1 / 26

Cervical Cancer Case Study

Cervical Cancer Case Study. Eshetu Atenafu, Sandra Gardner, So-hee Kang, Anjela Tzontcheva University of Toronto Department of Public Health Sciences (Biostatistics) Acknowledgments: Professors P.Corey, J. Hsieh, W. Lou, J.Stafford. Outcome Variable.

mckenzie-kemp
Download Presentation

Cervical Cancer Case Study

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Cervical CancerCase Study Eshetu Atenafu, Sandra Gardner, So-hee Kang, Anjela Tzontcheva University of Toronto Department of Public Health Sciences (Biostatistics) Acknowledgments: Professors P.Corey, J. Hsieh, W. Lou, J.Stafford

  2. Outcome Variable • Time to event calculated as recurrence date - surgery date; otherwise censored at death or last follow up date • 4 cases where recurrence date > follow up date • Decided there were no cases of left-censoring • N=871, 68 recurrent events, 92% censored for a total of 3,573 person-years of follow up over the time period of 1984 to 2001

  3. PELLYMPH AGE - 40 SURGYR - 1993 ADJ_RAD MAXDEPTH MARGINS HISTOLOG CLS 0 1 non treated 0 1  1cm >1cm 0 1 clear other HIST 1 (SCC) 0 1 - + HIST 3 (AC) SIZE GRADE 0 1 3cm >3cm GRADE 2 GRADE 3 Covariate manipulation

  4. Covariate Summary (1) • Age - median 40 years • 3% with disease left after surgery • 13% received radiation therapy • 46% capillary-lymphatic space invasion • 6% positive pelvic lymph nodes • Histology • SCC 62%, AC 28%

  5. Covariate Summary (2) • Tumor grade (cell differentiation) • better 21%, moderate 52%, worst 27% • Maximum depth of tumor • 22% greater than 1 cm • Tumor size • 5% greater than 3 cm • Median year of surgery is 1993

  6. Methods • Univariate log-rank tests • Non-parametric survival trees (CART-SD) • Semi-parametric (Cox regression) • Parametric models (Exponential, Weibull, Log-normal)

  7. Log-rank tests

  8. Using all available data per variable

  9. Complete data (n=549)

  10. MAXDEPTH • Loss of power concerns • We are losing 23 recurrent event cases due to missing Maxdepth and only 4 for other missing covariates • We developed models including and excluding Maxdepth • Attempted imputation of all missing values (TRANSCAN and IMPUTE, Design and Hmisc S-plus/R libraries, F.Harrell)

  11. Survival Trees • Builds a binary decision tree and groups patients with similar prognosis • Uses maximized version of Log-rank test to split the data into groups with different survival • Advantages: non-parametric, “ranks” covariates by importance, captures interactions • Disadvantages: non-interpretability of large trees, excludes cases with missing values

  12. Survival Tree including Maxdepth

  13. Survival Tree excluding Maxdepth

  14. Comparisons of Cox models

  15. Using imputed data

  16. Using all available data per variable

  17. Model Comparison

  18. Exponential Model Prognostic Groups

  19. Log-normal Model Prognostic Groups

  20. Comparison of Prognostic Groups

  21. Conclusions (1) • Important prognostic factors are: • tumor size >3cm • capillary-lymphatic space invasion • positive pelvic lymph nodes • Squamous cell carcoma type histology • Missing values and imputation issues with respect to maximum depth of tumor are of concern

  22. Conclusions (2) • We have selected 3 prognostic groups using non-parametric and parametric methods • Parametric models appear to overestimate the 5 year survival probability for the high risk group • Non-parametric and parametric 5 years survival estimates for the prognostic groups are similar, but the parametric models group fewer patients for high and moderate risk compared to the survival tree • We are concerned, however, that the predictive ability of these models is poor.

  23. Another Cohort • Ishikawa H. et al. (1999) Prognostic Factors of Adenocarcinoma of the Uterine Cervix, Gynecologic Oncology 73:42-46 • Nakanishi T. et al. (2000) A Comparison of Prognoses of Pathologic Stage 1b Adenocarinoma and Squamous Cell Carcinoma of the Uterine Cervix, Gynecologic Oncology 79:289-293 • Nakanishi T. et al. (2000) The significance of tumor size in clinical stage 1b cervical cancer: Can a cut-off figure be determined?, International Journal of Gynecologic Cancer 10:397-401

  24. References • LeBlanc, M. and Crowley J. (1993) Survival Trees by Goodness of Split. JASA 88: 457-467 • Segal, M. R.(1988) Regression Trees for Censored Data. Biometrics 44: 35-47 • Lausen B and Schumacher M. (1992) Maximally Selected Rank Statistics. Biometrics 48: 73-85 • Haupt G. Survival Trees in S-plus (library survcart demo)

More Related