Transcript Use of population-based databases in comparative

As noted by Gary H. Lyman (JCO, 2012)
“CER is an important framework for
systematically identifying and summarizing
the totality of evidence on the
effectiveness, safety, and value of
competing strategies to inform patients,
providers, and policy makers, and to
provide valid recommendations on the
management of patients with cancer.”
Various Methods to
Conduct CER
Population
-based
databases
Observational
Studies
Randomized
Controlled
Trials
Systematic
Review of the
literature
CER
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Considered the “gold standard”,
providing the least biased estimates
for CER
› Consider, however,
 provide data on efficacy or outcomes
in controlled setting rather than in ‘real
world’ settings
 RCTs not always feasible or ethically
acceptable (rare conditions, vulnerable
populations)
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Fill evidence gaps in CER
Provide outcomes data in ‘real world’
settings  effectiveness
Ability to study rare conditions and/or
outcomes in vulnerable populations and
to compare a number of treatment
alternatives
 POPULATION-BASED DATABASES
› Large number of subjects at an affordable
cost
› Longer periods of follow-up
 Examine long term risks and benefits
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Enrollment and claims data:
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Medicaid (poor, aged, disabled)
Medicare (aged, disabled)
Veterans Administration (military)
Private insurance
Linked databases:
› Surveillance, Epidemiology and End-Results (SEER)
and Medicare files
› The Ohio Cancer Aging Linked Database (CALD),
consisting of data from the Ohio Cancer Incidence
Surveillance System, Medicare, Medicaid, and
clinical assessment data from home health and
nursing home care
› The linked Health and Retirement Study and
Medicare data
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Enrollment data:
› Demographics
› Eligibility category(ies)
› In the context of the Medicaid program,
 Length of enrollment
 Gaps in enrollment
 Area of residence
 Ability to link to contextual variables (availability of health care
resources)
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Claims data:
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Dates of service
Diagnosis codes
Procedure codes
Prescription drugs
Charge/cost data
Capture all treatment modalities covered
by the program, and the associated
charges/costs to the program
 Identify subgroups of the population
receiving certain treatment modalities
 Ability to follow-up long term to monitor
certain outcomes
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Morbidity (complications)
Mortality
Readmissions
Costs
Completeness/accuracy of administrative
data (flu vaccine, digital rectal exam)
 Limited ability to describe a patient’s
clinical presentation cross-sectionally, or
longitudinally
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› Lack of disease-specific data (e.g., cancer
stage; recurrence)
› Lack of data on health and functional status,
and/or on geriatric syndromes (e.g., cognitive
status, depressive symptoms)
  use linked databases
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Difficult to adjust for selection bias
› For example, systematic differences in the
way physicians prescribe (newer treatment
to more severe cases)
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 Use of statistical techniques such as
propensity scores or instrumental
variables to address bias
Radiofrequency ablation (RFA) use
among patients with hepatocellular
carcinoma (HCC) has increased over
the last decade.
 Although RFA is widely perceived as safe
and effective, this has not been
rigorously evaluated using populationbased data.
 Assessments outside specialized centers
are lacking.
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Evaluate the safety and effectiveness of
RFA when used to treat HCC.
Data Source: Linked SEER-Medicare data
(2002-2005)
 Outcomes:
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› 30- and 90-day mortality
› Readmission
› Survival
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Comparison groups (treatment modalities
identified based on procedure codes
documented in claims data):
› Resection
› RFA
› No treatment
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Multivariate and propensity score
adjusted regression models.
› Propensity score calculation included liver-
related comorbid conditions (e.g., ascites,
hepatitis B/C, GI bleed, cirrhotic liver)
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2,631 patients; demographics and
comorbidities:
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Average age: 76.1 ± 6.1 years
65.9% male
67.9% white
68.5% having a Charlson score ≥ 1
Treatment modalities:
› 84.2% untreated
› RFA: 7.8%
› Resection: 7.9%
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Between RFA and resection:
› 1-year survival: 72.2% vs. 79.7%, p=0.18
› 3-year survival: 39.2% vs. 58.0%, p < 0.001
› 5-year survival: 34.8% vs. 50.2%, p < 0.001
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Multivariable results:
› RFA (single session or multiple sessions) vs. no
treatment: no diff within 1 year
› Resection vs. RFA or no treatment: 50-75%
decreased hazard of death
RFA vs. Resection: early adverse events
not significantly lower in patients treated
with RFA
 RFA vs. no treatment: no obvious benefits
in the 1-year survival
 [There may be some survival benefits in certain subgroups of
patients who have not yet been well characterized..]
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Residual confounding, despite the use of
propensity scores.
 Lack of pertinent clinical data to
quantify surgical risk (e.g., lab data,
anesthetic factors), or other clinical
variables impacting surgical decisionmaking and patient selection.
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