When and how can real world data analyses substitute for randomized controlled trials?

Franklin, Jessica M.; Schneeweiss, Sebastian

doi:10.1002/cpt.857

Cited by 316Advanced Causal Inference TechniquesStatistical Methods in Clinical TrialsPharmacovigilance and Adverse Drug Reactions

When and how can real world data analyses substitute for randomized controlled trials?

Franklin, Jessica M., Schneeweiss, Sebastian·2017·Clinical Pharmacology & Therapeutics

DOI 10.1002/cpt.857

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Abstract

Regulators consider randomized controlled trials (RCTs) as the gold standard for evaluating the safety and effectiveness of medications, but their costs, duration, and limited generalizability have caused some to look for alternatives. Real world evidence based on data collected outside of RCTs, such as registries and longitudinal healthcare databases, can sometimes substitute for RCTs, but concerns about validity have limited their impact. Greater reliance on such real world data (RWD) in regulatory decision making requires understanding why some studies fail while others succeed in producing results similar to RCTs. Key questions when considering whether RWD analyses can substitute for RCTs for regulatory decision making are WHEN one can study drug effects without randomization and HOW to implement a valid RWD analysis if one has decided to pursue that option. The WHEN is primarily driven by externalities not controlled by investigators, whereas the HOW is focused on avoiding known mistakes in RWD analyses.

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Real-world data complement RCT data by reflecting actual use of hysterectomy and UP surgery in practice, capturing how patient and clinical factors influence surgical decisions and outcomes, and controlling for these factors with biostatistical methods.
“Real-world data complement RCT data by reflecting actual use of hysterectomy and UP surgery in practice, capturing how patient and clinical factors influence surgical decisions and outcomes, and controlling for these factors with biostatistical methods.33,34”
From Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study (2025) · cites this paper as [33]

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Franklin, J. M., Glynn, R. J., Martin, D., & Schneeweiß, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1351 · Cited by 155 · see citing works
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Measuring prevalence and incidence of chronic conditions in claims and electronic health record databases
Rassen, J. A., Bartels, D. B., Schneeweiß, S., Patrick, A. R., & Murk, W. K. (2018) · Clinical Epidemiology · doi:10.2147/clep.s181242 · Cited by 136 · see citing works
An Exploratory Analysis of Real-World End Points for Assessing Outcomes Among Immunotherapy-Treated Patients With Advanced Non–Small-Cell Lung Cancer
Stewart, M. D., Norden, A. D., Dreyer, N. A., Henk, H. J., Abernethy, A. P., Chrischilles, E. A., Kushi, L. H., Mansfield, A. S., Khozin, S., Sharon, E., et al. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.18.00155 · Cited by 126 · see citing works
Nonrandomized Real‐World Evidence to Support Regulatory Decision Making: Process for a Randomized Trial Replication Project
Franklin, J. M., Pawar, A. M., Martin, D., Glynn, R. J., Levenson, M. S., Temple, R., & Schneeweiß, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1633 · Cited by 122 · see citing works
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Use of Health Care Databases to Support Supplemental Indications of Approved Medications
Fralick, M., Kesselheim, A. S., Avorn, J., & Schneeweiß, S. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3919 · Cited by 119 · see citing works
From Bit to Bedside: A Practical Framework for Artificial Intelligence Product Development in Healthcare
Higgins, D., & Madai, V. I. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000052 · Cited by 118 · see citing works
Reproducibility of real-world evidence studies using clinical practice data to inform regulatory and coverage decisions
Wang, S. V., Sreedhara, S. K., Schneeweiß, S., Initiative, R., Franklin, J. M., Gagne, J. J., Huybrechts, K. F., Patorno, E., Jin, Y., Lee, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32310-3 · Cited by 116 · see citing works
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Assessing the Risk for Gout With Sodium–Glucose Cotransporter-2 Inhibitors in Patients With Type 2 Diabetes
Fralick, M., Chen, S. K., Patorno, E., & Kim, S. C. (2020) · Annals of Internal Medicine · doi:10.7326/m19-2610 · Cited by 110 · see citing works
Development and Validation of a Pediatric Comorbidity Index
Sun, J. W., Bourgeois, F. T., Haneuse, S., Hernández–Dı́az, S., Landon, J. E., Bateman, B. T., & Huybrechts, K. F. (2020) · American Journal of Epidemiology · doi:10.1093/aje/kwaa244 · Cited by 102 · see citing works
Real-world studies no substitute for RCTs in establishing efficacy
Gerstein, H. C., McMurray, J., & Holman, R. R. (2019) · The Lancet · doi:10.1016/s0140-6736(18)32840-x · Cited by 97 · see citing works
Defining the role of real-world data in cancer clinical research: The position of the European Organisation for Research and Treatment of Cancer
Saesen, R., Hemelrijck, M. V., Bogaerts, J., Booth, C. M., Cornelissen, J. J., Dekker, A. L. A. J., Eisenhauer, E. A., Freitas, A., Gronchi, A., Hernán, M. Á., et al. (2023) · European Journal of Cancer · doi:10.1016/j.ejca.2023.03.013 · Cited by 97 · see citing works
Using Real-World Data to Predict Findings of an Ongoing Phase IV Cardiovascular Outcome Trial: Cardiovascular Safety of Linagliptin Versus Glimepiride
Patorno, E., Schneeweiß, S., Gopalakrishnan, C., Martin, D., & Franklin, J. M. (2019) · Diabetes Care · doi:10.2337/dc19-0069 · Cited by 94 · see citing works
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Harnessing Real-World Evidence to Advance Cancer Research
Tang, M., Pearson, S., Simes, R. J., & Chua, B. H. (2023) · Current Oncology · doi:10.3390/curroncol30020143 · Cited by 88 · see citing works
Automated data-adaptive analytics for electronic healthcare data to study causal treatment effects
Schneeweiß, S. (2018) · Clinical Epidemiology · doi:10.2147/clep.s166545 · Cited by 81 · see citing works
Real-World Evidence, Causal Inference, and Machine Learning
Crown, W. H. (2019) · Value in Health · doi:10.1016/j.jval.2019.03.001 · Cited by 76 · see citing works
The Structured Process to Identify Fit‐For‐Purpose Data: A Data Feasibility Assessment Framework
Gatto, N. M., Campbell, U. B., Rubinstein, E. R., Jaksa, A. A., Mattox, P. W., Mo, J., & Reynolds, R. F. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2466 · Cited by 76 · see citing works
Randomized Trials Versus Common Sense and Clinical Observation
Fanaroff, A. C., Califf, R. M., Harrington, R. A., Granger, C. B., McMurray, J. J. V., Patel, M. R., Bhatt, D. L., Windecker, S., Hernandez, A. F., Gibson, C. M., et al. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.05.069 · Cited by 76 · see citing works
Are Novel, Nonrandomized Analytic Methods Fit for Decision Making? The Need for Prospective, Controlled, and Transparent Validation
Eichler, H., König, F., Arlett, P. R., Enzmann, H. G., Humphreys, A., Pétavy, F., Schwarzer‐Daum, B., Sepodes, B. M. N., Vamvakas, S., & Rasi, G. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1638 · Cited by 69 · see citing works
Association of Remdesivir Treatment With Mortality Among Hospitalized Adults With COVID-19 in the United States
Chokkalingam, A. P., Hayden, J., Goldman, J. D., Li, H., Asubonteng, J. O., Mozaffari, E., Bush, C., Wang, J. R., Kong, A. M., Osinusi, A. O., et al. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.44505 · Cited by 68 · see citing works
Use of low‐dose quetiapine increases the risk of major adverse cardiovascular events: results from a nationwide active comparator‐controlled cohort study
Højlund, M., Andersen, K., Ernst, M. T., Correll, C. U., & Hallas, J. (2022) · World Psychiatry · doi:10.1002/wps.21010 · Cited by 59 · see citing works
Trends in the Use of Oral Anticoagulants for Adults With Venous Thromboembolism in the US, 2010-2020
Iyer, G. S., Tesfaye, H., Khan, N. F., Zakoul, H., & Bykov, K. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.4059 · Cited by 55 · see citing works
Risk of amputations associated with SGLT2 inhibitors compared to DPP‐4 inhibitors: A propensity‐matched cohort study
Adimadhyam, S., Lee, T. A., Calip, G. S., Marsh, D. E. S., Layden, B. T., & Schumock, G. T. (2018) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13459 · Cited by 55 · see citing works
Assessing the association between fluoroquinolones and emerging adverse drug reactions raised by regulatory agencies: An umbrella review
Gatti, M., Bianchin, M., Raschi, E., & Ponti, F. D. (2020) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2020.01.009 · Cited by 47 · see citing works
An Electronic Health Record Text Mining Tool to Collect Real‐World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma
van Laar, S. A., Handoko, K. B., Guchelaar, H., & Zwaveling, J. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1966 · Cited by 47 · see citing works
Biostatistical Considerations When Using RWD and RWE in Clinical Studies for Regulatory Purposes: A Landscape Assessment
Levenson, M. S., He, W., Chen, J., Fang, Y., Faries, D. E., Goldstein, B. A., Ho, M., Lee, K., Mishra-Kalyani, P. S., Rockhold, F. W., et al. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1883473 · Cited by 46 · see citing works
Development and Validation of a Nordic Multimorbidity Index Based on Hospital Diagnoses and Filled Prescriptions
Kristensen, K. B., Lund, L. C., Jensen, P. B., Broe, A., Rotbain, E. C., Damkier, P., Pottegård, A., Andersen, J. H., Højlund, M., Olesen, M. S., et al. (2022) · Clinical Epidemiology · doi:10.2147/clep.s353398 · Cited by 45 · see citing works
Association between Anticholinergic Medication Use and Risk of Dementia among Patients with Parkinson's Disease
Sheu, J., Tsai, M., Erickson, S. R., & Wu, C. (2019) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2305 · Cited by 45 · see citing works
Benchmarking Observational Analyses Against Randomized Trials: a Review of Studies Assessing Propensity Score Methods
Forbes, S. P., & Dahabreh, I. J. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-020-05713-5 · Cited by 45 · see citing works
Prevalence of Avoidable and Bias‐Inflicting Methodological Pitfalls in Real‐World Studies of Medication Safety and Effectiveness
Bykov, K., Patorno, E., D’Andrea, E., He, M., Lee, H., Graff, J. S., & Franklin, J. M. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2364 · Cited by 42 · see citing works
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Real‐World Evidence of Treatment Effects: The Useful and the Misleading
Schneeweiß, S. (2019) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1405 · Cited by 41 · see citing works
Real‐World Evidence for Assessing Pharmaceutical Treatments in the Context of COVID‐19
Franklin, J. M., Lin, K. J., Gatto, N. M., Rassen, J. A., Glynn, R. J., & Schneeweiß, S. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2185 · Cited by 41 · see citing works
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The Current Landscape in Biostatistics of Real-World Data and Evidence: Clinical Study Design and Analysis
Chen, J., Ho, M., Lee, K., Song, Y., Fang, Y., Goldstein, B. A., He, W., Irony, T., Jiang, Q., van der Laan, M. J., et al. (2021) · Statistics in Biopharmaceutical Research · doi:10.1080/19466315.2021.1883474 · Cited by 40 · see citing works
Real‐World Evidence: What It Is and What It Can Tell Us According to the International Society for Pharmacoepidemiology (ISPE) Comparative Effectiveness Research (CER) Special Interest Group (SIG)
Yuan, H., Ali, M. S., Brouwer, E., Girman, C. J., Guo, J. J., Lund, J. L., Patorno, E., Slaughter, J. L., Wen, X., & Bennett, D. (2018) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1086 · Cited by 39 · see citing works
Evaluation of randomized controlled trials: a primer and tutorial for mental health researchers
Harrer, M., Cuijpers, P. J., Schuurmans, L., Kaiser, T., Buntrock, C., van Straten, A., & Ebert, D. D. (2023) · Trials · doi:10.1186/s13063-023-07596-3 · Cited by 37 · see citing works
Methodological challenges using routine clinical care data for real-world evidence: a rapid review utilizing a systematic literature search and focus group discussion
Pfaffenlehner, M., Behrens, M., Zöller, D., Ungethüm, K., Günther, K., Rücker, V., Reese, J., Heuschmann, P. U., Kesselmeier, M., Remo, F., et al. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02440-x · Cited by 41 · see citing works
Safety of dapagliflozin in a broad population of patients with type 2 diabetes: Analyses from the DECLARE‐TIMI 58 study
Cahn, A., Raz, I., Bonaca, M. P., Mosenzon, O., Murphy, S. A., Yanuv, I., Rozenberg, A., Wilding, J. P. H., Bhatt, D. L., McGuire, D. K., et al. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14041 · Cited by 37 · see citing works
Real-world data analyses unveiled the immune-related adverse effects of immune checkpoint inhibitors across cancer types
Wang, F., Yang, S., Palmer, N., Fox, K. P., Kohane, I. S., Liao, K. P., Yu, K., & Kou, S. C. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00223-x · Cited by 37 · see citing works
Generating and using real-world data: A worthwhile uphill battle
Verkerk, K., & Voest, E. E. (2024) · Cell · doi:10.1016/j.cell.2024.02.012 · Cited by 39 · see citing works
Randomized Trial Replication Using Observational Data for Comparative Effectiveness of Secukinumab and Ustekinumab in Psoriasis
Yiu, Z. Z. N., Mason, K. J., Hampton, P. J., Reynolds, N. J., Smith, C. H., Lunt, M., Griffiths, C. E. M., Warren, R. B., & BADBIR Study Group (2020) · JAMA Dermatology · doi:10.1001/jamadermatol.2020.4202 · Cited by 34 · see citing works
The EMPagliflozin compaRative effectIveness and SafEty (EMPRISE) study programme: Design and exposure accrual for an evaluation of empagliflozin in routine clinical care
Patorno, E., Najafzadeh, M., Pawar, A., Franklin, J. M., Déruaz‐Luyet, A., Brodovicz, K. G., Ortiz, A. J. S., Bessette, L. G., Kulldorff, M., & Schneeweiß, S. (2019) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.103 · Cited by 33 · see citing works
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Real‐world evidence to support regulatory decision making: New or expanded medical product indications
Franklin, J. M., Liaw, K., Iyasu, S., Critchlow, C. W., & Dreyer, N. A. (2021) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5222 · Cited by 34 · see citing works
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Association of Type of Oral Anticoagulant Dispensed With Adverse Clinical Outcomes in Patients Extending Anticoagulation Therapy Beyond 90 Days After Hospitalization for Venous Thromboembolism
Pawar, A. M., Gagne, J. J., Gopalakrishnan, C., Iyer, G. S., Tesfaye, H., Brill, G. E., Chin, K., & Bykov, K. (2022) · JAMA · doi:10.1001/jama.2022.1920 · Cited by 34 · see citing works
Report on the current status of the use of real‐world data (RWD) and real‐world evidence (RWE) in drug development and regulation
Breckenridge, A. M., Breckenridge, R. A., & Peck, C. C. (2019) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.14026 · Cited by 32 · see citing works
Replication of randomized clinical trial results using real-world data: paving the way for effectiveness decisions
Sheffield, K. M., Dreyer, N. A., Murray, J. F., Faries, D. E., & Klopchin, M. N. (2020) · Journal of Comparative Effectiveness Research · doi:10.2217/cer-2020-0161 · Cited by 32 · see citing works
Role of Real-World Evidence in Informing Cancer Care: Lessons from Colorectal Cancer
Batra, A., & Cheung, W. Y. (2019) · Current Oncology · doi:10.3747/co.26.5625 · Cited by 31 · see citing works
Use of RWE to Inform Regulatory, Public Health Policy, and Intervention Priorities for the Developing World
McNair, D., Lumpkin, M., Kern, S. E., & Hartman, D. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2449 · Cited by 31 · see citing works
When Can Nonrandomized Studies Support Valid Inference Regarding Effectiveness or Safety of New Medical Treatments?
Franklin, J. M., Platt, R. G., Dreyer, N. A., London, A. J., Simon, G. E., Watanabe, J. H., Horberg, M. A., Hernandez, A. F., & Califf, R. M. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2255 · Cited by 33 · see citing works
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Observational designs for real-world evidence studies
Taur, S. R. (2022) · Perspectives in Clinical Research · doi:10.4103/picr.picr_217_21 · Cited by 31 · see citing works
Predicting Response to Tocilizumab Monotherapy in Rheumatoid Arthritis: A Real-world Data Analysis Using Machine Learning
Johansson, F., Collins, J. E., Yau, V., Guan, H., Kim, S. C., Losina, E., Sontag, D. A., Stratton, J., Trinh, H., Greenberg, J. D., et al. (2021) · The Journal of Rheumatology · doi:10.3899/jrheum.201626 · Cited by 30 · see citing works
Reflection on the enactment and impact of safety laws for regenerative medicine in Japan
Takashima, K., Morrison, M. C., & Minari, J. (2021) · Stem Cell Reports · doi:10.1016/j.stemcr.2021.04.017 · Cited by 29 · see citing works
Decreased risk of treatment failure with vedolizumab and thiopurines combined compared with vedolizumab monotherapy in Crohn’s disease
Kirchgesner, J., Desai, R. J., Schneeweiss, M. C., Beaugerie, L., Schneeweiß, S., & Kim, S. C. (2022) · Gut · doi:10.1136/gutjnl-2022-327002 · Cited by 29 · see citing works
External control arm analysis: an evaluation of propensity score approaches, G-computation, and doubly debiased machine learning
Loiseau, N., Trichelair, P., He, M., Andreux, M., Zaslavskiy, M., Wainrib, G., & Blum, M. G. B. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01799-z · Cited by 29 · see citing works
Design differences and variation in results between randomised trials and non-randomised emulations: meta-analysis of RCT-DUPLICATE data
Heyard, R., Held, L., Schneeweiß, S., & Wang, S. V. (2024) · BMJ Medicine · doi:10.1136/bmjmed-2023-000709 · Cited by 30 · see citing works
Using real world data to assess cardiovascular outcomes of two antidiabetic treatment classes
Stapff, M. (2018) · World Journal of Diabetes · doi:10.4239/wjd.v9.i12.252 · Cited by 28 · see citing works
Machine learning in predicting outcomes for stroke patients following rehabilitation treatment: A systematic review
Zu, W.-T., Huang, X., Xu, T., Du, L., Wang, Y.-M., Wang, L.-S., & Nie, W.-B. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287308 · Cited by 31 · see citing works
Advances in methodologies of negative controls: a scoping review
Yang, Q., Yang, Z., Cai, X., Zhao, H., Jia, J., & Sun, F. (2023) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2023.111228 · Cited by 28 · see citing works
Advancements in Electronic Medical Records for Clinical Trials: Enhancing Data Management and Research Efficiency
Lee, M., Kim, K., Shin, Y., Lee, Y. K., & Kim, T. J. (2025) · Cancers · doi:10.3390/cancers17091552 · Cited by 36 · see citing works
Assessing and Interpreting Real‐World Evidence Studies: Introductory Points for New Reviewers
Wang, S. V., & Schneeweiß, S. (2021) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.2398 · Cited by 27 · see citing works
Examining differences in retention on medication for opioid use disorder: An analysis of Ohio Medicaid data
Zhang, P., Tossone, K., Ashmead, R., Bickert, T., Bailey, E., Doogan, N., Mack, A., Schmidt, S., & Bonny, A. E. (2021) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2021.108686 · Cited by 27 · see citing works
Preventive effect of rebamipide on NSAID-induced lower gastrointestinal tract injury using FAERS and JADER
Imai, T., Hazama, K., Kosuge, Y., Suzuki, S., & Ootsuka, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-06611-y · Cited by 27 · see citing works
Real-World Evidence of Traditional Chinese Medicine (TCM) Treatment on Cancer: A Literature-Based Review
Peng, L., Zhang, K., Li, Y., Chen, L., Gao, H., & Chen, H. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/7770380 · Cited by 27 · see citing works
Analysis of a Real-World Progression Variable and Related Endpoints for Patients with Five Different Cancer Types
Torres, A. Z., Nussbaum, N. C., Parrinello, C. M., Bourla, A. B., Bowser, B. E., Wagner, S., Tabano, D. C., George, D. J., & Miksad, R. A. (2022) · Advances in Therapy · doi:10.1007/s12325-022-02091-8 · Cited by 26 · see citing works
A National Spinal Muscular Atrophy Registry for Real-World Evidence
Hodgkinson, V. L., Oskoui, M., Lounsberry, J. J., M’Dahoma, S., Butler, E. C., Campbell, C., MacKenzie, A., McMillan, H. J., Simard, L. R., Vajsar, J., et al. (2020) · Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · doi:10.1017/cjn.2020.111 · Cited by 25 · see citing works
Target Trial Emulation for Transparent and Robust Estimation of Treatment Effects for Health Technology Assessment Using Real-World Data: Opportunities and Challenges
Gomes, M., Latimer, N. R., Soares, M. O., Dias, S., Baio, G., Freemantle, N., Dawoud, D., Wailoo, A., & Grieve, R. (2022) · PharmacoEconomics · doi:10.1007/s40273-022-01141-x · Cited by 26 · see citing works
Glucose‐lowering medications and the risk of cancer: A methodological review of studies based on real‐world data
Bykov, K., He, M., Franklin, J. M., Garry, E. M., Seeger, J. D., & Patorno, E. (2019) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13766 · Cited by 25 · see citing works
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Real-World Evidence to Assess Medication Safety or Effectiveness in Children: Systematic Review
Lasky, T., Carleton, B., Horton, D. B., Kelly, L. E., Bennett, D., Czaja, A. S., Gifkins, D., Osokogu, O. U., & McMahon, A. W. (2020) · Drugs - Real World Outcomes · doi:10.1007/s40801-020-00182-y · Cited by 24 · see citing works
Real-World Data Analytics Fit for Regulatory Decision-Making
Schneeweiß, S., & Glynn, R. J. (2018) · American Journal of Law & Medicine · doi:10.1177/0098858818789429 · Cited by 23 · see citing works
A comparative study of clinical trial and real-world data in patients with diabetic kidney disease
Kurki, S. H., Halla-aho, V., Haußmann, M., Lähdesmäki, H. J., Leinonen, J., & Koskinen, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-51938-3 · Cited by 23 · see citing works
Mortality risk of antipsychotic augmentation for adult depression
Gerhard, T., Stroup, T. S., Correll, C. U., Setoguchi, S., Strom, B. L., Huang, C. S., Tan, Z., Crystal, S., & Olfson, M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239206 · Cited by 23 · see citing works
Transparency in real‐world evidence (RWE) studies to build confidence for decision‐making: Reporting RWE research in diabetes
Patorno, E., Schneeweiß, S., & Wang, S. V. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13918 · Cited by 22 · see citing works
Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials
Hong, Y. D., Jansen, J. P., Guerino, J., Berger, M. L., Crown, W. H., Goettsch, W., Mullins, C. D., Willke, R. J., & Orsini, L. S. (2021) · BMC Medicine · doi:10.1186/s12916-021-02176-1 · Cited by 22 · see citing works
The present and future scope of real‐world evidence research in diabetes: What questions can and cannot be answered and what might be possible in the future?
Zaccardi, F., Davies, M. J., & Khunti, K. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13929 · Cited by 22 · see citing works
Tell me what you want, what you really really want: Estimands in observational pharmacoepidemiologic comparative effectiveness and safety studies
Luijken, K., van EEKELEN, R., Gardarsdóttir, H., Groenwold, R. H. H., & van Geloven, N. (2023) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5620 · Cited by 21 · see citing works
Machine Learning Methods in Real-World Studies of Cardiovascular Disease
Zhou, J., You, D., Bai, J., Chen, X., Wu, Y., Wang, Z., Tang, Y., Zhao, Y., & Feng, G. (2023) · Cardiovascular Innovations and Applications · doi:10.15212/cvia.2023.0011 · Cited by 21 · see citing works
Observational research on sodium glucose co‐transporter‐2 inhibitors: A real breakthrough?
Raschi, E., Poluzzi, E., Fadini, G. P., Marchesini, G., & Ponti, F. D. (2018) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13468 · Cited by 21 · see citing works
Using an artificial intelligence tool incorporating natural language processing to identify patients with a diagnosis of ANCA-associated vasculitis in electronic health records
van Leeuwen, J. R., Penne, E. L., Rabelink, T. J., Knevel, R., & Teng, Y. K. O. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107757 · Cited by 20 · see citing works
A Statistical Roadmap for Journey from Real-World Data to Real-World Evidence
Fang, Y., Wang, H., & He, W. (2019) · Therapeutic Innovation & Regulatory Science · doi:10.1007/s43441-019-00008-2 · Cited by 20 · see citing works
External Comparator Groups Derived from Real-world Data Used in Support of Regulatory Decision Making: Use Cases and Challenges
Carrigan, G. J., Bradbury, B. D., Brookhart, M. A., Capra, W. B., Chia, V., Rothman, K. J., Sarsour, K., Taylor, M. D., & Brown, J. S. (2022) · Current Epidemiology Reports · doi:10.1007/s40471-022-00305-9 · Cited by 19 · see citing works
Hysterectomy versus uterine preservation for pelvic organ prolapse surgery: a prospective cohort study
Brennand, E. A., Scime, N. V., Huang, B., Edwards, A. D., Kim-Fine, S., Hall, J., Birch, C., Robert, M., & Carter Ramirez, A. (2025) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2024.10.021 · Cited by 18 · see citing works
Actionable Real-World Evidence to Improve Health Outcomes and Reduce Medical Spending Among Risk-Stratified Patients with Diabetes
Garry, E. M., Schneeweiß, S., Eapen, S., Petruski‐Ivleva, N., Cheever, E., Murk, W. K., Rajan, A., Rassen, J. A., Gambino, D., Jang, H. J., et al. (2019) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2019.25.12.1442 · Cited by 18 · see citing works
Key considerations in the design of real-world studies
Fang, Y., He, W., Wang, H., & Wu, M. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106091 · Cited by 18 · see citing works
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Authors: Franklin, J. M., & Schneeweiss, S.
Affiliations: Brigham and Women's Hospital (US)
Corresponding author: Franklin, Jessica M.
ORCID: Franklin, Jessica M.
Schneeweiss, Sebastian
Venue: Clinical Pharmacology & Therapeutics
Publisher: Wiley
Published: 2017-09-25
Volume: 102
Issue: 6
Pages: 924-933
ISSN: 0009-9236 (ISSN-L)
1532-6535
DOI: 10.1002/cpt.857
OpenAlex: W2745708468
Cited by: 316
References: 76
Citations by year: 2026: 41 · 2025: 40 · 2024: 33 · 2023: 32 · 2022: 43 · 2021: 41 · 2020: 41 · 2019: 27 · 2018: 15 · 2017: 3
FWCI: 16.36
Type: journal article
Language: en
Open access: closed
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Clinical Pharmacology & Therapeutics (publishedVersion)
PubMed (publishedVersion)
Funders: Brigham and Women's Hospital
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
Keywords: real-world data, real-world evidence, randomized controlled trials, regulatory decision-making, generalizability, confounding, observational study design, registry, health databases
MeSH terms: Data Collection, Decision Making, Humans, Registries, Statistics as Topic, Randomized Controlled Trials as Topic, Databases, Factual, Drug Approval

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