Heterogeneity analysis methods for therapeutic effects and their application in traditional Chinese medicine scenarios_Chinese Journal of Evidence-Based Medicine

Authors：

HUANG Nian ¹ , ZHANG Wen ¹ , LAI Yingsi ¹ , CHEN Xinlin ² , ZHANG Wangjian ¹ ,  GU Jing ¹ , FANG Jiqian ¹

1. Department of Medical statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, P. R. China;
2. School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;

Corresponding?author：

GU Jing, Email: gujing5@mail.sysu.edu.cn

Keywords：

Traditional Chinese medicine; Individualized treatment; Heterogeneity of treatment effect

DOI：

10.7507/1672-2531.202508116

Video：

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Traditional Chinese medicine (TCM) emphasizes syndrome differentiation and treatment, whereas commonly used evidence-based efficacy evaluation approaches are not aligned with TCM’s individualized diagnostic and therapeutic paradigm. “Average-based” assessments that ignore individual differences not only cannot accurately reflect the true efficacy of TCM interventions but also fail to guide TCM clinical practice. Accordingly, this review systematically summarizes commonly used methods for capturing the heterogeneity of treatment effect and proposes their potential applications in TCM clinical research, aiming to provide efficacy evaluation approaches that better accord with TCM’s diagnostic and therapeutic characteristics and, in turn, to furnish high-quality quantitative evidence for TCM clinical practice.

Citation： HUANG Nian, ZHANG Wen, LAI Yingsi, CHEN Xinlin, ZHANG Wangjian, GU Jing, FANG Jiqian. Heterogeneity analysis methods for therapeutic effects and their application in traditional Chinese medicine scenarios. Chinese Journal of Evidence-Based Medicine, 2026, 26(5): 572-579. doi: 10.7507/1672-2531.202508116 Copy

1.	Kuhlemeier A, Desai Y, Tonigan A, et al. Applying methods for personalized medicine to the treatment of alcohol use disorder. J Consult Clin Psychol, 2021, 89(4): 288-300.
2.	Schaffhausen J. What precisely is precision medicine. Trends Pharmacol Sci, 2017, 38(1): 1-2.
3.	符宇, 邵明義, 燕樹勛, 等. 真實世界研究與中醫臨床療效評價. 中醫雜志, 2019, 60(7): 546-550.
4.	陳健, 陳啟龍, 蘇式兵. 中醫藥精準醫療的思考與探索. 世界科學技術-中醫藥現代化, 2016, 18(4): 557-562.
5.	Kent DM, Paulus JK, van Klaveren D, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement. Ann Intern Med, 2020, 172(1): 35-45.
6.	Hamaya R, Hara K, Manson JE, et al. Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial. Eur J Epidemiol, 2025, 40(2): 151-166.
7.	Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ, 2018, 363: k4245.
8.	Bunouf P, Groc M, Dmitrienko A, et al. Data-driven subgroup identification in confirmatory clinical trials. Ther Innov Regul Sci, 2022, 56(1): 65-75.
9.	Schandelmaier S, Briel M, Varadhan R, et al. Development of the instrument to assess the credibility of effect modification analyses (ICEMAN) in randomized controlled trials and meta-analyses. Can Med Assoc J, 2020, 192(32): E901-E906.
10.	Linck EJG, Goligher EC, Semler MW, et al. Toward precision in critical care research: methods for observational and interventional studies. Crit Care Med, 2024, 52(9): 1439-1450.
11.	Reddy K, Sinha P, O'Kane CM, et al. Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med, 2020, 8(6): 631-643.
12.	Deng C, Wolf JM, Vock DM, et al. Practical guidance on modeling choices for the virtual twins method. J Biopharm Stat, 2023, 33(5): 653-676.
13.	Seibold H, Zeileis A, Hothorn T. Model-based recursive partitioning for subgroup analyses. Int J Biostat, 2016, 12(1): 45-63.
14.	Lipkovich I, Dmitrienko A, Denne J, et al. Subgroup identification based on differential effect search-a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med, 2011, 30(21): 2601-2621.
15.	Berger JO, Wang XJ, Shen L. A Bayesian approach to subgroup identification. J Biopharm Stat, 2014, 24(1): 110-129.
16.	Sinha P, Calfee C S, Delucchi KL. Practitioner's guide to latent class analysis: Methodological considerations and common pitfalls. Crit Care Med, 2021, 49(1): E63-E79.
17.	Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med, 2014, 2(8): 611-620.
18.	Bos LD, Schouten LR, Van vught LA, et al. Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax, 2017, 72(10): 876-883.
19.	Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med, 2018, 6(9): 691-698.
20.	Famous KR, Delucchi K, Ware LB, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am J Respir Crit Care Med, 2017, 195(3): 331-338.
21.	Goligher EC, Lawler PR, Jensen TP, et al. Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19. JAMA, 2023, 329(13): 1066-1077.
22.	Kent DM, van Klaveren D, Paulus JK, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement: explanation and elaboration. Ann Intern Med, 2020, 172(1): W1-W25.
23.	Takahashi K, Serruys PW, Fuster V, et al. Redevelopment and validation of the SYNTAX score Ⅱ to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation. Lancet, 2020, 396(10260): 1399-1412.
24.	Tibshirani R, Bien J, Friedman J, et al. Strong rules for discarding predictors in lasso-type problems. J R Stat Soc Series B Stat Methodol, 2012, 74(2): 245-266.
25.	Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 1970, 12(1): 55-67.
26.	Feuerriegel S, Frauen D, Melnychuk V, et al. Causal machine learning for predicting treatment outcomes. Nat Med, 2024, 30(4): 958-968.
27.	Ling Y, Upadhyaya P, Chen L, et al. Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: methodology review and benchmark. J Biomed Inform, 2023, 137: 104256.
28.	Bica I, Alaa AM, Lambert C, et al. From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges. Clin Pharmacol Ther, 2021, 109(1): 87-100.
29.	周文岳, 易飛, 李冰麗, 等. 因果森林在醫學處理效應異質性評價中的基本原理與應用. 中國循證醫學雜志, 2023, 23(4): 485-491.
30.	Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci U S A, 2016, 113(27): 7353-7360.
31.	Wager S, and Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc, 2018, 113(523): 1228-1242.
32.	Jawadekar N, Kezios K, Odden MC, et al. Practical guide to honest causal forests for identifying heterogeneous treatment effects. Am J Epidemiol, 2023, 192(7): 1155-1165.
33.	Hill J, Linero A, Murray J. Bayesian additive regression trees: a review and look forward. Annu Rev Stat Appl, 2020, 7: 251-278.
34.	Johansson F, Shalit U, Sontag D. Learning representations for counterfactual inference. Proceedings of the International Conference on Machine Learning, 2016.
35.	Künzel SR, Sekhon JS, Bickel PJ, et al. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci U S A, 2019, 116(10): 4156-4165.
36.	Salditt M, Eckes T, Nestler S. A tutorial introduction to heterogeneous treatment effect estimation with meta-learners. Adm Policy Ment Health, 2024, 51(5): 650-673.
37.	Beemer J, Spoon K, He L, et al. Ensemble learning for estimating individualized treatment effects in student success studies. Int J Artif Intell E, 2018, 28: 315-335.
38.	Li Z, Chen J, Laber E, et al. Optimal treatment regimes: a review and empirical comparison. Int Stat Rev, 2023, 91(3): 427-463.
39.	Chakraborty B, Murphy SA. Dynamic treatment regimes. Annu Rev Stat Appl, 2014, 1: 447-464.
40.	Williams NT, Hoffman KL, Díaz I, et al. Learning optimal dynamic treatment regimes from longitudinal data. Am J Epidemiol, 2024, 193(12): 1768-1775.
41.	Shi C, Fan A, Song R, et al. High-dimensional a-learning for optimal dynamic treatment regimes. Ann Stat, 2018, 46(3): 925-957.
42.	Watkins CJCH, Dayan P. Q-learning. Mach Learn, 1992, 8(3-4): 279-292.
43.	Chen J, Fu H, He X, et al. Estimating individualized treatment rules for ordinal treatments. Biometrics, 2018, 74(3): 924-933.
44.	Grolleau F, Petit F, Gaudry S, et al. Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials. J Am Med Inform Assoc, 2024, 31(5): 1074-1083.
45.	孟慶剛. 中醫臨床療效評價循證證據體系構建探析. 中華中醫藥學刊, 2018, 36(5): 1031-1034.
46.	Xu WJ, Zhang NL, Li P, et al. Applying latent tree analysis to classify traditional Chinese medicine syndromes (Zheng) in patients with psoriasis vulgari. J Tradit Chin Med, 2022, 42(1): 132-139.
47.	朱文鋒. 證素辨證學. 北京: 人民衛生出版社, 2008.
48.	滕雪, 劉春麗, 濮家琳, 等. 機器學習在中醫智能辨證中的研究進展. 醫藥高職教育與現代護理, 2025, 8(1): 83-87.
49.	Ge T, Zou R, Bo J, et al. Machine learning prediction of myocardial ischaemia?reperfusion injury: clinical features and qishen yiqi dripping pills mechanism. Phytomedicine, 2026, 150: 157590.
50.	Fu J, Cai X, Huang S, et al. Predicting acupuncture efficacy for major depressive disorder using baseline clinical variables: a machine learning study. J Psychiatr Res, 2023, 168: 64-70.
51.	楊忠奇, 湯慧敏, 唐雅琴, 等. 試論真實世界研究與人用經驗在中藥新藥研發中的應用. 中國中藥雜志, 2021, 46(22): 5987-5991.
52.	鄭琪光. 中醫臨床病歷表型概念譜抽取方法研究. 北京: 北京交通大學, 2020.
53.	李紅巖, 李燦, 郎許鋒, 等. 中醫四診智能化現狀及關鍵技術探討. 中醫雜志, 2022, 63(12): 1101-1108.
54.	Curth A, Peck RW, McKinney E, et al. Using machine learning to individualize treatment effect estimation: challenges and opportunities. Clin Pharmacol Ther, 2024, 115(4): 710-719.

1. Kuhlemeier A, Desai Y, Tonigan A, et al. Applying methods for personalized medicine to the treatment of alcohol use disorder. J Consult Clin Psychol, 2021, 89(4): 288-300.
2. Schaffhausen J. What precisely is precision medicine. Trends Pharmacol Sci, 2017, 38(1): 1-2.
3. 符宇, 邵明義, 燕樹勛, 等. 真實世界研究與中醫臨床療效評價. 中醫雜志, 2019, 60(7): 546-550.
4. 陳健, 陳啟龍, 蘇式兵. 中醫藥精準醫療的思考與探索. 世界科學技術-中醫藥現代化, 2016, 18(4): 557-562.
5. Kent DM, Paulus JK, van Klaveren D, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement. Ann Intern Med, 2020, 172(1): 35-45.
6. Hamaya R, Hara K, Manson JE, et al. Machine-learning approaches to predict individualized treatment effect using a randomized controlled trial. Eur J Epidemiol, 2025, 40(2): 151-166.
7. Kent DM, Steyerberg E, van Klaveren D. Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects. BMJ, 2018, 363: k4245.
8. Bunouf P, Groc M, Dmitrienko A, et al. Data-driven subgroup identification in confirmatory clinical trials. Ther Innov Regul Sci, 2022, 56(1): 65-75.
9. Schandelmaier S, Briel M, Varadhan R, et al. Development of the instrument to assess the credibility of effect modification analyses (ICEMAN) in randomized controlled trials and meta-analyses. Can Med Assoc J, 2020, 192(32): E901-E906.
10. Linck EJG, Goligher EC, Semler MW, et al. Toward precision in critical care research: methods for observational and interventional studies. Crit Care Med, 2024, 52(9): 1439-1450.
11. Reddy K, Sinha P, O'Kane CM, et al. Subphenotypes in critical care: translation into clinical practice. Lancet Respir Med, 2020, 8(6): 631-643.
12. Deng C, Wolf JM, Vock DM, et al. Practical guidance on modeling choices for the virtual twins method. J Biopharm Stat, 2023, 33(5): 653-676.
13. Seibold H, Zeileis A, Hothorn T. Model-based recursive partitioning for subgroup analyses. Int J Biostat, 2016, 12(1): 45-63.
14. Lipkovich I, Dmitrienko A, Denne J, et al. Subgroup identification based on differential effect search-a recursive partitioning method for establishing response to treatment in patient subpopulations. Stat Med, 2011, 30(21): 2601-2621.
15. Berger JO, Wang XJ, Shen L. A Bayesian approach to subgroup identification. J Biopharm Stat, 2014, 24(1): 110-129.
16. Sinha P, Calfee C S, Delucchi KL. Practitioner's guide to latent class analysis: Methodological considerations and common pitfalls. Crit Care Med, 2021, 49(1): E63-E79.
17. Calfee CS, Delucchi K, Parsons PE, et al. Subphenotypes in acute respiratory distress syndrome: latent class analysis of data from two randomised controlled trials. Lancet Respir Med, 2014, 2(8): 611-620.
18. Bos LD, Schouten LR, Van vught LA, et al. Identification and validation of distinct biological phenotypes in patients with acute respiratory distress syndrome by cluster analysis. Thorax, 2017, 72(10): 876-883.
19. Calfee CS, Delucchi KL, Sinha P, et al. Acute respiratory distress syndrome subphenotypes and differential response to simvastatin: secondary analysis of a randomised controlled trial. Lancet Respir Med, 2018, 6(9): 691-698.
20. Famous KR, Delucchi K, Ware LB, et al. Acute respiratory distress syndrome subphenotypes respond differently to randomized fluid management strategy. Am J Respir Crit Care Med, 2017, 195(3): 331-338.
21. Goligher EC, Lawler PR, Jensen TP, et al. Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19. JAMA, 2023, 329(13): 1066-1077.
22. Kent DM, van Klaveren D, Paulus JK, et al. The predictive approaches to treatment effect heterogeneity (PATH) statement: explanation and elaboration. Ann Intern Med, 2020, 172(1): W1-W25.
23. Takahashi K, Serruys PW, Fuster V, et al. Redevelopment and validation of the SYNTAX score Ⅱ to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation. Lancet, 2020, 396(10260): 1399-1412.
24. Tibshirani R, Bien J, Friedman J, et al. Strong rules for discarding predictors in lasso-type problems. J R Stat Soc Series B Stat Methodol, 2012, 74(2): 245-266.
25. Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics, 1970, 12(1): 55-67.
26. Feuerriegel S, Frauen D, Melnychuk V, et al. Causal machine learning for predicting treatment outcomes. Nat Med, 2024, 30(4): 958-968.
27. Ling Y, Upadhyaya P, Chen L, et al. Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: methodology review and benchmark. J Biomed Inform, 2023, 137: 104256.
28. Bica I, Alaa AM, Lambert C, et al. From real-world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges. Clin Pharmacol Ther, 2021, 109(1): 87-100.
29. 周文岳, 易飛, 李冰麗, 等. 因果森林在醫學處理效應異質性評價中的基本原理與應用. 中國循證醫學雜志, 2023, 23(4): 485-491.
30. Athey S, Imbens G. Recursive partitioning for heterogeneous causal effects. Proc Natl Acad Sci U S A, 2016, 113(27): 7353-7360.
31. Wager S, and Athey S. Estimation and inference of heterogeneous treatment effects using random forests. J Am Stat Assoc, 2018, 113(523): 1228-1242.
32. Jawadekar N, Kezios K, Odden MC, et al. Practical guide to honest causal forests for identifying heterogeneous treatment effects. Am J Epidemiol, 2023, 192(7): 1155-1165.
33. Hill J, Linero A, Murray J. Bayesian additive regression trees: a review and look forward. Annu Rev Stat Appl, 2020, 7: 251-278.
34. Johansson F, Shalit U, Sontag D. Learning representations for counterfactual inference. Proceedings of the International Conference on Machine Learning, 2016.
35. Künzel SR, Sekhon JS, Bickel PJ, et al. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci U S A, 2019, 116(10): 4156-4165.
36. Salditt M, Eckes T, Nestler S. A tutorial introduction to heterogeneous treatment effect estimation with meta-learners. Adm Policy Ment Health, 2024, 51(5): 650-673.
37. Beemer J, Spoon K, He L, et al. Ensemble learning for estimating individualized treatment effects in student success studies. Int J Artif Intell E, 2018, 28: 315-335.
38. Li Z, Chen J, Laber E, et al. Optimal treatment regimes: a review and empirical comparison. Int Stat Rev, 2023, 91(3): 427-463.
39. Chakraborty B, Murphy SA. Dynamic treatment regimes. Annu Rev Stat Appl, 2014, 1: 447-464.
40. Williams NT, Hoffman KL, Díaz I, et al. Learning optimal dynamic treatment regimes from longitudinal data. Am J Epidemiol, 2024, 193(12): 1768-1775.
41. Shi C, Fan A, Song R, et al. High-dimensional a-learning for optimal dynamic treatment regimes. Ann Stat, 2018, 46(3): 925-957.
42. Watkins CJCH, Dayan P. Q-learning. Mach Learn, 1992, 8(3-4): 279-292.
43. Chen J, Fu H, He X, et al. Estimating individualized treatment rules for ordinal treatments. Biometrics, 2018, 74(3): 924-933.
44. Grolleau F, Petit F, Gaudry S, et al. Personalizing renal replacement therapy initiation in the intensive care unit: a reinforcement learning-based strategy with external validation on the AKIKI randomized controlled trials. J Am Med Inform Assoc, 2024, 31(5): 1074-1083.
45. 孟慶剛. 中醫臨床療效評價循證證據體系構建探析. 中華中醫藥學刊, 2018, 36(5): 1031-1034.
46. Xu WJ, Zhang NL, Li P, et al. Applying latent tree analysis to classify traditional Chinese medicine syndromes (Zheng) in patients with psoriasis vulgari. J Tradit Chin Med, 2022, 42(1): 132-139.
47. 朱文鋒. 證素辨證學. 北京: 人民衛生出版社, 2008.
48. 滕雪, 劉春麗, 濮家琳, 等. 機器學習在中醫智能辨證中的研究進展. 醫藥高職教育與現代護理, 2025, 8(1): 83-87.
49. Ge T, Zou R, Bo J, et al. Machine learning prediction of myocardial ischaemia?reperfusion injury: clinical features and qishen yiqi dripping pills mechanism. Phytomedicine, 2026, 150: 157590.
50. Fu J, Cai X, Huang S, et al. Predicting acupuncture efficacy for major depressive disorder using baseline clinical variables: a machine learning study. J Psychiatr Res, 2023, 168: 64-70.
51. 楊忠奇, 湯慧敏, 唐雅琴, 等. 試論真實世界研究與人用經驗在中藥新藥研發中的應用. 中國中藥雜志, 2021, 46(22): 5987-5991.
52. 鄭琪光. 中醫臨床病歷表型概念譜抽取方法研究. 北京: 北京交通大學, 2020.
53. 李紅巖, 李燦, 郎許鋒, 等. 中醫四診智能化現狀及關鍵技術探討. 中醫雜志, 2022, 63(12): 1101-1108.
54. Curth A, Peck RW, McKinney E, et al. Using machine learning to individualize treatment effect estimation: challenges and opportunities. Clin Pharmacol Ther, 2024, 115(4): 710-719.

Chinese Journal of Evidence-Based Medicine

Heterogeneity analysis methods for therapeutic effects and their application in traditional Chinese medicine scenarios

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