surgical resection, hemoglobin level, lymph node involvement, and superior vena cava (SVC) obstruction all significantly impacted DSS. Apparent DSS for patients was influenced by the statistical methods of assessment. This would be clinically relevant in the development or improvement of clinical management strategies.
authors.affiliation: University of Saskatchewan, Faculty of Medicine, Saskatoon, Saskatchewan, Canada. patricia.tai@scf.sk.ca
authors.forename: Patricia
authors.initials: P
authors.lastname: Tai
authors.forename: Judith-Anne W
authors.initials: JA
authors.lastname: Chapman
authors.forename: Edward
authors.initials: E
authors.lastname: Yu
authors.forename: Dennie
authors.initials: D
authors.lastname: Jones
authors.forename: Changhong
authors.initials: C
authors.lastname: Yu
authors.forename: Fei
authors.initials: F
authors.lastname: Yuan
authors.forename: Lee
authors.initials: L
authors.lastname: Sang-Joon
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D018288:Carcinoma, Small Cell / Q000401:mortality* / Q000556:secondary / Q000628:therapy; D015331:Cohort Studies; D015897:Comorbidity; D016001:Confidence Intervals; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality* / Q000628:therapy; D008207:Lymphatic Metastasis; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D012307:Risk Factors; D012525:Saskatchewan / Q000453:epidemiology; D013479:Superior Vena Cava Syndrome / Q000453:epidemiology; D016019:Survival Analysis; D015996:Survival Rate