For rare real-world data involving off-label drug use or comorbidity-associated polypharmacy, researchers have increasingly adopted target trial emulation to investigate drug repurposing for target indications. The success of such studies hinges on rigorous trial design and strict adherence to predefined protocols and standardized pipelines. Key elements in the trial design include the precise definition of inclusion and exclusion criteria, the selection of trial and control drugs and determination of treatment allocation time, the determination of appropriate efficacy endpoints for the target indication, the identification of causal estimands, and the development of robust strategies for confounding adjustment. The execution of the trial follows a structured process: screening eligible subjects, extracting relevant drug exposure data, constructing treatment and control groups, emulating the target trial, and ultimately generating hypotheses for drug repurposing through statistical inference. Propensity score methods, including stratification, matching and weighting techniques, are critical tools for addressing confounding bias and ensuring accurate estimation of causal effects. In recent years, creative  progress has been made in target trial emulation, particularly in the calculation of propensity scores. Researchers have adopted advanced machine learning techniques, to enhance variable selection and have actively explored the use of innovative methods of digital intelligence technology like classification and regression trees, support vector machines, and deep learning for the application of propensity score calculation. Target trial emulation based on real-world data has achieved remarkable advancements in drug repurposing, demonstrating broad application prospects, particularly in cardiovascular diseases, metabolic disorders, Alzheimer's disease, and cancer.

1.Zong N, Wen A, Moon S, et al. Computational drug repurposing based on electronic health records: a scoping review[J]. NPJ Digit Med, 2022, 5(1): 77. DOI: 10.1038/s41746-022-00617-6.

2.Tan GSQ, Sloan EK, Lambert P, et al. Drug repurposing using real-world data[J]. Drug Discov Today, 2023, 28(1): 103422. DOI: 10.1016/j.drudis.2022.103422.

3.Ozery-Flato M, Goldschmidt Y, Shaham O, et al. Framework for identifying drug repurposing candidates from observational healthcare data[J]. JAMIA Open, 2020, 3(4): 536-544. DOI: 10.1093/jamiaopen/ooaa048.

4.国家药品监督管理局. 真实世界证据支持药物研发与审评的指导原则（试行）[S/OL]. (2020-01-07) [2025-07-23]. https://www.cde.org.cn/zdyz/domesticinfopage?zdyzIdCODE=db4376287cb678882a3f6c89.

5.Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available[J]. Am J Epidemiol, 2016, 183(8): 758-764. DOI: 10.1093/aje/kwv254.

6.Matthews AA, Danaei G, Islam N, et al. Target trial emulation: applying principles of randomised trials to observational studies[J]. Br Med J, 2022, 378: e071108. DOI: 10.1136/bmj-2022-071108.

7.Weberpals J, Becker T, Davies J, et al. Deep learning-based propensity scores for confounding control in comparative effectiveness research a large-scale, real-world data study[J]. Epidemiology, 2021, 32(3): 378-388. DOI: 10.1097/ede.0000000000001338.

8.International Council for Harmonisation. Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials E9(R1) [R/OL]. (2019-11-20) [2025-07-23]. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf.

9.Urner M, Barnett AG, Li Bassi G, et al. Venovenous extracorporeal membrane oxygenation in patients with acute covid-19 associated respiratory failure: comparative effectiveness study[J]. Br Med J, 2022, 377: e068723. DOI: 10.1136/bmj-2021-068723.

10.Zang C, Zhang H, Xu J, et al. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data[J]. Nat Commun, 2023, 14(1): 8180. DOI: 10.1038/s41467-023-43929-1.

11.Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies[J]. Stat Med, 2015, 34(28): 3661-3679. DOI: 10.1002/sim.6607.

12.Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score[J]. J Am Stat Assoc, 1984, 79(387): 516-524. DOI: 10.2307/2288398.

13.Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects[J]. Biometrika, 1983, 70(1): 41-55. DOI: 10.1093/biomet/70.1.41.

14.Imbens GW, Rubin DB, eds. Causal inference in statistics, social, and biomedical sciences[M]. Cambridge: Cambridge University Press, 2015: 274-276, 382.

15.Austin PC. Goodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score[J]. Pharmacoepidemiol Drug Saf, 2008, 17(12): 1202-1217. DOI: 10.1002/pds.1673.

16.Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples[J]. Stat Med, 2009, 28(25): 3083-3107. DOI: 10.1002/sim.3697.

17.Normand SLT, Landrum MB, Guadagnoli E, et al. Validating recommendations for coronary angiography following acute myocardial infarction in the elderly: a matched analysis using propensity scores[J]. J Clin Epidemiol, 2001, 54(4): 387-398. DOI: 10.1016/S0895-4356(00)00321-8.

18.Austin PC, Mamdani MM. A comparison of propensity score methods: a case-study estimating the effectiveness of post-AMI statin use[J]. Stat Med, 2006, 25(12): 2084-2106. DOI: 10.1002/sim.2328.

19.Lunceford JK, Davidian M. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study[J]. Stat Med, 2004, 23(19): 2937-2960. DOI: 10.1002/sim.1903.

20.Austin PC. The performance of different propensity score methods for estimating marginal hazard ratios[J]. Stat Med, 2013, 32(16): 2837-2849. DOI: 10.1002/sim.5705.

21.Buchanan AL, Hudgens MG, Cole SR, et al. Worth the weight: using inverse probability weighted Cox models in AIDS research[J]. AIDS Res Hum Retroviruses, 2014, 30(12): 1170-1177. DOI: 10.1089/aid.2014.0037.

22.Karpatne A, Jia X, Kumar V. Knowledge-guided machine learning: current trends and future prospects[EB/OL]. (2024-05-01) [2025-07-23]. https://doi.org/10.48550/arXiv.2403.15989.

23.Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and Meta-classifiers as alternatives to logistic regression[J]. J Clin Epidemiol, 2010, 63(8): 826-833. DOI: 10.1016/j.jclinepi.2009. 11.020.

24.Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning[J]. Stat Med, 2010, 29(3): 337-346. DOI: 10.1002/sim.3782.

25.Liu R, Wei L, Zhang P. A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data[J]. Nat Mach Intell, 2021, 3(1): 68-75. DOI: 10.1038/s42256-020-00276-w.

26.Ghosh S, Bian J, Guo Y, et al. Deep propensity network using a sparse autoencoder for estimation of treatment effects[J]. J Am Med Inform Assoc, 2021, 28(6): 1197-1206. DOI: 10.1093/jamia/ocaa346.

27.Fisher ML, Gottlieb SS, Plotnick GD, et al. Beneficial effects of metoprolol in heart failure associated with coronary artery disease: a randomized trial[J]. J Am Coll Cardiol, 1994, 23(4): 943-950. DOI: 10.1016/0735-1097(94)90641-6.

28.Wong TY, Simo R, Mitchell P. Fenofibrate - a potential systemic treatment for diabetic retinopathy?[J]. Am J Ophthalmol, 2012, 154(1): 6-12. DOI: 10.1016/j.ajo.2012.03.013.

29.Charpignon ML, Vakulenko-Lagun B, Zheng B, et al. Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia[J]. Nat Commun, 2022, 13(1): 7652. DOI: 10.1038/s41467-022-35157-w.

30.Fang J, Zhang P, Zhou Y, et al. Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease[J]. Nat Aging, 2021, 1(12): 1175-1188. DOI: 10.1038/s43587-021-00138-z.

31.Yan C, Grabowska ME, Dickson AL, et al. Leveraging generative AI to prioritize drug repurposing candidates for Alzheimer's disease with real-world clinical validation[J]. NPJ Digit Med, 2024, 7(1): 46. DOI: 10.1038/s41746-024-01038-3.

32.Wu Y, Warner JL, Wang L, et al. Discovery of noncancer drug effects on survival in electronic health records of patients with cancer: a new paradigm for drug repurposing[J]. JCO Clin Cancer Inform, 2019, 3: 1-9. DOI: 10.1200/cci.19.00001.

33.Dickerman BA, García-Albéniz X, Logan RW, et al. Emulating a target trial in case-control designs: an application to statins and colorectal cancer[J]. Int J Epidemiol, 2020, 49(5): 1637-1646. DOI: 10.1093/ije/dyaa144.

34.Hubbard RA, Gatsonis CA, Hogan JW, et al. "Target trial emulation" for observational studies - potential and pitfalls[J]. N Engl J Med, 2024, 391(21): 1975-1977. DOI: 10.1056/NEJMp2407586.

35.Xie J, Wu EQ, Wang S, et al. Real-world data for healthcare research in China: call for actions[J]. Value Health Reg Issues, 2022, 27: 72-81. DOI: 10.1016/j.vhri.2021.05.002.

36.Zhang YF, Zhang H, Lipton Z, et al. Can transformers be strong treatment effect estimators?[EB/OL]. (2022-10-17) [2025-07-23]. https://doi.org/10.48550/arXiv.2202.01336.

37.Liu R, Chen PY, Zhang P. CURE: a deep learning framework pre-trained on large-scale patient data for treatment effect estimation[J]. Patterns, 2024, 5(6): 100973. DOI: 10.1016/j.patter.2024.100973.