Transcript Document

Clinical Epidemiology &
Analytics – filling the evidence
gap
Woodie M. Zachry, III, PhD
Global Lead Clinical Epidemiology and
Analytics
The Present – Overview of CE&A activities
Establishing the disease profile
–
–
–
–
Natural history of the disease
Issues in special populations
Incidence/prevalence of the disease
Risk factors of disease
Identifying drug safety issues in collaboration with Pharmacovigilance
– Safety issues of Abbott products and other current therapies
– Subpopulations at higher risk?
– Drug-drug interactions?
Providing clinical trial support and instrumentation
– Identifying biomarkers/surrogate endpoints and its relationship to outcomes
Company Confidential
© 2009 Abbott
2
Study Types & Data Sources
Study
Type
Potential Sources of Information
Preclinical AEGIS AERS WHO Registry Claims Clinical Trials
Database
Database
Data
Systematic
Review with
Meta
Analysis
Randomized
Controlled
Trial
Experimental
Designs
Cohort
Case Control
Case Report
X
X
X
Company Confidential
© 2009 Abbott
X
Literature
Cochrane
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
3
GRADE
– The Grading of Recommendations Assessment, Development and
Evaluation (GRADE )
– Provides a system for rating quality of evidence and strength of
recommendations that is explicit, comprehensive, transparent, and
pragmatic and is increasingly being adopted by organizations
worldwide
• High quality— Further research is very unlikely to change the estimate of
effect
• Moderate quality— Further research is likely to have an important impact
on the estimate of effect and may change the estimate
• Low quality— Further research is very likely to have an important impact
on the estimate of effect and is likely to change the estimate
• Very low quality— Any estimate of effect is very uncertain
Company Confidential
© 2009 Abbott
4
Hierarchy of Evidence
Meta-analysis
RCT
Prospective
Less Bias
Observational Studies
Retrospective
Less Bias
Case-Control
Comparison with bias
Case Series
Uncontrolled
Nonsystematic
Clinical Experience
Company Confidential
© 2009 Abbott
5
Multiple EBM Stakeholders
Levels
of Evidence
Chest
CONSORT
Statement
RCTs
Clinical
Practice
Guidelines
FDA
AHRQ
NIH
Users’ Guides
JAMA
HTAs
ACP Journal Club
Clinical Evidence
Company Confidential
© 2009 Abbott
NICE
6
EMEA
QUORUM
Statement
Systematic Review
Meta-Analysis
Cochrane
Collaborative
Where we want to be
Evidence
Summaries across
All Phases
of Development and
Study Designs
Company Confidential
© 2009 Abbott
Evidence
Based
Approach
7
Identify
Evidence
Gaps and Propose
Ways to Fill
Gaps
Case-Control analysis of ambulance,
emergency room, or inpatient hospital
events for epilepsy and antiepileptic
drug formulation changes
Woodie M Zachry, III PhD
Quynhchau D Doan PhD
Jerry D Clewell, PharmD
Brien J Smith MD
Background Epilepsy Treatment
Disease & Treatment
– Incidence: 200,000 cases annually in US, Prevalence: 1% from birth to age 20, then 3% by age 75.6
– Treatment choice dependent upon Partial vs. Generalized presentation, history & secondary causes.
– “A-rated” compounds are considered to be therapeutically bioequivalent to the reference listed drug
(United States Food & Drug Administration Center for Drug Evaluation & Research)
• Generic substitution, observational experience
– 65% of US physicians surveyed reported caring for a patient who had a breakthrough seizure after
a brand to generic switch.1
– 49.2% of foreign physicians surveyed reported problems when switching from brand alternatives to
generics.2
– 67.8% of surveyed neurologists reported breakthrough seizures after a switch.3
– 12.9% of Lamotrigine switches had to be switched back due to medical necessity (v.s 1.5-2.9 for
Non-AED).4
– 10.8% of patients switching supplier for CBZ, PHT, & VAL had perceived problems validated by
GP.5
1.
2.
3.
4.
5.
6.
Berg MJ, Gross RA. Physicians and patients perceive that generic drug substitution of anti-epileptic drugs can cause breakthrough seizures - results from a U.S. survey.
60th Annual Meeting of the American Epilepsy Society; Dec 1-5, 2006; San Diego, California.
Kramer G. et al. Experience with generic drugs in epilepsy patients: an electronic survey of members of the German, Austrian and Swiss branches of the ILAE. Epilepsia
2007;48, 609-11.
Wilner AN. Therapeutic equivalency of generic antiepileptic drugs: results of a survey. Epilepsy Behavior 2004;5(6):995-8.
Andermann F, et al. Compulsory generic switching of antiepileptic drugs: high switchback rates ro branded compounds compared with other drug classes. Epilepsia
2007;48(3):464-9.
Crawford P. et al. Generic Prescribing for epilepsy. Is it safe? Siezure 1996;5:1-5.
Centers for Disease Control and Prevention 2007. http://www.cdc.gov/epilepsy/ Accessed October 10, 2007.
Company Confidential
© 2009 Abbott
9
Confidence in Treatment-Effect Relationship
Low
High
Case Reports
Case-Control Epidemiological
Cohort Epidemiological
RCCT
Hypothesis
generation
Hypothesis test (without
temporal relationship)
Hypothesis test (with
temporal relationship
assessment)
Hypothesis test (Cause –
Effect relationship inferred)
Spontaneous
reports to authorities
with variable
completeness and
data quality
Subjects selected based on
current disease status (yes /
no).
Exposed Vs. non-exposed
subjects assembled before
development of disease.
Treatment and Control
groups studied in
randomized, blinded trial
Retrospectively evaluate
exposure to agent(s) &
confounders
Baseline confounding
variables assessed before
disease development.
Detection bias
Usually not possible to
calculate rate of development
of disease given the presence
or absence of exposure.1,2
Treatment-emergent,
temporal relationship to
exposure, and incidence of
disease can be measured.
Causality can be inferred
Cannot establish causality
Most closely resembles RCT
design.1,2
Generalizability limited by
inability to detect events in
the greater population, and
sub-populations.
Selection bias
Effects of risk
factors are most
difficult to evaluate
Confounding patient
factors often not
considered
Cannot establish causality
Limited ability to detect rare
events.
Cannot establish
causality
1Mednick
D, Day D. JMCP 1997;3(1):66-75. 2Hennekens, C. Epidemiology in Medicine. 3Harris S. J Cont. Ed. In Health Prof 2000;20:133-45.
Company Confidential
© 2009 Abbott
10
Methods
• Objective: To determine if patients who received epilepsy care in
an inpatient setting, emergency room, or ambulance have
greater odds of having had a change between A rated AED
medication alternatives in the past 6 months when compared to
epileptic patients with no evidence of receiving epileptic care in
similar settings.
Company Confidential
© 2009 Abbott
11
Methods
• Retrospective claims database analysis utilizing the Ingenix LabRx database
• Case-control study
– Unmatched & Matched 1:3 for age within 5 years and epilepsy diagnosis type
– Index date for case patients: 1st seizure event requiring inpatient admission,
emergency room visit, or ambulance during 3Q2006 – 4Q2006
– Index date for control patients: 1st office visit during 3Q2006 – 4Q2006
• Index primary ICD-9 diagnosis of 345.xx excluding 345.6
• 12 and 64 years of age
• No inpatient admission, emergency room visit, or ambulance in 6 months prior
to index date
• Possess at least 145 day supply of AED medication for 6 months prior to index
event
• Continuous eligibility for 6 months prior to index.
Company Confidential
© 2009 Abbott
12
Diagnosis Categories
• Siezure type
• Modifier
– Generalized
– XXX.X0 – without mention of
intractable epilepsy
• Convulsive 345.0
• Non-convulsive 345.1
– XXX.X1 – with mention of
intractable epilepsy
• Petite mal status 345.2
• Grand mal status 345.3
– Partial
• Complex partial 345.4
• Simple partial 345.5
• Epilepsia partialis continua 345.7
– Other
• Other forms 345.8
• Epilepsy unspecified 345.9
Company Confidential
© 2009 Abbott
13
All Patients (Non-Matched)
Variable
% Male
Age (SD)
Case Patients Control Patients
P value
(n=417)
(n=5562)
(a-priori=0.05)
44.8%
45.1%
NS
37.4yrs (14.8)
37.2yrs (14.6)
NS
Insurance
<0.001
Commercial
95.4%
98.1%
Medicaid
4.6%
1.9%
US Region
NS
West
12.7%
14.5%
Midwest
33.1%
33.8%
South
42.0%
40.0%
Northeast
12.2%
11.6%
Company Confidential
© 2009 Abbott
14
Matched Case-Control Patients
Variable
% Male
Age (SD)
Case Patients Control Patients
P value
(n=416)
(n=1248)
(a-priori=0.05)
45.0%
44.2%
NS
37.4yrs (14.8)
37.5yrs (14.7)
NS
Insurance
0.004
Commercial
95.4%
98.2%
Medicaid
4.6%
1.8%
US Region
NS
West
12.7 %
14.3 %
Midwest
33.2 %
33.6 %
South
41.8 %
39.2 %
Northeast
12.3 %
13.0 %
Company Confidential
© 2009 Abbott
15
All Patients (Non-Matched)
Seizure Type
Generalized nonintractable
Case Patients
(n=417)
30.5%
Control Patients
(n=5562)
35.5%
Generalized intractable
9.1%
6.7%
Partial nonintractable
19.2%
36.0%
Partial intractable
26.4%
16.6%
Other, nonintractable
3.1%
1.1%
Other, intractable
11.8%
4.1%
2 <0.001
Company Confidential
© 2009 Abbott
16
Matched Case-Control Patients
Seizure Type
Case Patients
(n=416)
30.5%
Control Patients
(n=1248)
30.5%
Generalized intractable
9.1%
9.1%
Partial nonintractable
19.2%
19.2%
Partial intractable
26.4%
26.4%
Other, nonintractable
2.9%
2.9%
Other, intractable
11.8%
11.8%
Generalized nonintractable
2 = NS
Company Confidential
© 2009 Abbott
17
All Patients (Non-Matched)
• Odds of a change between A rated alternatives
Patient switched
medications
Patients did NOT
switch medications
Case
47
370
Control
346
5216
Odds ratio = 1.915 (95% CI, 1.387 - 2.644)
Company Confidential
© 2009 Abbott
18
How to calculate an unmatched odds ratio
Unmatched analysis
Risk
factor
Cohort Status
Case Control
Exposed
a
b
a+b
Not Exposed c
d
c+d
a+c
b+d
n
Switch
No Switch
Equations
OR = ad/bc
SE = SQRT(1/a+1/b+1/c+1/d)
CI = EXP(logeOR + 1.96SE)
Example
Example Calculation
Case Control
OR estimate
47
346 393 SE
370 5216 5586 1.96*SE
417 5562
lnOR
Lower Limit
Upper Limit
Company Confidential
© 2009 Abbott
19
1.91
0.16
0.32
0.65
0.33
0.97
1.39
2.64
Matched Case-Control Patients
• Odds of a change between A rated alternatives
Patient switched
medications
Patients did NOT
switch medications
Case
47
369
Control
81
1167
Odds ratio = 1.811 (95% CI, 1.247 – 2.629)
Company Confidential
© 2009 Abbott
20
Matched primary analysis
Case with exposure
Number of controls with exposure
0
1
2
yes
40
7
0
no
298
68
3
total
i = # of exposures
mi = number of t i where
the case is exposed
ti = the total number of
sets with i exposures
i
mi
1
2
3
108
10
0
1.810811
0.190201 0.190201
0.372795
0.593775
0.22098 1.247298
3
0
0
iti
108
10
0
118
H
i(4-i)ti
ti
1
2
3
OR estimate
SE
1.96*SE
lnOR
Lower Limit
40
7
0
47
total
i
ti
total sets
47
369
416
i(4-i)ti
108
20
0
128
324
40
0
364
(4-i)m i
i(ti -m i )
120
68
14
6
0
0
134
74
J
(iOR+4-i)2 H/J
324 23.1439 13.99937
40 31.60263 1.265717
0 41.37619
0
15.26509
Cochran-Mantel-Haenszel Statistic
MH calc
60
58
3364
MH stat 9.241758
P value
0.0024
21
Matched Case-Control Patients Excluding Medicaid
Patients
• Odds of a change between A rated alternatives
Patient switched
medications
Patients did NOT
switch medications
Case
45
352
Control
79
1146
Odds ratio = 1.855 (95% CI, 1.262 – 2.726)
Company Confidential
© 2009 Abbott
22
Matched Case-Control Patients Excluding Patients
Who Changed Dosage Schedule
• Odds of a change between A rated alternatives
Patient switched
medications
Patients did NOT
switch medications
Case
22
205
Control
49
918
Odds ratio = 2.011 (95% CI, 1.189 – 3.4)
Company Confidential
© 2009 Abbott
23
Discussion
• This study tested a hypothesis and found a relationship between
emergent and inpatient care visits and previous AED formulation
switching. This is concordant with problems identified in the
survey and case study literature.
– surveyed physicians believe there may be potential safety problems
associated with switching between AED formulations for the same
medication
– There is some evidence of a significant percentage of patients who
must switch back to a branded formulation after trying a generic
formulation.
Company Confidential
© 2009 Abbott
24
Discussion
• This study assumes that patients experiencing break-through
seizures will seek care in emergency and inpatient settings
more often than ambulatory settings.
• Study subjects seeking care for break through events in an
ambulatory setting may have attenuated the true magnitude of
the significant relationship found in this study.
• Attempts were made to strengthen the assumption that subjects
were taking AEDs. However, claims data only records the date a
prescription was filled, not when or if the patient took the
medication.
• Subtle differences in formulations may take time to accumulate
and effect outcomes. However, the majority of formulation
changes occurred within 2 months of the index event.
Company Confidential
© 2009 Abbott
25
Discussion
• Several factors may play a role in break through seizures that were not
controlled for in this analysis (e.g., sleep deprivation, alcohol intake,
hormonal influences). These effects may be additive to or even
supersede formulation changes in precipitating break-through seizures.
• Zonisamide became available as a generic during the study time
period. The high percentage of zonisamide formulation changes may
have played a role in the significant relationship discovered.
• Case-control studies cannot establish a temporal association between
AED formulation switches and outcomes.
Company Confidential
© 2009 Abbott
26
Conclusions
• This analysis has found an association between patients who
utilized an ER, ambulance or inpatient hospital for epilepsy and
the prior occurrence of AED formulation switching involving “A”
rated generics.
– After matching by age and epilepsy diagnosis, Cases had 81%
greater odds of prior “A” rated switches compared to matched
controls.
– The case population had significantly more Medicaid patients.
– Post hoc analyses excluding patients who had a dosage change and
Medicaid patients did not change the significance of the original
analysis.
– Further investigations are warranted to better understand a possible
cause-effect relationship.
Company Confidential
© 2009 Abbott
27
Company Confidential
© 2009 Abbott
28
Hierarchy of Evidence
Meta-analysis
RCT
Prospective
Less Bias
Observational Studies
Retrospective
Less Bias
Case-Control
Comparison with bias
Case Series
Uncontrolled
Nonsystematic
Clinical Experience
Company Confidential
© 2009 Abbott
29