Popper K. Science: conjectures and refutations. In: Mace CA, editor. A lecture given at Peterhouse, Cambridge, in Summer 1953, as part of a course on developments and trends in contemporary British philosophy, organized by the British Council; originally published under the title ‘Philosophy of Science: a Personal Report’ in British Philosophy in Mid-Century, 1957.
Young SS, Karr A. Deming, data and observational studies. Significance. 2011;8(3):116–20.
Concato J, Shah N, Horwitz RI. Randomized, controlled trials, observational studies, and the hierarchy of research designs. N Engl J Med. 2000;342(25):1887–92.
Benson K, Hartz AJ. A comparison of observational studies and randomized, controlled trials. N Engl J Med. 2000;342(25):1878–86.
Danaei G, Tavakkoli M, Hernán MA. Bias in observational studies of prevalent users: lessons for comparative effectiveness research from a meta-analysis of statins. Am J Epidemiol. 2012;175(4):250–62.
MacLehose RR, Reeves BC, Harvey IM, Sheldon TA, Russell IT, Black AM. A systematic review of comparisons of effect sizes derived from randomised and non-randomised studies. Health Technol Assess. 2000;4(34):1–154.
Furlan AD, Tomlinson G, Jadad AA, Bombardier C. Methodological quality and homogeneity influenced agreement between randomized trials and nonrandomized studies of the same intervention for back pain. J Clin Epidemiol. 2008;61(3):209–31.
Abraham NS, Byrne CJ, Young JM, Solomon MJ. Meta-analysis of well-designed nonrandomized comparative studies of surgical procedures is as good as randomized controlled trials. J Clin Epidemiol. 2010;63(3):238–45.
Suissa S. Randomized trials built on sand: examples from COPD, hormone therapy, and cancer. Rambam Maimonides Med J. 2012;3(3):e0014.
Tuma RS. Statisticians set sights on observational studies. J Natl Cancer Inst. 2007;99(9):664–5, 8.
Varas-Lorenzo C, Garcia-Rodriguez LA, Perez-Gutthann S, Duque-Oliart A. Hormone replacement therapy and incidence of acute myocardial infarction. A population-based nested case–control study. Circulation. 2000;101(22):2572–8.
Grodstein F, Stampfer MJ, Manson JE, Colditz GA, Willett WC, Rosner B, et al. Postmenopausal estrogen and progestin use and the risk of cardiovascular disease. N Engl J Med. 1996;335(7):453–61.
Wilson PW, Garrison RJ, Castelli WP. Postmenopausal estrogen use, cigarette smoking, and cardiovascular morbidity in women over 50. The Framingham Study. N Engl J Med. 1985;313(17):1038–43.
Manson JE, Hsia J, Johnson KC, Rossouw JE, Assaf AR, Lasser NL, et al. Estrogen plus progestin and the risk of coronary heart disease. N Engl J Med. 2003;349(6):523–34.
Hernán MA, Robins JM, García Rodríguez LA. Discussion on “Statistical Issues Arising in the Women’s Health Initiative”. Biometrics. 2005;61(4):922–30.
Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19(6):766–79.
Cardwell CR, Abnet CC, Cantwell MM, Murray LJ. Exposure to oral bisphosphonates and risk of esophageal cancer. JAMA J Am Med Assoc. 2010;304(6):657–63.
Green J, Czanner G, Reeves G, Watson J, Wise L, Beral V. Oral bisphosphonates and risk of cancer of oesophagus, stomach, and colorectum: case-control analysis within a UK primary care cohort. BMJ. 2010;341:c4444.
Meier CR, Schlienger RG, Kraenzlin ME, Schlegel B, Jick H. HMG-CoA reductase inhibitors and the risk of fractures. JAMA J Am Med Assoc. 2000;283(24):3205–10.
van Staa TP, Wegman S, de Vries F, Leufkens B, Cooper C. Use of statins and risk of fractures. JAMA J Am Med Assoc. 2001;285(14):1850–5.
de Vries F, de Vries C, Cooper C, Leufkens B, van Staa TP. Reanalysis of two studies with contrasting results on the association between statin use and fracture risk: the General Practice Research Database. Int J Epidemiol. 2006;35(5):1301–8.
Ioannidis JP, Tzoulaki I. Minimal and null predictive effects for the most popular blood biomarkers of cardiovascular disease. Circ Res. 2012;110(5):658–62.
McDonald CJ. The evolution of Intel’s copy exactly! Technology transfer method. Intel Technol J. 1998;Q4:1–6.
Terwiesch C, Xu Y. The copy exactly ramp-up strategy: trading-off learning with process change. August 4, 2003, cited 2012 December 24. http://qbox.wharton.upenn.edu/documents/opim/research/P6.pdf.
Rothwell PM. External validity of randomised controlled trials: “to whom do the results of this trial apply?”. Lancet. 2005;365(9453):82–93.
Rothwell PM. Factors that can affect the external validity of randomised controlled trials. PLoS Clin Trials. 2006;1(1):e9.
Stang PE, Ryan PB, Overhage JM, Schuemie MJ, Hartzema AG, Welebob E. Variation in choice of study design: findings from the epidemiology design decision inventory and evaluation (EDDIE) survey. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0103-1.
The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on methodological standards in pharmacoepidemiology (revision 1). EMA/95098/2010. Cited 2013 January 23. http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideofMethStandardsinPE.pdf.
Gagne JJ, Fireman B, Ryan PB, Maclure M, Gerhard T, Toh S, et al. Design considerations in an active medical product safety monitoring system. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):32–40.
Gagne JJ, Nelson JC, Fireman B, Seeger JD, Toh D, Gerhard T, et al. Taxonomy for monitoring methods within a medical product safety surveillance system: year two report of the mini-sentinel taxonomy project workgroup (workgroup) 2012, cited 2012 October 29. http://www.mini-sentinel.org/work_products/Statistical_Methods/Mini-Sentinel_Methods_Taxonomy-Year-2-Report.pdf.
Taleb NN. The Black Swan: the impact of the highly improbable. New York: Random House; 2010.
Avorn J. In defense of pharmacoepidemiology—embracing the yin and yang of drug research. N Engl J Med. 2007;357(22):2219–21.
Madigan D, Ryan PB, Schuemie M, Stang PE, Overhage JM, Hartzema AG, et al. Evaluating the impact of database heterogeneity on observational study results. Am J Epidemiol. 2013;178(4):645–51.
Kuhn TS. The structure of scientific revolutions. 3rd ed. Chicago: University of Chicago Press; 1996.
Suissa S. Time-related biases in pharmacoepidemiology. In: International Society of Pharmacoepidemiology mid-year meeting, Miami Beach, Florida, 2012.
Prasad V, Jena AB. Prespecified falsification end points: can they validate true observational associations? JAMA J Am Med Assoc. 2013;309(3):241–2.
Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2013. doi:10.1002/sim.5925.
Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012;31(30):4401–15.
Overhage JM, Ryan PB, Reich CG, Hartzema AG, Stang PE. Validation of a common data model for active safety surveillance research. J Am Med Inf Assoc JAMIA. 2012;19(1):54–60.
Reich C, Ryan PB, Stang PE, Rocca M. Evaluation of alternative standardized terminologies for medical conditions within a network of observational healthcare databases. J Biomed Inf. 2012;45(4):689–96.
Ryan PB, Schuemie MJ, Gruber S, Zorych I, Madigan D. Empirical performance of a new user cohort method: Lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0099-6.
Madigan D, Schuemie MJ, Ryan PB. Empirical performance of the case–control method: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0105-z.
Suchard MA, Zorych I, Simpson SE, Schuemie MJ, Ryan PB, Madigan D. Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0100-4.
Ryan PB, Schuemie MJ, Madigan D. Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0101-3.
Schuemie MJ, Madigan D, Ryan PB. Empirical performance of LGPS and LEOPARD: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0107-x.
Norén GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigna D. Empirical performance of the calibrated self-controlled cohort analysis within temporal pattern discovery: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0095-x.
DuMouchel B, Ryan PB, Schuemie MJ, Madigan D. Evaluation of disproportionality safety signaling applied to healthcare databases. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0106-y.
Coloma PM, Trifirò G, Schuemie MJ, Gini R, Herings R, Hippisley-Cox J, et al. Electronic healthcare databases for active drug safety surveillance: is there enough leverage? Pharmacoepidemiol Drug Saf. 2012;21(6):611–21.
Hartzema AG, Reich C, Ryan PB, Stang PE, Madigna D, Welebob E, et al. Managing data quality for a drug safety surveillance system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0098-7.
Hansen RA, Gray MD, Fox BI, Hollingsworth JC, Gao J, Zeng P. How well do various health outcome definitions used in observational studies identify cases that are consistent with expert opinion? Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0104-0.
Reich C, Ryan PB, Schuemie MJ. Alternative outcome definitions and their effect on the performance of methods for observational outcome studies. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0111-1.
Reich CG, Ryan PB, Suchard MA. The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0112-0.
Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salame G, et al. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf. 2009;18(12):1176–84.
Ryan PB, Schuemie MJ, Welebob E, Duke J, Valentine S, Hartzema AG. Defining a reference set to support methodological research in drug safety. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0097-8.
Schuemie MJ, Gini R, Coloma PM, Straatman H, Herings RMC, Pedersen L, et al. Replication of the OMOP experiment in Europe: evaluating methods for risk identification in electronic health record databases. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0109-8.
Ryan PB, Schuemie MJ. Evaluating performance of risk identification methods through a large-scale simulation of observational data. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0110-2.
Hand DJ. Measuring classifier performance: a coherent alternative to the area under the ROC curve. Mach Learn. 2009;77(1):103–23.
Tisdale J, Miller D. Drug-induced diseases: prevention, detection, and management. 2nd ed. USA: American Society of Health-System Pharmacists; 2010.