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Peto Trend Test: Investigating The Impact Of Tumor Misclassification

Peto Trend Test: Investigating The Impact Of Tumor Misclassification. FDA/Industry Workshop. Outline. Study design Data structure Statistical methodology Misclassification of Tumors Methods: Assessment of misclassification Data and Permutation of Tumors Results – 3 Data sets

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Peto Trend Test: Investigating The Impact Of Tumor Misclassification

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  1. Peto Trend Test: Investigating The Impact Of Tumor Misclassification FDA/Industry Workshop

  2. Outline • Study design • Data structure • Statistical methodology • Misclassification of Tumors • Methods: Assessment of misclassification • Data and Permutation of Tumors • Results – 3 Data sets • Conclusions and Work in progress

  3. Long-term Oncogenicity Study Design • Studies involve both sexes of 2 rodent species • Exposure starts at 6-8 weeks of age • One control group + 3 dose groups • Exposure through various routes (Food, water, gavage, inhalation etc) Some interim sacrifices, controls are untreated or ‘vehicle’ control

  4. STUDY DESIGN/OBJECTIVES • Totest if exposure to increasing dose levels of compound leads to increase in tumor rates. • Design Criteria based on: • Dose levels • Randomization • Data collection/readings • Sample size • Study Duration

  5. DATA STRUCTURE • Animal ID • Organ and Tumor • Binary response indicator • 1 -> tumor found at given organ site • Time at which the tumor response was observed or death time. • Indicator defining Incidental or Fatal tumor.

  6. Data Structure

  7. Statistical Methods Complication: • Drug may affect the mortality of different groups Adjusting for differences in mortality is complex due to non-observable onset time of tumors. Assume: Death time is onset time for FATAL tumors

  8. Peto Test • Peto mortality–prevalence test • Modified Cochran-Armitage test • Computed like two Cochran-Armitage Z-score approximations • One for prevalence • One for mortality Assume: The two statistics are independent.

  9. Issue Of Misclassification • Analyses is biased if tumor lethality and cause of death is not valid/accurate • Pathologist are “stressed” about classifying tumors as incidental or fatal OBJECTIVE: To assess the impact of misclassification on the Peto Trend test

  10. How to Assess Impact ? • Simulating/bootstrapping the data with • Varying percentage of misclassification • Apply Peto trend test in all data sets [THIS APPROACH IS NOT EFFICIENT] • Permuting data sets • Create datasets with varying Peto p-values • Permute the membership of tumors in I or F • Apply Peto trend test for each permutation [USED THIS TECHNIQUE]

  11. Implementation Generateddatasets with Peto trend test p-values close to 0.005, 0.025 and 0.1. • 250 animals • 100 controls and 50 each in 3 dose groups • X number of incidental tumors • Y number of fatal tumors • Death time (for each animal)

  12. Permuting the Tumors • Find all combinations of • Changing incidental to fatal • One, two and three tumors at a time • Changing fatal to incidental • One, two and three tumors at a time • Simultaneous misclassification (I F) Compute the Peto trend test p-values for all permuted data sets.

  13. RESULTS • Dataset 1: Original Peto p-value = 0.0253 • Dataset 2: Original Peto p-value = 0.006 • Dataset 3: Original Peto p-value = 0.1038 Additional: • Dataset 4: Original Peto p-value = 0.0031 • Dataset 5: Original Peto p-value = 0.1067

  14. Survival in Data 1

  15. Data 1 - Tumor Incidence • Data 1 has 5 incidental and 7 fatal tumors • Initial Peto test p-value of 0.0253

  16. Data 1: All Combinations Of Two Tumors Changing From Incidental To Fatal

  17. Results - Data 1

  18. Original p-value = 0.0253 Graphical Results

  19. Original p-value = 0.0253 Graphical Results

  20. Original p-value = 0.0253 Original p-value = 0.0253 Graphical Results

  21. Data 2 - Tumor Incidence • Data 2 has 4 incidental and 9 fatal tumors • Initial Peto test p-value of 0.0060

  22. Results- Data 2

  23. Data 3 - Tumor Incidence • Data 3 has 8 incidental and 6 fatal tumors • Original Peto test p-value of 0.1038

  24. Data 3 - Survival

  25. Survival – Data 3 • p-value=0.1038 • 8 incidental, 6 fatal tumors

  26. Results- Data 3

  27. Data 3 - Animal death times

  28. Conclusions & Work in Progress • Mis-classification does not impact the original data findings. • Fatal to incidental seems to have (relatively) more of an effect – why? In Progress: • Early deaths in High dose group. • Opposing incidence trends for fatal and incidental tumors.

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