anti-infective drugs advisory committee meeting december 15, 2006 1 data mining analysis of multiple...

1Anti-Infective Drugs Advisory Committee MeetingAnti-Infective Drugs Advisory Committee MeetingDecember 15, 2006December 15, 2006

Data Mining Analysis of Multiple Data Mining Analysis of Multiple Antibiotics in AERSAntibiotics in AERS

Data Mining Analysis of Multiple Data Mining Analysis of Multiple Antibiotics in AERSAntibiotics in AERS

Jonathan G. Levine, PhDMathematical Statistician

Office of Critical Path Programs Office of the Commissioner

FDA

and

Ana Szarfman, MD, PhDMedical Officer

Division of Cardiovascular and Renal ProductsOffice of New Drugs

and Division of Biometrics VI, Office of Biostatistics

Office of Translational SciencesCDER, FDA

Jonathan G. Levine, PhDMathematical Statistician

Office of Critical Path Programs Office of the Commissioner

FDA

and

Ana Szarfman, MD, PhDMedical Officer

Division of Cardiovascular and Renal ProductsOffice of New Drugs

and Division of Biometrics VI, Office of Biostatistics

Office of Translational SciencesCDER, FDA


What is data mining?What is data mining?What is data mining?What is data mining?

• In general: Statistical analysis applied to large databases without any a priori hypotheses.

• In this case: Using the MGPS algorithm to analyze all suspect drug and adverse event pairs in the AERS database.

• I will briefly discuss AERS and MGPS; details are in the review contained in the briefing package.

• In general: Statistical analysis applied to large databases without any a priori hypotheses.

• In this case: Using the MGPS algorithm to analyze all suspect drug and adverse event pairs in the AERS database.

• I will briefly discuss AERS and MGPS; details are in the review contained in the briefing package.


What is the AERS DatabaseWhat is the AERS DatabaseWhat is the AERS DatabaseWhat is the AERS Database

• Computerized adverse case reporting system– Voluntary reporting by health care workers and the

general public.– Mandatory reporting by manufacturers for serious,

unexpected events• Adverse event reports

– Coded according to the standardized terminology of the Medical Dictionary for Regulatory Activities (MedDRA)

– Over 3 million reports from 1968 to the present.– Small number of data elements (drugs, events, age, sex,

etc.)– Lots of missing data

• Computerized adverse case reporting system– Voluntary reporting by health care workers and the

general public.– Mandatory reporting by manufacturers for serious,

unexpected events• Adverse event reports

– Coded according to the standardized terminology of the Medical Dictionary for Regulatory Activities (MedDRA)

– Over 3 million reports from 1968 to the present.– Small number of data elements (drugs, events, age, sex,

etc.)– Lots of missing data


Disproportionality Analysis Using Disproportionality Analysis Using DuMouchel’s MGPS MethodDuMouchel’s MGPS Method

Disproportionality Analysis Using Disproportionality Analysis Using DuMouchel’s MGPS MethodDuMouchel’s MGPS Method

• Calculate observed and expected number of reports for a particular drug-event combination.

Observed rate = Number of reports for event X with drug Y Number of reports for drug Y

Expected rate = Number of reports for event X in AERS Number of reports in AERS

Reporting Ratio (RR)= Observed rate Expected rate

• “Shrink” the RR towards 1. The shrunk RR is referred to as the EBGM score.

• The amount of shrinkage is a function of the amount of information in AERS about the drug-event combination.

• Calculate observed and expected number of reports for a particular drug-event combination.

Observed rate = Number of reports for event X with drug Y Number of reports for drug Y

Expected rate = Number of reports for event X in AERS Number of reports in AERS

Reporting Ratio (RR)= Observed rate Expected rate

• “Shrink” the RR towards 1. The shrunk RR is referred to as the EBGM score.

• The amount of shrinkage is a function of the amount of information in AERS about the drug-event combination.


Why Shrink RRs?Why Shrink RRs?Why Shrink RRs?Why Shrink RRs?

• Expected counts are often so small that a single report will yield a huge RR– Example: Acetaminophen has one report for

“Alice in wonderland syndrome” – The expected number of cases is

approximately 0.011– RR= 89.4 – EBGM = 1.37– Shrinking dramatically reduces the false

positive rate

• Expected counts are often so small that a single report will yield a huge RR– Example: Acetaminophen has one report for

“Alice in wonderland syndrome” – The expected number of cases is

approximately 0.011– RR= 89.4 – EBGM = 1.37– Shrinking dramatically reduces the false

positive rate


Drug-Event Combinations Drug-Event Combinations AnalyzedAnalyzed

Drug-Event Combinations Drug-Event Combinations AnalyzedAnalyzed

• Drugs Selected by Division of Antiinfective and Ophthalmic Products

• We considered the results for all adverse events in the AERs database.

• Only adverse events with at least one of the selected drugs having EBGM >=2 and an N>=2 for at least one cumulative time period were analyzed in detail.

• Removed adverse events most likely related to the indications being treated (e.g., pneumonia, meningitis, otitis, pain).

• Selected the event codes that reflected a more severe problem (e.g., we selected “Hepatic failure” instead of “Aspartate Aminotransferase Increased”, “Toxic epidermal necrolysis” instead of “Rash”).

• Drugs Selected by Division of Antiinfective and Ophthalmic Products

• We considered the results for all adverse events in the AERs database.

• Only adverse events with at least one of the selected drugs having EBGM >=2 and an N>=2 for at least one cumulative time period were analyzed in detail.

• Removed adverse events most likely related to the indications being treated (e.g., pneumonia, meningitis, otitis, pain).

• Selected the event codes that reflected a more severe problem (e.g., we selected “Hepatic failure” instead of “Aspartate Aminotransferase Increased”, “Toxic epidermal necrolysis” instead of “Rash”).


Ranking by Max EBGM for Ranking by Max EBGM for Recoded EventsRecoded Events

Ranking by Max EBGM for Ranking by Max EBGM for Recoded EventsRecoded Events

• This data reduction left us with 168 adverse events terms and 6 serious outcomes for the 16 drugs, a total of 2,784 possible EBGM values.

• How to present this many estimates?• We chose to reduce the dimensionality of the

problem by:– Grouping similar Adverse Events– Looking at maximum EBGM value over both

adverse event group and cumulative year subset.

• This data reduction left us with 168 adverse events terms and 6 serious outcomes for the 16 drugs, a total of 2,784 possible EBGM values.

• How to present this many estimates?• We chose to reduce the dimensionality of the

problem by:– Grouping similar Adverse Events– Looking at maximum EBGM value over both

adverse event group and cumulative year subset.


ResultsResultsResultsResults

• Insufficient time to discuss all results• Only summary conclusions for 11 selected

adverse event groups will be provided in this presentation.

• Details are presented in the review provided in the briefing package.

• Insufficient time to discuss all results• Only summary conclusions for 11 selected

adverse event groups will be provided in this presentation.

• Details are presented in the review provided in the briefing package.


EBGMs for Selected Drug-Event EBGMs for Selected Drug-Event CombinationsCombinations

EBGMs for Selected Drug-Event EBGMs for Selected Drug-Event CombinationsCombinations

Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

16 Drugs Selected by DAIOP16 Drugs Selected by DAIOP16 Drugs Selected by DAIOP16 Drugs Selected by DAIOP


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Selected Serious Event GroupsSelected Serious Event GroupsSelected Serious Event GroupsSelected Serious Event Groups


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Color Coding of EBGM ScoresColor Coding of EBGM ScoresColor Coding of EBGM ScoresColor Coding of EBGM Scores


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Eye Events and MyastheniaEye Events and MyastheniaEye Events and MyastheniaEye Events and Myasthenia


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

SyncopeSyncopeSyncopeSyncope


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Hepatic Failure and HepatitisHepatic Failure and HepatitisHepatic Failure and HepatitisHepatic Failure and Hepatitis


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

CholestasisCholestasisCholestasisCholestasis


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Drug Interaction and Drug Drug Interaction and Drug IneffectiveIneffective

Drug Interaction and Drug Drug Interaction and Drug IneffectiveIneffective


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Clostridial InfectionClostridial InfectionClostridial InfectionClostridial Infection


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Toxic Skin ReactionsToxic Skin ReactionsToxic Skin ReactionsToxic Skin Reactions


Cutpoints:

EBGM 0 195.24

<=1.5 <=2 <=4 >4Min. Max.

1.5 2 4

Hypersensitivity ReactionsHypersensitivity ReactionsHypersensitivity ReactionsHypersensitivity Reactions


ConclusionsConclusionsConclusionsConclusions

• There is an unusually large signal for eye events with telithromyicin

• There is an unusually large signal for myasthenia with telithromyicin

• The large signal for telithromyicin and syncope is second only to the signal for moxifloxacin

• There is an unusually large signal for eye events with telithromyicin

• There is an unusually large signal for myasthenia with telithromyicin

• The large signal for telithromyicin and syncope is second only to the signal for moxifloxacin



• “Hepatic Failure” and “Hepatitis” both have signals with telithromycin– Hepatic Failure has a signal less than

trovafloxacin and nitrofurantoin, but comparable to amoxicillin and clavulanate

– Hepatitis has a signal comparable to trovafloxacin, nitrofurantoin, and amoxicillin and clavulanate

• “Cholestasis” has a weak signal with telithromycin.– The majority of antibiotics have a stronger

signal for cholestasis

• “Hepatic Failure” and “Hepatitis” both have signals with telithromycin– Hepatic Failure has a signal less than

trovafloxacin and nitrofurantoin, but comparable to amoxicillin and clavulanate

– Hepatitis has a signal comparable to trovafloxacin, nitrofurantoin, and amoxicillin and clavulanate

• “Cholestasis” has a weak signal with telithromycin.– The majority of antibiotics have a stronger

signal for cholestasis



• “Drug Interaction” has a high signal score with telithromycin, – Azithromycin, clarithromycin, and

erythromycin all have higher signal scores than telithromycin.

• “Toxic Skin” and “Hypersensitivity Reaction” have weak signals for telithromycin compared to the majority of other antibiotics.

• “Drug Ineffective” and “Clostridial Infection” do not have signals for telithromycin

• “Drug Interaction” has a high signal score with telithromycin, – Azithromycin, clarithromycin, and

erythromycin all have higher signal scores than telithromycin.

• “Toxic Skin” and “Hypersensitivity Reaction” have weak signals for telithromycin compared to the majority of other antibiotics.

• “Drug Ineffective” and “Clostridial Infection” do not have signals for telithromycin

24

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Anti-Infective Drugs Advisory Committee in Joint Session with the Anti-Infective Drugs Advisory Committee in Joint Session with the Drug Safety and Risk Management Advisory Committee. Drug Safety and Risk Management Advisory Committee. December 14 & 15, 2006December 14 & 15, 2006

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