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Cervical Cancer Case Study

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Cervical Cancer Case Study Eshetu Atenafu, Sandra Gardner, So-hee Kang, Anjela Tzontcheva University of Toronto Department of Public Health Sciences – PowerPoint PPT presentation

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Title: Cervical Cancer Case Study


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 gt follow up date
  • Decided there were no cases of left-censoring
  • N871, 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
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 (n549)
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
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20
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21
Log-normal Model Prognostic Groups
22
Comparison of Prognostic Groups
23
Conclusions (1)
  • Important prognostic factors are
  • tumor size gt3cm
  • 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

24
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.

25
Another Cohort
  • Ishikawa H. et al. (1999) Prognostic Factors of
    Adenocarcinoma of the Uterine Cervix, Gynecologic
    Oncology 7342-46
  • Nakanishi T. et al. (2000) A Comparison of
    Prognoses of Pathologic Stage 1b Adenocarinoma
    and Squamous Cell Carcinoma of the Uterine
    Cervix, Gynecologic Oncology 79289-293
  • 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
    10397-401

26
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)
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