Parametric Survival Analysis of Adjuvant Therapy for Breast Cancer Using Both Clinical and Computer Generated Data

Robert L. Vogel, J. W. Gamel, Pinuccia Valngussa, Gianni Bonadonna

Research output: Contribution to journalArticlepeer-review

Abstract

Background. Standard, nonparametric statistical methods estimate only the impact of therapy on survival rate up to a selected follow-up interval. In contrast, parametric methods can estimate the impact of treatment on the two cardinal parameters of malignancy: likelihood of cure and recurrence free survival time among uncured patients.
Methods. The authors screened a total of six parametric survival models. Three of these, including the log normal model, were applied to survival data from five clinical trials of adjuvant therapy for Stage I1 breast cancer. For comparison, the log rank test, a standard nonparametric method, was also applied to the same data.
Results. Both parametric and nonparametric methods identified a significant therapeutic effect in three of the five trials. In only one of these three trials, however, did parametric analysis identify a significant difference in the likelihood of cure between treatment groups. In the remaining two trials, a significant difference was found in recurrence free survival time among uncured patients. The three parametric survival models gave similar results.
Conclusion. These findings suggest that parametric analysis may warrant further study as a method for measuring the long term clinical impact of adjuvant therapy on Stage I1 breast cancer.
Original languageAmerican English
JournalCancer
Volume74
StatePublished - Nov 1 1994

Disciplines

  • Public Health

Keywords

  • Adjuvant therapy
  • Breast Cancer
  • Clinical
  • Computer generated data
  • Parametric survival analysis

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