LEADING RESEARCH… MEASURES THAT COUNT Challenges of Studying Cardiovascular Outcomes in ADHD...

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LEADING RESEARCH… MEASURES THAT COUNT Challenges of Studying Cardiovascular Outcomes in ADHD Elizabeth B. Andrews, MPH, PhD, VP, Pharmacoepidemiology and Risk Management Drug Safety and Risk Management Advisory Committee Meeting February 9, 2006

Transcript of LEADING RESEARCH… MEASURES THAT COUNT Challenges of Studying Cardiovascular Outcomes in ADHD...

Page 1: LEADING RESEARCH… MEASURES THAT COUNT Challenges of Studying Cardiovascular Outcomes in ADHD Elizabeth B. Andrews, MPH, PhD, VP, Pharmacoepidemiology and.

LEADING RESEARCH… MEASURES THAT COUNT

Challenges of Studying Cardiovascular Outcomes in ADHD

Elizabeth B. Andrews, MPH, PhD,VP, Pharmacoepidemiology and Risk Management

Drug Safety and Risk Management Advisory Committee Meeting

February 9, 2006

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Key Questions for Today

• What is the absolute risk of MI, stroke, and sudden death in users of ADHD medicines?

• Is the risk of MI, stroke, and sudden death increased in users of ADHD medicines compared with the risk in the general population?

• Is the risk of MI, stroke, and sudden death increased in users of ADHD medicines compared with the risk in individuals with ADHD and not treated with these medications?

• Do the risks differ across drugs for ADHD?• What is the upper limit of the potential increased risk, given

limits of study feasibility?

• What are the consequences of the answers to these questions?

ADHD = attention deficit hyperactivity disorderMI = myocardial infarction

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Start With the Goal

• We should measure the risk difference, not the relative risk.

• What added risk has public health or policy significance, given the benefits of treatment?

• What level of increased risk would be acceptable to patients and their families?

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Ideal Study

Subject Selection• Individuals with ADHD• Individuals with ADHD who use ADHD medicines• General population comparison

Exposure Measurement• Exact dose and duration of treatment

Outcome Measurement• Identify and characterize 100% of cases of MI, stroke, and sudden death

Risk Factor Measurement• Characterize all study members on prior medical history, including cardiac

abnormalities• Capture information on all potential confounders, including substance abuse

Follow-up• Complete follow-up of all patients for multiple years

Analysis• Unbiased comparisons of differences across all groups of interest

Power or Precision• Sufficient size to rule out relatively small increases in risk

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Subject Selection

• Individuals treated with ADHD medicines• Individuals with documented ADHD only?• Comparison group not treated with ADHD

medicines• Pediatrics and adults• Lower and upper age limits?

• Exclusions• Underlying cardiac abnormality, if known?• History of cardiovascular disease• Other (e.g., cancer)

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Exposure

Medications

• All drugs for ADHD?

• Specific medications

• Stimulants• Methylphenidate• Amphetamine• Dexmethylphenidate • Dextroamphetamine • Methamphetamine

• Atomoxetine

• Other drugs used in ADHD?

• Incident use vs. prevalent use

• Select users with no prior recorded use

• Multiple medicines for same patient

• Capture all use, record start date of all new uses, estimate end date

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Outcomes

Outcomes of interest• MI

• Stroke

• Sudden death

Common Features• Rates show similar patterns by age

• Rare in children in age-range for ADHD treatment (<3/100,000 p-y)

• More common in adults (1/1000 p-y)

• Risk factors show similar patterns by age

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Myocardial Infarction

• Most cases result in medical attention.

• Cases can be identified through medical claims.

• Prior studies have demonstrated high positive predictive value (PPV) for claims algorithms in adults.

• There is no need to validate diagnoses through chart abstraction.

• Cases not resulting in medical attention will be missed in most claims databases.

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Stroke

• Most cases result in medical attention.• Cases can be identified through medical claims. • Prior studies have demonstrated high positive

predictive value (PPV) for claims algorithms in adults.

• Claims alone may not be sufficient to distinguish between ischemic and hemorrhagic stroke.

• Important to distinguish between new and repeat stroke (>20% of hospitalizations for stroke are repeat).

• Cases not resulting in medical attention will be missed in most claims databases.

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Sudden Death

• Many cases do not result in medical attention.• If exposure cohorts are followed via health records, need to

link with vital records to ascertain deaths.• Death certificates may not have sufficient accuracy to

differentiate between deaths of cardiovascular origin from other deaths.

• Death certificate cause of death will be more accurate if it mentions that an autopsy was performed.

• Coroner’s reports are ideal for determining circumstances of death (Autopsies will be performed for most cases of sudden death in children).

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Risk Factors - Partial List

• Age • Diabetes

• Gender • Coagulation disorders

• Race • Medications

• Smoking • Hyperlipidemia

• Substance abuse • Epilepsy

• Hypertension • Obesity

• Cardiac abnormalities

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Source: Epidemiology: An Introduction, Kenneth J. Rothman, 2002

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Design

• Retrospective cohort study (MI, stroke, sudden death)

• Include all relevant incident exposures

• Create operational definitions of risk periods consistent with use and prescribing patterns

Drug A

Drug ADrug B

No ADHD Drug

Pre-exposure time Exposure time Follow up until event or end of study

Drug ADrug B

Stroke

Stroke

Stroke

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Design Alternative (Case-Control Study)

• Identify all cases within defined population

• Sample controls from same population

• Create operational definitions of exposure periods relevant to outcomes

Drug A

Drug ADrug B

No ADHD Drug

No ADHD DrugDrug B

Stroke

Stroke

Stroke

Stroke

No StrokeDrug A

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Data Collection Strategy

• Data available in electronic claims• Age, sex• Exposures (ADHD medications)• Outcomes (MI, stroke)

• Combination of diagnosis codes, procedures, prescriptions• Many potential confounders (conditions, medications)• Observation time before and after incident exposure

• Data not available in electronic claims • Death, cause of death• Smoking• BMI• Over-the-counter aspirin use• Substance abuse• Race

BMI = body mass index

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Supplemental Data Collection

Death

• Linkage with vital records to determine death

• Coroner’s report for cause and circumstances of death, cardiac abnormalities

Medical record abstraction?

• Classification of stroke type when not clear from claims (i.e., no Rx for anticoagulants) if stroke type is important distinguish

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Supplemental Data Collection

Patient-based survey for race,

BMI, smoking, aspirin

use, substance abuse

• Not captured in claims

• Not captured in medical records

• Accuracy greatest if patient self-report

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How Many Studies or Analyses?

• Risks and risk factors may differ between pediatric and adult populations.• Example: cardiac abnormality in pediatrics,

cigarette smoking in adults

• Risk factors may differ across the three outcomes of interest. • Example: epilepsy for sudden death

• Six distinct sets of analyses will be required.

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Analysis Plan

• Measure: incidence of each event (#cases/person-time)

• Compare incidence by exposure category

• Stratify on age, gender, covariates if possible

• In adults, model incidence ratio for ADHD drugs vs. general population using multivariable model, controlling for measured confounders

• Compare drugs if appropriate

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Adjust for Important Unmeasured Confounders

Use external adjustment to control for for confounders measured in survey

• Obtain prevalence of confounders (e.g., smoking) in ADHD from patient-based survey

• Obtain prevalence of confounders in general population from literature

• Obtain estimates of association between confounders and outcomes (e.g., smoking and MI) from literature

• Adjust and conduct sensitivity analyses

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Study Size

Study size needed will be driven by outcome rates, but restricted by availability of data.

• Outcomes are extremely rare in young populations (<2/100,000).

• Study may be able to establish upper bound of the potential increased risk.

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For Event With 3/100,000 Over 3 Years

Probability that the upper limit of the confidence limit will be less than 395% Confidence levelExpected Relative Risk = 1Allocation Ratio: 1 unexposed for each 1 exposed

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Study Size

• Outcomes more common in adults – study may be able to compare risks between drugs and general population and among drugs.

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For Event With 3/1,000 Over 3 Years

Probability that the upper limit of the confidence limit will be less than 295% Confidence levelExpected Relative Risk = 1Allocation Ratio: 1 unexposed for each 1 exposed

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Matching the Ideal With the Possible Designs

Many differences exist between the ideal and the

possible!

Anticipated problems

• Sudden death will not be completely captured.

• Race information cannot be measured directly.

• Substance abuse information will not be complete.

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Conclusions - Study Design

• Data collection and analyses need to be tailored to the three different outcomes.

• Analyses will differ for children and adults.

• No single database will be sufficient.

• Large medical databases:

• are essential to this type of research question,

• are limited to medical encounters and associated data, and

• may be supplemented through linkages and patient-based surveys.

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Conclusions – Next Steps

1. Policy decision is key: determine level of increased risk that would dictate change in clinical guidance – in children and adults.

2. When feasible and appropriate, design study to address these levels of concern.

3. When such a study is not feasible, continue active surveillance study to reduce level of uncertainty around the potential increased risk.