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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


As noted by Gary H. Lyman (JCO, 2012) Effectiveness Research (CER)

“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 Effectiveness Research (CER)

Conduct CER

Population-based databases


Randomized controlled trials rcts
Randomized Controlled Trials (RCTs) Effectiveness Research (CER)

  • 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 Effectiveness Research (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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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


Example of a CER study using large databases Effectiveness Research (CER)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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • Evaluate the safety and effectiveness of RFA when used to treat HCC.


Methods
Methods Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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%


Safety assessment
Safety assessment Effectiveness Research (CER)


Effectiveness assessment
Effectiveness assessment Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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 Effectiveness Research (CER)

  • 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.


Population health and outcomes research core contact sxk15@case edu
POPULATION HEALTH AND OUTCOMES RESEARCH Effectiveness Research (CER) COREContact:[email protected]


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