Use of Population-Based Databases in Comparative

Effectiveness Research (CER)Siran M. Koroukian, Ph.D.

Department of Epidemiology and BiostatisticsPopulation Health and Outcomes Research Core

December 14, 2012

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.”

CER

Randomized Controlled

Trials

Observational Studies

Systematic Review of

the literature

Population-based databases

Various Methods to Conduct CER

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 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 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 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

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

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 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

Example of a CER study using large databases

Comparative assessment of the safety and effectiveness of

radiofrequency ablation among elderly Medicare beneficiaries with hepatocellular carcinoma

Massarweh et al. Ann Surg Oncol, 2012; 19:1058-

1065

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 Evaluate the safety and effectiveness

of RFA when used to treat HCC.

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 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 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

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 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 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