Reproducing Protocol‐Based Studies Using Parameterizable Tools—Comparison of Analytic Approaches Used by Two Medical Product Surveillance Networks

The US Sentinel System and the Canadian Network for Observational Drug Effect Studies (CNODES) are two medical product safety surveillance networks. Using Sentinel's preprogrammed, parameterizable analytic tools, we reproduced two protocol‐based studies conducted by CNODES to assess the risks of acute pancreatitis and heart failure (HF) associated with the use of incretin‐based drugs, compared with use of ≥ 2 oral hypoglycemic agents. Results from the replication new‐user cohort analyses aligned with those from the CNODES nested case‐control studies. The adjusted hazard ratios were 0.95 (0.81–1.12; vs. 1.03 (0.87–1.22) in CNODES) for acute pancreatitis and 0.91 (0.84–1.00; vs. 0.82 (0.67–1.00) in CNODES) for HF among patients without HF history. The CNODES's common protocol approach allows studies tailored to specific safety questions, whereas the Sentinel's common data model plus pretested program approach enables more rapid analysis. Despite these differences, it is possible to obtain comparable results using both approaches.

[1]  Robert W. Platt,et al.  Observational Studies of Drug Safety in Multi-Database Studies: Methodological Challenges and Opportunities , 2016, EGEMS.

[2]  Sebastian Schneeweiss,et al.  Choosing Among Common Data Models for Real‐World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products , 2020, Clinical pharmacology and therapeutics.

[3]  Robert W. Platt,et al.  Distributed Networks of Databases Analyzed Using Common Protocols and/or Common Data Models , 2019, Pharmacoepidemiology.

[4]  Sebastian Schneeweiss,et al.  A basic study design for expedited safety signal evaluation based on electronic healthcare data , 2010, Pharmacoepidemiology and drug safety.

[5]  A. Levy,et al.  CNODES: the Canadian Network for Observational Drug Effect Studies , 2012, Open medicine : a peer-reviewed, independent, open-access journal.

[6]  Richard Platt,et al.  The organizational structure and governing principles of the Food and Drug Administration's Mini‐Sentinel pilot program , 2012, Pharmacoepidemiology and drug safety.

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

[8]  Pierre Ernst,et al.  Association Between Incretin-Based Drugs and the Risk of Acute Pancreatitis. , 2016, JAMA internal medicine.

[9]  Moride Yola,et al.  Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research. , 1994 .

[10]  Olaf Klungel,et al.  The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE) , 2018, British Medical Journal.

[11]  Robert W. Platt,et al.  How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias , 2020, Pharmacoepidemiology and drug safety.

[12]  Marsha A Raebel,et al.  Design considerations, architecture, and use of the Mini‐Sentinel distributed data system , 2012, Pharmacoepidemiology and drug safety.

[13]  Richard Platt,et al.  The FDA Sentinel Initiative - An Evolving National Resource. , 2018, The New England journal of medicine.

[14]  Til Stürmer,et al.  Indications for propensity scores and review of their use in pharmacoepidemiology. , 2006, Basic & clinical pharmacology & toxicology.

[15]  Jerry H. Gurwitz,et al.  A systematic review of validated methods for identifying heart failure using administrative data , 2012, Pharmacoepidemiology and drug safety.

[16]  Sengwee Toh,et al.  Confounding adjustment via a semi‐automated high‐dimensional propensity score algorithm: an application to electronic medical records , 2011, Pharmacoepidemiology and drug safety.

[17]  Jerry H. Gurwitz,et al.  Mini-Sentinel Systematic Evaluation of Health Outcome of Interest Definitions for Studies Using Administrative and Claims Data: Heart Failure , 2012 .

[18]  Y. Moride,et al.  Evidence of the depletion of susceptibles effect in non-experimental pharmacoepidemiologic research. , 1994, Journal of clinical epidemiology.

[19]  Olaf Klungel,et al.  Reporting to Improve Reproducibility and Facilitate Validity Assessment for Healthcare Database Studies V1.0. , 2017, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  Bruce H Fireman,et al.  Confounding Adjustment in Comparative Effectiveness Research Conducted Within Distributed Research Networks , 2013, Medical care.

[21]  S Toh,et al.  Successful Comparison of US Food and Drug Administration Sentinel Analysis Tools to Traditional Approaches in Quantifying a Known Drug‐Adverse Event Association , 2016, Clinical pharmacology and therapeutics.

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

[23]  Sengwee Toh,et al.  Analyzing partially missing confounder information in comparative effectiveness and safety research of therapeutics , 2012, Pharmacoepidemiology and drug safety.

[24]  M Maclure,et al.  Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. , 2001, American journal of epidemiology.

[25]  Barbara Evans,et al.  A policy framework for public health uses of electronic health data , 2012, Pharmacoepidemiology and drug safety.

[26]  Pierre Ernst,et al.  A Multicenter Observational Study of Incretin-based Drugs and Heart Failure. , 2016, The New England journal of medicine.

[27]  James M. Robins,et al.  Observational Studies Analyzed Like Randomized Experiments: An Application to Postmenopausal Hormone Therapy and Coronary Heart Disease , 2008, Epidemiology.

[28]  Sebastian Schneeweiss,et al.  A combined comorbidity score predicted mortality in elderly patients better than existing scores. , 2011, Journal of clinical epidemiology.

[29]  P. Rosenbaum,et al.  Invited commentary: propensity scores. , 1999, American journal of epidemiology.

[30]  Charles E. Leonard,et al.  Evaluation of the US Food and Drug Administration sentinel analysis tools in confirming previously observed drug‐outcome associations: The case of clindamycin and Clostridium difficile infection , 2018, Pharmacoepidemiology and drug safety.

[31]  Christian Hampp,et al.  Sentinel Modular Program for Propensity Score–Matched Cohort Analyses: Application to Glyburide, Glipizide, and Serious Hypoglycemia , 2017, Epidemiology.

[32]  R. Platt,et al.  Developing the Sentinel System--a national resource for evidence development. , 2011, The New England journal of medicine.

[33]  Sengwee Toh,et al.  Development and Application of Two Semi-Automated Tools for Targeted Medical Product Surveillance in a Distributed Data Network , 2017, Current Epidemiology Reports.

[34]  S. Anderson,et al.  The FDA's sentinel initiative—A comprehensive approach to medical product surveillance , 2016, Clinical pharmacology and therapeutics.

[35]  Richard Platt,et al.  The U.S. Food and Drug Administration's Mini‐Sentinel program: status and direction , 2012, Pharmacoepidemiology and drug safety.

[36]  R. Carnahan,et al.  A systematic review of validated methods for identifying pancreatitis using administrative data , 2012, Pharmacoepidemiology and drug safety.

[37]  Christian Hampp,et al.  Evaluation of the US Food and Drug Administration Sentinel Analysis Tools Using a Comparator with a Different Indication: Comparing the Rates of Gastrointestinal Bleeding in Warfarin and Statin Users , 2019, Pharmaceutical Medicine.