Research article

Disease-specific survival for limited-stage small-cell lung cancer affected by statistical method of assessment

Patricia Tai¹ , Judith-Anne W Chapman² , Edward Yu³ , Dennie Jones⁴ , Changhong Yu² , Fei Yuan² and Lee Sang-Joon⁵

¹University of Saskatchewan, Faculty of Medicine, Saskatoon; Department of Radiation Oncology, Regina, Saskatchewan, Canada

²National Cancer Institute of Canada Clinical Trials Group, Queen's University, Kingston, Canada

³Division of Radiation Oncology, Department of Oncology, University of Western Ontario, London, Ontario, Canada

⁴University of New Mexico, Division of Hematology/Oncology, Cancer Research and Treatment Center, Albuquerque, New Mexico, USA

⁵University of New Mexico, Department of Internal Medicine, Division of Epidemiology and Biostatistics, Albuquerque, New Mexico, USA

author email corresponding author email

BMC Cancer 2007, 7:31doi:10.1186/1471-2407-7-31

Published:	20 February 2007

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.

Conclusion

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.