<div>1. Wartenberg D and Thompson WD. Privacy versus public health: the
impact of current confidentiality rules. <i>American Journal of
Public Health</i> . 2010; 100: 407-12.</div><div>2. Platt RW, Henry D and Suissa S. The Canadian Network for
Observational Drug Effect Studies (CNODES): Reflections on the first
eight years, and a look to the future. 2019.</div><div>3. Platt R, Brown JS, Robb M, et al. The FDA sentinel initiative—an
evolving national resource. <i>The New England journal of medicine</i> .
2018; 379: 2091-3.</div><div>4. Stang PE, Ryan PB, Racoosin JA, et al. Advancing the science for
active surveillance: rationale and design for the Observational Medical
Outcomes Partnership. <i>Annals of internal medicine</i> . 2010; 153:
600-6.</div><div>5. Budrionis A and Bellika JG. The learning healthcare system: where are
we now? A systematic review. <i>Journal of biomedical informatics</i> .
2016; 64: 87-92.</div><div>6. Maro JC, Platt R, Holmes JH, et al. Design of a national distributed
health data network. <i>Annals of internal medicine</i> . 2009; 151:
341-4.</div><div>7. Suissa S, Henry D, Caetano P, et al. CNODES: the Canadian network for
observational drug effect studies. <i>Open Medicine</i> . 2012; 6: e134.</div><div>8. Suissa S, Henry D, Caetano P, et al. CNODES: the Canadian Network for
Observational Drug Effect Studies. <i>Open medicine : a
peer-reviewed, independent, open-access journal</i> . 2012; 6: e134-40.</div><div>9. Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV and Brown JS.
Launching PCORnet, a national patient-centered clinical research
network. <i>Journal of the American Medical Informatics Association</i> .
2014; 21: 578-82.</div><div>10. Agency EM. Data Analysis and Real World Interrogation Network
(DARWIN EU). 2022.</div><div>11. Hripcsak G, Duke JD, Shah NH, et al. Observational Health Data
Sciences and Informatics (OHDSI): opportunities for observational
researchers. <i>Studies in health technology and informatics</i> . 2015;
216: 574.</div><div>12. Pratt N, Andersen M, Bergman U, et al. Multi‐country rapid adverse
drug event assessment: the Asian Pharmacoepidemiology Network (AsPEN)
antipsychotic and acute hyperglycaemia study. <i>Pharmacoepidemiology
and drug safety</i> . 2013; 22: 915-24.</div><div>13. Baggs J, Gee J, Lewis E, et al. The Vaccine Safety Datalink: a model
for monitoring immunization safety. <i>Pediatrics</i> . 2011; 127:
S45-S53.</div><div>14. Thurin NH, Pajouheshnia R, Roberto G, et al. From Inception to
ConcePTION: Genesis of a Network to Support Better Monitoring and
Communication of Medication Safety During Pregnancy and Breastfeeding.<i>Clinical Pharmacology &amp; Therapeutics</i> . 2022; 111: 321-31.</div><div>15. Toh S, Gagne JJ, Rassen JA, Fireman BH, Kulldorff M and Brown JS.
Confounding adjustment in comparative effectiveness research conducted
within distributed research networks. <i>Medical care</i> . 2013; 51:
S4-S10.</div><div>16. Toh S, Reichman ME, Houstoun M, et al. Multivariable confounding
adjustment in distributed data networks without sharing of patient‐level
data. <i>Pharmacoepidemiology and drug safety</i> . 2013; 22: 1171-7.</div><div>17. Secrest MH, Platt RW, Dormuth CR, et al. Extreme restriction design
as a method for reducing confounding by indication in
pharmacoepidemiologic research. <i>Pharmacoepidemiology and drug
safety</i> . 2020; 29: 26-34.</div><div>18. Secrest MH, Platt RW, Reynier P, Dormuth CR, Benedetti A and Filion
KB. Multiple imputation for systematically missing confounders within a
distributed data drug safety network: A simulation study and real-world
example. <i>Pharmacoepidemiology and drug safety</i> . 2020; 29 Suppl 1:
35-44.</div><div>19. Rothman KJ, Greenland S and Lash TL. <i>Modern epidemiology</i> .
Wolters Kluwer Health/Lippincott Williams &amp; Wilkins Philadelphia, 2008.</div><div>20. Westreich D, Edwards JK, Lesko CR, Cole SR and Stuart EA. Target
Validity and the Hierarchy of Study Designs. <i>Am J Epidemiol</i> .
2019; 188: 438-43.</div><div>21. Lesko CR, Ackerman B, Webster-Clark M and Edwards JK. Target
validity: bringing treatment of external validity in line with internal
validity. <i>Current epidemiology reports</i> . 2020; 7: 117-24.</div><div>22. Dahabreh IJ, Robertson SE, Steingrimsson JA, Stuart EA and Hernan
MA. Extending inferences from a randomized trial to a new target
population. <i>Statistics in medicine</i> . 2020; 39: 1999-2014.</div><div>23. Pearl J and Bareinboim E. External validity: From do-calculus to
transportability across populations. <i>Statistical Science</i> . 2014;
29: 579-95.</div><div>24. Webster-Clark M and Breskin A. Directed acyclic graphs, effect
measure modification, and generalizability. <i>American Journal of
Epidemiology</i> . 2021; 190: 322-7.</div><div>25. Westreich D, Edwards JK, Lesko CR, Stuart E and Cole SR.
Transportability of Trial Results Using Inverse Odds of Sampling
Weights. <i>Am J Epidemiol</i> . 2017; 186: 1010-4.</div><div>26. Dahabreh IJ, Haneuse SJA, Robins JM, et al. Study designs for
extending causal inferences from a randomized trial to a target
population. <i>American journal of epidemiology</i> . 2021; 190: 1632-42.</div><div>27. MacPherson H. Pragmatic clinical trials. <i>Complementary
therapies in medicine</i> . 2004; 12: 136-40.</div><div>28. Schneeweiss S, Brown JS, Bate A, Trifirò G and Bartels DB. Choosing
among common data models for real‐world data analyses fit for making
decisions about the effectiveness of medical products. <i>Clinical
Pharmacology &amp; Therapeutics</i> . 2020; 107: 827-33.</div><div>29. Weissfeld M, Gagne JJ and Izem R. SENTINEL METHODS. 2016.</div><div>30. Marin-Martinez F and Sánchez-Meca J. Weighting by inverse variance
or by sample size in random-effects meta-analysis. <i>Educational and
Psychological Measurement</i> . 2010; 70: 56-73.</div>