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Comparison of data mining methods for detection of adverse events associated with use of Herceptin
Efstathia Polychronopoulou, MPH Preventive Medicine and Community Health University of Texas Medical Branch
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Background Post-marketing pharmacovigilance relies on spontaneous reporting systems to detect rare adverse events Reporting bias Inaccurate or duplicate reports Lack of accurate “denominator” Observational datasets can enhance drug safety surveillance, but there has been limited use.
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Study objective Compare data mining methods for detection of adverse events associated with use of Herceptin using an administrative claims dataset
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Methodology Data source:
SEER and Texas Cancer Registries linked to Medicare ( ) Inclusion criteria: Women > 66 years at Breast Cancer diagnosis 1 yr. continuous enrollment before diagnosis and during follow-up Confirmed HER2 status Received chemotherapy or chemotherapy + Herceptin
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Outcome – Adverse Events (AEs)
All inpatient diagnoses post-treatment Primary outpatient diagnoses, excluding chemotherapy / radiation Follow-up time: 3 months, 6 months or 1 year Clinical Classification Software: each ICD-9 code mapped to a clinical category
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Subject – level analysis
Data mining methods Approach 1 Approach 2 Subject – level analysis For each AE, all subjects with a claim All unexposed subjects contribute to the “denominator” Visit - level analysis All occurrences of an AE for each subject All claims from unexposed subjects contribute to the “denominator”
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Proportional Reporting Ratio (PRR)
Disproportionality measure based on 2x2 contingency table Signaling threshold : PRR > 2 and 95% lower CI > 1 Evans et al. (2001)
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Gamma Poisson Shrinker (GPS)
Number of adverse events: Y i,j ~ Poisson ( 𝝁 𝒊,𝒋 ) where i = drug, j = adverse-event and 𝝁 𝒊,𝒋 = 𝝀 𝒊,𝒋 ·𝚬 [𝒀 𝐢,𝒋 ] 𝝀 𝒊,𝒋 a mixture of two Gamma distributions EBGM: Geometric Mean of posterior λ distribution Signaling threshold: 95% LCI > 1.5 Retrieved from Madigan et. al, 2010 . Dumouchel (1999)
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Tree – based scan statistic (TBSS)
Retrieved from Wang S. et al., 2018 Events defined on a hierarchical tree Log-Likelihood ratio: evaluates simultaneously all possible cuts on all branches Inference using Monte Carlo simulation Adjusts for multiple testing Signal threshold p-value < 0.05 Kulldorff et al. (2003)
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Study cohort characteristics
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Subject – level analysis
3 months Chemotherapy N = 5,150 + Herceptin N = 1,494 PRR TBSS Septicemia Aortic valve disorders Other heart disease Duodenal ulcer Hernia Disorders of teeth/jaw Cellulitis Arthritis/osteomyelitis Acquired deformities Poisoning by other medications Heart Valve disorders Mitral valve Other non-rheumatic valve GPS Diseases of circulatory system Diseases of heart Cardiomyopathy Peri, -endo, -myo carditis
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Subject – level analysis
6 months Chemotherapy N =4,865 + Herceptin N = 1,447 PRR GPS TBSS Heart valve disorders Chronic rheumatic Aortic Mitral Other Other heart disease Peri, -endo, -myo, carditis Cardiomyopathy Pneumonia Emphysema Duodenal ulcer Skin ulcer Arthritis/ osteomyelitis Upper limb fracture Respiratory complications Poisoning by other drugs Diseases of circulatory system Diseases of heart Anemia Complications
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Subject – level analysis
12 months Chemotherapy N = 4,181 + Herceptin N = 1,259 PRR GI laceration Duodenal ulcer Skin ulcer Cancer head/neck Liver malignancy Other pericarditis GPS Heart valve disorders Aortic Mitral Other Other heart disease Peri, -endo, -myo carditis Cardiomyopathy TBSS Chronic rheumatic heart valve Diseases of circulatory system Diseases of heart
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Visit – Level Analysis PRR GPS TBSS 3 months Skin ulcer Duodenal ulcer
Heart valve disorders Mitral Other Peri, -endo, -myo carditis Cardiomyopathy Poisoning by other drugs Diseases of heart GI disorders Skin ulcer Duodenal ulcer Asthma Anemia Upper limb fracture Hernia Septicemia Acquired deformities Other heart disease Cancer of nervous system Central nervous systems disorder Otitis media Nausea Lymphatics Cancer of thyroid PRR GPS TBSS Hypovolemia 3 months Arthritis / osteomyelitis Disorders of teeth/jaw
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Visit – Level Analysis TBSS GPS PRR 6 months Nausea Anemia
Aortic valve Hypovolemia TBSS GPS Electrolyte disorders Diseases of the heart Diseases of the circulatory system GI disorders Heart valve disorders Mitral Other Chronic rheumatic Peri, -endo, -myo carditis Cardiomyopathy Other heart disease Poisoning by other drugs Otitis media 6 months Skin ulcer Hernia Acquired deformities Cancer of thyroid Cancer of nervous system GI ulcer GI laceration Duodenal ulcer Pneumothorax PRR
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Visit – Level Analysis GPS TBSS PRR 12 months Anemia Nausea
Hypovolemia Central nervous system disorders GPS Upper limb fracture TBSS Heart valve disorders Chronic rheumatic Mitral Aortic Other Peri, -endo, -myo carditis Cardiomyopathy Other heart disease Poisoning by other drugs Skin ulcer Duodenal ulcer Otitis media Electrolyte disorders Diseases of the heart Diseases of circulatory system Heart failure Anal/rectal conditions 12 months Arthritis/osteomyelitis Disorders of teeth/jaw PRR GI laceration Gi ulcer Cancer of thyroid Cancer of head/neck Cancer of uterus
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Summary Signals consistent across all methods and types of analyses
Signals detected with visit - level analysis only
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Conclusions Visit – level analysis may be more appropriate for unknown long-term AEs Subject – level analysis may serve a confirmatory purpose for suspected AEs PRR is more sensitive to small event counts and false positives GPS is the more conservative method and may not detect rare events TBSS and PRR may be more useful as an initial signal detection step
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Acknowledgments Dr Sharon Giordano, MD Anderson Cancer Center
Lin-Na Chou, MS, UTMB Dr Xiaoying Yu, UTMB Dr Yong-Fang Kuo, UTMB This work was supported by grant RP awarded from the Cancer Prevention and Research Institute of Texas.
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References Harpaz R., DuMouchel W., Shah NH et al., Novel Data Mining Methodologies for Adverse Drug Event Discovery and Analysis. Clin Pharmacol Ther, 2012; 91(6): Evans S.J.W., Waller P.C., Davis S., Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports. Pharmacoepidemiology and Drug Safety, 2001; 10(6): 483-6 DuMouchel W., Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System. The American Statistician, 1999; 53(3): Kulldorff M., Fang Z.., Walsh SJ. A Tree-Based Scan Statistic for Database Diseases Surveillance. Biometrics, 2003; 59 (2): Wang SV, Gagne JJ, Maro JC et al. Development and Evaluation of a Global Propensity Score for Data Mining with Tree-Based Scan Statistics. Sentinel Methods Protocol, 2018. Madigan D., Ryan P., Simpson S. et al. Bayesian Methods in Pharmacovigilance. Oxford University Press, 2010.
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