Ahsan, H., McInerney, D.J., Kim, J., Potter, C., Young, G., Amir, S., Wallace, B.C.: Retrieving evidence from EHRs with llms: Possibilities and challenges. Proc. Mach. Learn. Res. 248, 489–505 (2024)

PubMed  PubMed Central  Google Scholar 

Audigier, V., White, I.R., Jolani, S., Debray, T.P.A., Quartagno, M., Carpenter, J., van Buuren, S., Resche-Rigon, M.: Multiple imputation for multilevel data with continuous and binary variables. Stat. Sci. 33(2), 160–183 (2018). https://doi.org/10.1214/18-STS646

Article  Google Scholar 

Baldwin, D.S., Loft, H., Dragheim, M.: A randomised, double-blind, placebo controlled, duloxetine-referenced, fixed-dose study of three dosages of Lu AA21004 in acute treatment of major depressive disorder (MDD). Eur. Neuropsychopharmacology: J. Eur. Coll. Neuropsychopharmacol 22(7), 482–491 (2012). https://doi.org/10.1016/j.euroneuro.2011.11.008

Article  CAS  Google Scholar 

Boulenger, J.-P., Loft, H., Olsen, C.K.: Efficacy and safety of Vortioxetine (Lu AA21004), 15 and 20 mg/day: A randomized, double-blind, placebo-controlled, duloxetine-referenced study in the acute treatment of adult patients with major depressive disorder. Int. Clin. Psychopharmacol. 29(3), 138 (2014). https://doi.org/10.1097/YIC.0000000000000018

Article  PubMed  PubMed Central  Google Scholar 

Brantner, C.L., Chang, T.-H., Nguyen, T.Q., Hong, H., Stefano, L.D., Stuart, E.A.: Methods for integrating trials and Non-experimental data to examine treatment effect heterogeneity. Statistical Science 38(4), 640–654 (2023). https://doi.org/10.1214/23-sts890

Article  PubMed  PubMed Central  Google Scholar 

Brantner, C.L., Nguyen, T.Q., Tang, T., Zhao, C., Hong, H., Stuart, E.A.: Comparison of methods that combine multiple randomized trials to estimate heterogeneous treatment effects. Stat. Med. 43(7), 1291–1314 (2024). https://doi.org/10.1002/sim.9955

Article  PubMed  PubMed Central  Google Scholar 

Cameron, C., Fireman, B., Hutton, B., Clifford, T., Coyle, D., Wells, G., Dormuth, C.R., Platt, R., Toh, S.: Network meta-analysis incorporating randomized controlled trials and non-randomized comparative cohort studies for assessing the safety and effectiveness of medical treatments: Challenges and opportunities. Systematic Reviews 4(1), 147 (2015). https://doi.org/10.1186/s13643-015-0133-0

Article  PubMed  PubMed Central  Google Scholar 

Castanedo, F.: A Review of Data Fusion Techniques. The Scientific World Journal 2013, 704504 (2013). https://doi.org/10.1155/2013/704504

Article  PubMed  PubMed Central  Google Scholar 

Cheng, D., Cai, T.: Adaptive combination of randomized and observational data. (2021). ArXiv:2111.15012. http://arxiv.org/abs/2111.15012

Cheng, C., Messerschmidt, L., Bravo, I., Waldbauer, M., Bhavikatti, R., Schenk, C., Grujic, V., Model, T., Kubinec, R., Barceló, J.: A general primer for data harmonization. Sci. Data. 11(1), 152 (2024). https://doi.org/10.1038/s41597-024-02956-3

Article  PubMed  PubMed Central  Google Scholar 

Colnet, B., Mayer, I., Chen, G., Dieng, A., Li, R., Varoquaux, G., Vert, J.-P., Josse, J., Yang, S.: Causal inference methods for combining randomized trials and observational studies: A review. (2023) (No. arXiv:2011.08047). http://arxiv.org/abs/2011.08047

D’Agostino, A., English, C.D., Rey, J.A.: Vortioxetine (Brintellix): A new serotonergic antidepressant. Pharm. Ther 40(1), 36–40 (2015)

Google Scholar 

Degtiar, I., Rose, S.: A review of generalizability and transportability. Annual Rev. Stat. Its Application. 10(10, 2023), 501–524 (2023). https://doi.org/10.1146/annurev-statistics-042522-103837

Article  Google Scholar 

Duloxetine—StatPearls—NCBI Bookshelf. (n.d.). Retrieved January 29, from (2025). https://www-ncbi-nlm-nih-gov.proxy.lib.duke.edu/books/NBK549806/

Efthimiou, O., Mavridis, D., Debray, T.P.A., Samara, M., Belger, M., Siontis, G.C.M., Leucht, S., Salanti, G.: Combining randomized and non-randomized evidence in network meta‐analysis. Stat. Med. 36(8), 1210–1226 (2017). https://doi.org/10.1002/sim.7223 & on behalf of GetReal Work Package 4

Forrest, C.B., McTigue, K.M., Hernandez, A.F., Cohen, L.W., Cruz, H., Haynes, K., Kaushal, R., Kho, A.N., Marsolo, K.A., Nair, V.P., Platt, R., Puro, J.E., Rothman, R.L., Shenkman, E.A., Waitman, L.R., Williams, N.A., Carton, T.W.: PCORnet® 2020: Current state, accomplishments, and future directions. Journal of Clinical Epidemiology 129, 60–67 (2021). https://doi.org/10.1016/j.jclinepi.2020.09.036

Article  PubMed  Google Scholar 

Freidlin, B., Korn, E.L.: Augmenting randomized clinical trial data with historical control data: Precision medicine applications. JNCI: Journal of the National Cancer Institute 115(1), 14–20 (2023). https://doi.org/10.1093/jnci/djac185

Article  PubMed  PubMed Central  Google Scholar 

Gagnon-Bartsch, J.A., Sales, A.C., Wu, E., Botelho, A.F., Erickson, J.A., Miratrix, L.W., Heffernan, N.T.: Precise Unbiased Estimation in Randomized Experiments using Auxiliary Observational Data. (2023) (No. arXiv:2105.03529).http://arxiv.org/abs/2105.03529

Getzen, E., Ungar, L., Mowery, D., Jiang, X., Long, Q.: Mining for equitable health: Assessing the impact of missing data in electronic health records. J. Biomed. Inform. 139, 104269 (2023). https://doi.org/10.1016/j.jbi.2022.104269

Article  PubMed  PubMed Central  Google Scholar 

Goldstein, B.A., Bhavsar, N.A., Phelan, M., Pencina, M.J.: Controlling for informed presence bias due to the number of health encounters in an electronic health record. Am. J. Epidemiol. 184(11), 847–855 (2016). https://doi.org/10.1093/aje/kww112

Article  PubMed  PubMed Central  Google Scholar 

Goldstein, B.A., Phelan, M., Pagidipati, N.J., Peskoe, S.B.: How and when informative visit processes can bias inference when using electronic health records data for clinical research. Journal of the American Medical Informatics Association 26(12), 1609–1617 (2019). https://doi.org/10.1093/jamia/ocz148

Article  PubMed  PubMed Central  Google Scholar 

Hannan, E.L.: Randomized clinical trials and observational studies. JACC: Cardiovasc. Interventions 1(3), 211–217 (2008). https://doi.org/10.1016/j.jcin.2008.01.008

Article  Google Scholar 

Hernán, M.A., Robins, J.M.: Using big data to emulate a target trial when a randomized trial is not available. Am. J. Epidemiol. 183(8), 758–764 (2016). https://doi.org/10.1093/aje/kwv254

Article  PubMed  PubMed Central  Google Scholar 

Hernán, M.A., Wang, W., Leaf, D.E.: Target trial emulation: A framework for causal inference from observational data. JAMA 328(24), 2446–2447 (2022). https://doi.org/10.1001/jama.2022.21383

Article  PubMed  Google Scholar 

Hripcsak, G., Duke, J. D., Shah, N. H., Reich, C. G., Huser, V., Schuemie, M. J.,Suchard, M. A., Park, R. W., Wong, I. C. K., Rijnbeek, P. R., van der Lei, J., Pratt,N., Noré, N, G. N., Li, Y.-C., Stang, P. E., Madigan, D., & Ryan, P. B. (2015).Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers. In MEDINFO 2015: eHealth-enabled Health (pp. 574–578). IOS Press. https://doi.org/10.3233/978-1-61499-564-7-574

Hubbard, R.A., Gatsonis, C.A., Hogan, J.W., Hunter, D.J., Normand, S.-L.T., Troxel, A.B.: Target trial emulation for observational Studies—Potential and pitfalls. N. Engl. J. Med. 391(21), 1975–1977 (2024). https://doi.org/10.1056/NEJMp2407586

Article  PubMed  Google Scholar 

Jolani, S., Debray, T.P.A., Koffijberg, H., van Buuren, S., Moons, K.G.M.: Imputation of systematically missing predictors in an individual participant data meta-analysis: A generalized approach using MICE. Stat. Med. 34(11), 1841–1863 (2015). https://doi.org/10.1002/sim.6451

Article  PubMed  Google Scholar 

Kallus, N., Puli, A.M., Shalit, U.: Removing Hidden Confounding by Experimental Grounding. Advances in Neural Information Processing Systems, 31. (2018). https://proceedings.neurips.cc/paper_files/paper/2018/hash/566f0ea4f6c2e947f36795c8f58ba901-Abstract.html

Kim, H.K., Lenze, E.J., Ainsworth, N.J., Blumberger, D.M., Brown, P.J., Flint, A.J., Karp, J.F., Lavretsky, H., Lenard, E., Miller, J.P., Reynolds, C.F., Roose, S.P., Mulsant, B.H.: Relationship between patient health questionnaire (PHQ-9) and Montgomery-Asberg depression rating scale (MADRS) total scores in older adults with major depressive disorder: An analysis of the OPTIMUM clinical trial. Journal of Affective Disorders 361, 651–658 (2024). https://doi.org/10.1016/j.jad.2024.06.068

Article  PubMed  Google Scholar 

Kroenke, K., Spitzer, R.L., Williams, J.B.: The PHQ-9: Validity of a brief depression severity measure. J. Gen. Intern. Med. 16(9), 606–613 (2001). https://doi.org/10.1046/j.1525-1497.2001.016009606.x

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kurz, C.F., Krzywinski, M., Altman, N.: Propensity score weighting. Nat. Methods 22(4), 638–640 (2025). https://doi.org/10.1038/s41592-025-02629-y

Article  CAS  PubMed  Google Scholar 

Li, P., Stuart, E.A., Allison, D.B.: Multiple imputation: A flexible tool for handling missing data. JAMA 314(18), 1966–1967 (2015). https://doi.org/10.1001/jama.2015.15281

Article  CAS  PubMed  PubMed Central  Google Scholar 

Li, G., Wang, X., Ma, D.: Vortioxetine versus Duloxetine in the treatment of patients with major depressive disorder: A Meta-Analysis of randomized controlled trials. Clin. Drug Investig. 36(7), 509–517 (2016). https://doi.org/10.1007/s40261-016-0396-9

Article  CAS  PubMed  Google Scholar 

Ma, M.A., Gutiérrez, D.E., Frausto, J.M., Al-Delaimy, W.K.: Minority representation in clinical trials in the united states: Trends over the past 25 years. Mayo Clin. Proc. 96(1), 264–266 (2021). https://doi.org/10.1016/j.mayocp.2020.10.027

Article  PubMed  Google Scholar