Adjustments for unmeasured confounders in pharmacoepidemiologic database studies using external information.
BACKGROUND
Nonexperimental studies of drug effects in large automated databases can provide timely assessment of real-life drug use, but are prone to confounding by variables that are not contained in these databases and thus cannot be controlled.


OBJECTIVE
To describe how information on additional confounders from validation studies can help address the problem of unmeasured confounding in the main study.


METHODS
Review types of validation studies that allow adjustment for unmeasured confounding and illustrate these with an example.


METHODS
Main study: New Jersey residents age 65 years or older hospitalized between 1995 and 1997, who filled prescriptions within Medicaid or a pharmaceutical assistance program. Validation study: representative sample of Medicare beneficiaries.


METHODS
Association between nonsteroidal antiinflammatory drugs (NSAIDs) and mortality.


RESULTS
Validation studies are categorized as internal (ie, additional information is collected on participants of the main study) or external. Availability of information on disease outcome will affect choice of analytic strategies. Using an external validation study without data on disease outcome to adjust for unmeasured confounding, propensity score calibration (PSC) leads to a plausible estimate of the association between NSAIDs and mortality in the elderly, if the biases caused by measured and unmeasured confounders go in the same direction.


CONCLUSIONS
Estimates of drug effects can be adjusted for confounders that are not available in the main, but can be measured in a validation study. PSC uses validation data without information on disease outcome under a strong assumption. The collection and integration of validation data in pharmacoepidemiology should be encouraged.
Stürmer
Til
T
Divisions of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA. til.sturmer@post.harvard.edu
Glynn
Robert J
RJ
Rothman
Kenneth J
KJ
Avorn
Jerry
J
Schneeweiss
Sebastian
S
D000368:Aged; D000894:Anti-Inflammatory Agents, Non-Steroidal / Q000009:adverse effects; D002138:Calibration; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D008297:Male; D009026:Mortality; D009515:New Jersey / Q000453:epidemiology; D017891:Pharmacoepidemiology / Q000379:methods / Q000706:statistics & numerical data*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012107:Research Design; D020379:Risk Adjustment / Q000706:statistics & numerical data*
D000894:Anti-Inflammatory Agents, Non-Steroidal