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Use of Population-Based Databases in Comparative Effectiveness Research (CER). Siran M. Koroukian , Ph.D. Department of Epidemiology and Biostatistics Population Health and Outcomes Research Core December 14, 2012. As noted by Gary H. Lyman (JCO, 2012)

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use of population based databases in comparative effectiveness research cer

Use of Population-Based Databases in Comparative Effectiveness Research (CER)

Siran M. Koroukian, Ph.D.

Department of Epidemiology and Biostatistics

Population Health and Outcomes Research Core

December 14, 2012

slide2

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.”

slide3

Various Methods to

Conduct CER

Population-based databases

randomized controlled trials rcts
Randomized Controlled Trials (RCTs)
  • 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)
observational studies in cer
Observational studies in CER
  • 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
examples of population based databases
Examples of population-based databases
  • Enrollment and claims data:
    • 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
enrollment and claims data
Enrollment and claims data
  • 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)
  • Claims data:
    • Dates of service
    • Diagnosis codes
    • Procedure codes
    • Prescription drugs
    • Charge/cost data
advantages of enrollment and claims data
Advantages of enrollment and claims 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
    • Morbidity (complications)
    • Mortality
    • Readmissions
    • Costs
limitations of population based administrative databases
Limitations of population-based administrative databases
  • Completeness/accuracy of administrative data (flu vaccine, digital rectal exam)
  • Limited ability to describe a patient’s clinical presentation cross-sectionally, or longitudinally
    • 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
limitations of population based administrative databases1
Limitations of population-based administrative databases
  • Difficult to adjust for selection bias
    • For example, systematic differences in the way physicians prescribe (newer treatment to more severe cases)
  •  Use of statistical techniques such as propensity scores or instrumental variables to address bias
slide11

Example of a CER study using large databases Comparative assessment of the safety and effectiveness of radiofrequency ablation among elderly Medicare beneficiaries with hepatocellular carcinomaMassarweh et al. Ann SurgOncol, 2012; 19:1058-1065

background
Background
  • 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 population-based data.
  • Assessments outside specialized centers are lacking.
study objective
Study objective
  • Evaluate the safety and effectiveness of RFA when used to treat HCC.
methods
Methods
  • Data Source: Linked SEER-Medicare data (2002-2005)
  • Outcomes:
    • 30- and 90-day mortality
    • Readmission
    • Survival
  • Comparison groups (treatment modalities identified based on procedure codes documented in claims data):
    • Resection
    • RFA
    • No treatment
analytic approach
Analytic approach
  • 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)
results
Results
  • 2,631 patients; demographics and comorbidities:
    • 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%
effectiveness assessment
Effectiveness assessment
  • 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
  • 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
conclusions
Conclusions
  • 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..]
study limitations
Study limitations
  • 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 decision-making and patient selection.
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