1. Cervical Cancer Case Study Supervising Professor: Dr. P.D.M. Macdonald Team Members: Christine Calzonetti, Simo Goshev, Rongfang Gu, Shahidul Mohammad Islam, Amanda Lafontaine, Marcus Loreti, Maria Porco, William Volterman, Qihao Xie -McMaster University-

2. To determine which of the documented variables are useful for predicting recurrence of the disease To evaluate the extent to which tumor size, in particular, predicts the recurrence of the disease Objectives:

3. Graphical Analysis

4. The majority of patients observed were between the ages of 35 and 50 No significant difference between relapse and non-relapse patients MeanMedian Standard deviation Non-relapse 42.084011.04 Relapse 42.043911.17

5. Similar means Dissimilar boxplots possibly due to outliers Missing values in the relapse group may have affected the outcome MeanMedian Standard deviation Non-relapse 6.7656.83 Relapse 7.711110.11

6. A great disparity exists between the means and variability of relapse and non-relapse patients Relapse patients had larger tumor sizes upon diagnosis, suggesting that tumor size should be considered an important prognostic factor MeanMedian Standard deviation Non-relapse 8.07010.17 Relapse 18.862016.31

7. The difference in pie charts indicates that there are more cancerous cells found in the lymph nodes of patients who relapsed The statistical significance is unclear

8. Relapse patients had a greater quantity of cells deemed “worse”

9. Recorded at the time of follow up appointment (therefore cannot be used as a diagnostic factor) Most non-relapse patients have no presence of disease at last follow up appointment In relapse patients, approx. ½ died of disease, ¼ are alive with disease, ¼ are alive with no evidence of disease

10. Results and Conclusions

11. Survival Plot of Cervical Cancer Data Survival plot of data indicates that most relapses occur during the first three years after surgery, it is highly unlikely that relapse will occur after eight years The exponential curve deviated away from the survival curve at the tail end due to the patients who will never relapse

13. Time Small (0-10mm) Medium (11-30mm) Large (30+mm) Recurrence time for large group considerably lower than medium Clear distinction between medium and small The patients in the different size groups had noticeably different mean times to recur

15. Survival Analysis yielded the following results: Significant difference between medium and small groups Significant difference between large and small groups Same results found using Weibull distribution in place of exponential distribution A survival analysis of the data on S-Plus where the exponential distribution was assumed produced the following output: Value Std. Error z p (Intercept) 9.275 0.128 72.21 0.00e+000 cutsize1 -0.552 0.139 -3.97 7.06e-005 cutsize2 -0.670 0.100 -6.67 2.48e-011

16. A step-wise regression analysis of the data on S-Plus where the exponential distribution was assumed produced the following output: Value Std. Error z p (Intercept) 11.0113 0.8091 13.6092 3.53e-042 cutsize1 -0.0615 0.1796 -0.3425 7.32e-001 cutsize2 -0.3278 0.1618 -2.0256 4.28e-002 lymph -0.7694 0.4661 -1.6508 9.88e-002 depth -0.0703 0.0146 -4.7977 1.61e-006 grad -0.5229 0.2063 -2.5349 1.12e-002 age 0.0142 0.0145 0.9758 3.29e-001 rad 0.0245 0.2966 0.0827 9.34e-001 Regression Analysis yielded the following results: Initial variables: Final variables: Value Std. Error z p (Intercept) 11.5989 0.5526 20.99 8.03e-098 cutsize1 -0.0660 0.1786 -0.37 7.12e-001 cutsize2 -0.3292 0.1609 -2.05 4.08e-002 lymph -0.7735 0.3973 -1.95 5.16e-002 depth -0.0666 0.0141 -4.71 2.46e-006 grad -0.5378 0.2063 -2.61 9.15e-003

17. Initial analysis showed that possible prognostic factors were Size, Lymph Nodes, Tumor Depth and Cell Grade Cox’s Proportional Hazard reaffirmed that Size, Depth and Cell Grade were important diagnostic factors, but Lymph Nodes are only significant at the 10% level