Multivariate‐adjusted pharmacoepidemiologic analyses of confidential information pooled from multiple health care utilization databases

Mandated post‐marketing drug safety studies require vast databases pooled from multiple administrative data sources which can contain private and proprietary information. We sought to create a method to conduct pooled analyses while keeping information private and allowing for full confounder adjustment.

[1]  D. Juurlink,et al.  Proton pump inhibitors and clopidogrel: putting the interaction in perspective. , 2009, Circulation.

[2]  S. Schneeweiss,et al.  Risk of death associated with the use of conventional versus atypical antipsychotic drugs among elderly patients , 2007, Canadian Medical Association Journal.

[3]  E. Pezalla,et al.  Initial assessment of clinical impact of a drug interaction between clopidogrel and proton pump inhibitors. , 2008, Journal of the American College of Cardiology.

[4]  Fang Zhang,et al.  A distributed research network model for post‐marketing safety studies: the Meningococcal Vaccine Study , 2008, Pharmacoepidemiology and drug safety.

[5]  Sebastian Schneeweiss,et al.  Cardiovascular Outcomes and Mortality in Patients Using Clopidogrel With Proton Pump Inhibitors After Percutaneous Coronary Intervention or Acute Coronary Syndrome , 2009, Circulation.

[6]  M Blettner,et al.  Traditional reviews, meta-analyses and pooled analyses in epidemiology. , 1999, International journal of epidemiology.

[7]  B. Hansen The prognostic analogue of the propensity score , 2008 .

[8]  M Alan Brookhart,et al.  Osteoporosis Improvement: A Large‐Scale Randomized Controlled Trial of Patient and Primary Care Physician Education , 2007, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[9]  Robert L Davis,et al.  Real-Time Vaccine Safety Surveillance for the Early Detection of Adverse Events , 2007, Medical care.

[10]  Vittorio Krogh,et al.  Methods for pooling results of epidemiologic studies: the Pooling Project of Prospective Studies of Diet and Cancer. , 2006, American journal of epidemiology.

[11]  R. D'Agostino Adjustment Methods: Propensity Score Methods for Bias Reduction in the Comparison of a Treatment to a Non‐Randomized Control Group , 2005 .

[12]  Donald Rubin,et al.  Estimating Causal Effects from Large Data Sets Using Propensity Scores , 1997, Annals of Internal Medicine.

[13]  W. Ray,et al.  Evaluating medication effects outside of clinical trials: new-user designs. , 2003, American journal of epidemiology.

[14]  Cynthia Dwork,et al.  Privacy, accuracy, and consistency too: a holistic solution to contingency table release , 2007, PODS.

[15]  S B Thacker,et al.  Methods for pooled analyses of epidemiologic studies. , 1994, Epidemiology.

[16]  W. Ray,et al.  Adjustment for Multiple Cardiovascular Risk Factors Using a Summary Risk Score , 2008, Epidemiology.

[17]  Hoeteck Wee,et al.  Toward Privacy in Public Databases , 2005, TCC.

[18]  C. Friedenreich Commentary: improving pooled analyses in epidemiology. , 2002, International journal of epidemiology.

[19]  Til Stürmer,et al.  A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods. , 2006, Journal of clinical epidemiology.

[20]  Dominique Mottier,et al.  Influence of omeprazole on the antiplatelet action of clopidogrel associated with aspirin: the randomized, double-blind OCLA (Omeprazole CLopidogrel Aspirin) study. , 2008, Journal of the American College of Cardiology.

[21]  Gary H Lyman,et al.  The strengths and limitations of meta-analyses based on aggregate data , 2005, BMC Medical Research Methodology.

[22]  B. Strom,et al.  PDUFA reauthorization--drug safety's golden moment of opportunity? , 2007, The New England journal of medicine.

[23]  N. Laird,et al.  Meta-analysis in clinical trials. , 1986, Controlled clinical trials.

[24]  S Shapiro,et al.  Confounding by indication? , 1997, Epidemiology.

[25]  Sebastian Schneeweiss,et al.  Relationship Between Selective Cyclooxygenase-2 Inhibitors and Acute Myocardial Infarction in Older Adults , 2004, Circulation.

[26]  Richard Platt,et al.  Design of a National Distributed Health Data Network , 2009, Annals of Internal Medicine.

[27]  J. Avorn,et al.  High-dimensional Propensity Score Adjustment in Studies of Treatment Effects Using Health Care Claims Data , 2009, Epidemiology.

[28]  J. Avorn,et al.  A review of uses of health care utilization databases for epidemiologic research on therapeutics. , 2005, Journal of clinical epidemiology.

[29]  J. Robins,et al.  Estimating exposure effects by modelling the expectation of exposure conditional on confounders. , 1992, Biometrics.

[30]  Samy Suissa,et al.  Immortal time bias in pharmaco-epidemiology. , 2008, American journal of epidemiology.

[31]  Vincent Mor,et al.  Weaknesses of goodness‐of‐fit tests for evaluating propensity score models: the case of the omitted confounder , 2005, Pharmacoepidemiology and drug safety.

[32]  M Alan Brookhart,et al.  Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly. , 2005, American journal of epidemiology.

[33]  J. Avorn,et al.  Risk of death in elderly users of conventional vs. atypical antipsychotic medications. , 2005, The New England journal of medicine.

[34]  D. Rubin,et al.  The central role of the propensity score in observational studies for causal effects , 1983 .

[35]  Lori S. Parsons Reducing Bias in a Propensity Score Matched-Pair Sample Using Greedy Matching Techniques , 2001 .