A Structured Preapproval and Postapproval Comparative Study Design Framework to Generate Valid and Transparent Real‐World Evidence for Regulatory Decisions

Real‐world evidence provides important information about the effects of medicines in routine clinical practice. To engender trust that evidence generated for regulatory purposes is sufficiently valid, transparency in the reasoning that underlies study design decisions is critical. Building on existing guidance and frameworks, we developed the Structured Preapproval and Postapproval Comparative study design framework to generate valid and transparent real‐world Evidence (SPACE) as a process for identifying design elements and minimal criteria for feasibility and validity concerns, and for documenting decisions. Starting with an articulated research question, we identify key components of the randomized controlled trial needed to maximize validity, and pragmatic choices are considered when required. A causal diagram is used to justify the variables identified for confounding control, and key decisions, assumptions, and evidence are captured in a structured way. In this way, SPACE may improve dialogue and build trust among healthcare providers, patients, regulators, and researchers.

[1]  Richard F MacLehose,et al.  Good practices for quantitative bias analysis. , 2014, International journal of epidemiology.

[2]  Susan Rose,et al.  International Ethical Guidelines for Epidemiological Studies By the Council for International Organizations of Medical Sciences (CIOMS) , 2009 .

[3]  S. Schneeweiss,et al.  Improving therapeutic effectiveness and safety through big healthcare data , 2016, Clinical pharmacology and therapeutics.

[4]  David Martin,et al.  Evaluating the Use of Nonrandomized Real‐World Data Analyses for Regulatory Decision Making , 2019, Clinical pharmacology and therapeutics.

[5]  David M. Evans,et al.  Collider scope: when selection bias can substantially influence observed associations , 2016, bioRxiv.

[6]  R Platt,et al.  Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision‐Making , 2016, Clinical pharmacology and therapeutics.

[7]  N. Dreyer,et al.  Considerations in characterizing real‐world data relevance and quality for regulatory purposes: A commentary , 2018, Pharmacoepidemiology and drug safety.

[8]  R. D'Agostino,et al.  The Ziprasidone Observational Study of Cardiac Outcomes (ZODIAC): design and baseline subject characteristics. , 2008, The Journal of clinical psychiatry.

[9]  Tyler J. VanderWeele,et al.  Sensitivity Analysis in Observational Research: Introducing the E-Value , 2017, Annals of Internal Medicine.

[10]  S. Schwartz,et al.  Toward a Clarification of the Taxonomy of “Bias” in Epidemiology Textbooks , 2015, Epidemiology.

[11]  G. Bedogni Applying Quantitative Bias Analysis to Epidemiologic Data , 2011 .

[12]  Nancy A. Dreyer,et al.  Advancing a Framework for Regulatory Use of Real-World Evidence , 2018, Therapeutic innovation & regulatory science.

[13]  Canary Wharf,et al.  The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) , 2012 .

[14]  J. Lieberman,et al.  Effectiveness of antipsychotic drugs in patients with chronic schizophrenia. , 2005, The New England journal of medicine.

[15]  J. Pearl Causal diagrams for empirical research , 1995 .

[16]  A. Fourrier-Réglat,et al.  The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on Methodological Standards in Pharmacoepidemiology (Revision 1, 2012, Revision 2, 2013, Revision 3, 2014)). , 2012 .

[17]  Amy P. Abernethy,et al.  Harnessing the Power of Real‐World Evidence (RWE): A Checklist to Ensure Regulatory‐Grade Data Quality , 2017, Clinical pharmacology and therapeutics.

[18]  Miguel A Hernán,et al.  With great data comes great responsibility: publishing comparative effectiveness research in epidemiology. , 2011, Epidemiology.

[19]  R. Anziano,et al.  A Randomized Evaluation of the Effects of Six Antipsychotic Agents on QTc, In the Absence and Presence of Metabolic Inhibition , 2004, Journal of clinical psychopharmacology.

[20]  J. Kane,et al.  Lessons learned in the conduct of a global, large simple trial of treatments indicated for schizophrenia. , 2013, Contemporary clinical trials.

[21]  Sander Greenland,et al.  Multiple‐bias modelling for analysis of observational data , 2005 .

[22]  T. S. Stroup What can large simple trials do for psychiatry? , 2011, The American journal of psychiatry.

[23]  David Madigan,et al.  Good practices for real‐world data studies of treatment and/or comparative effectiveness: Recommendations from the joint ISPOR‐ISPE Special Task Force on real‐world evidence in health care decision making , 2017, Pharmacoepidemiology and drug safety.

[24]  Guidance for Industry and FDA Staff Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Healthcare Data , 2013 .

[25]  G. Shaw,et al.  Maternal pesticide exposure from multiple sources and selected congenital anomalies. , 1999 .

[26]  James M Robins,et al.  Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. , 2016, American journal of epidemiology.

[27]  R. D'Agostino,et al.  Comparative mortality associated with ziprasidone and olanzapine in real-world use among 18,154 patients with schizophrenia: The Ziprasidone Observational Study of Cardiac Outcomes (ZODIAC). , 2011, The American journal of psychiatry.

[28]  J. Pearl,et al.  Causal diagrams for epidemiologic research. , 1999, Epidemiology.

[29]  B. Klein,et al.  Acute nonarteritic anterior ischemic optic neuropathy and exposure to phosphodiesterase type 5 inhibitors. , 2015, The journal of sexual medicine.

[30]  T. Richardson Single World Intervention Graphs ( SWIGs ) : A Unification of the Counterfactual and Graphical Approaches to Causality , 2013 .