Application of longitudinal matching in causal inference for rolling enrollment interventions_Chinese Journal of Evidence-Based Medicine

Authors：

YANG Rongzhen ,  GUO Siwen

Department of Psychology, Renmin University of China, Beijing 100872, P. R. China;

Corresponding author：

GUO Siwen, Email: guosiwen@ruc.edu.cn

Keywords：

Matching; Comparison groups; Casual inference; Treatment effect

DOI：

10.7507/1672-2531.202310083

Video：

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Rolling enrollment is a common method for participant recruitment in medical practice. In the longitudinal data, where researchers are often interested in outcomes occurring after a certain period of treatment, the definition of causal effects differs from which in the cross-sectional data. It poses new challenges for the application of matching methods in the longitudinal studies. Longitudinal matching is an extension of matching methods in longitudinal studies involving static interventions such as rolling enrollment. Currently, longitudinal matching methods are widely applied in the comparative effectiveness research. This article elucidates the fundamental principles, applicable conditions, code implementation, and application instances of four longitudinal matching methods through theoretical discussions and empirical illustrations. It provides methodological references for estimating causal effects in longitudinal data analysis.

1.	刘颖欣, 王若婷, 黎国威. 匹配调整间接比较在医学研究中的应用. 中国循证医学杂志, 2022, 22(10): 1201-1205.
2.	Schneeweiss S. A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf, 2010, 19(8): 858-868.
3.	Schneeweiss S, Gagne JJ, Glynn RJ, et al. Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther, 2011, 90(6): 777-790.
4.	Seeger JD, Williams PL, Walker AM. An application of propensity score matching using claims data. Pharmacoepidemiol Drug Saf, 2005, 14(7): 465-476.
5.	Pimentel SD, Forrow LV, Gellar J, et al. Optimal matching approaches in health policy evaluations under rolling enrolment. J R Stat Soc Ser A Stat Soc, 2020, 183(4): 1411-1435.
6.	Abuadas FH, Petro-Nustas WJ, Abuadas MH. The effect of a health education intervention on jordanian participants' colorectal cancer knowledge, health perceptions, and screening practices. Cancer Nurs, 2018, 41(3): 226-237.
7.	Witman A, Beadles C, Liu Y, et al. Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching. Health Serv Res, 2019, 54(2): 492-501.
8.	Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC. , 2020.
9.	Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol, 1974, 66(5): 688-701.
10.	郑全庆. 临床流行病学. 西安: 西安交通大学出版社, 2007.
11.	Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci, 2010, 25(1): 1-21.
12.	郭申阳, Fraser MW. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
13.	Lenis D, Ackerman B, Stuart EA. Measuring model misspecification: application to propensity score methods with complex survey data. Comput Stat Data Anal, 2018, 128: 48-57.
14.	Imai K, Kim IS, Wang EH. Matching methods for causal inference with time-series cross-sectional data. Am J Pol Sci, 2023, 67(3): 587-605.
15.	范大超. 临床试验过程的分类及分期. 中国处方药, 2009, 89(8): 48-49.
16.	邱嘉平. 因果推断实用计量方法. 上海: 上海财经大学出版社, 2020.
17.	Rosenbaum PR, Rubin DB. Propensity scores in the design of observational studies for causal effects. Biometrika, 2023, 110(1): 1-13.
18.	Lewis TC, Adhikari S, Tatapudi V, et al. A propensity-matched cohort study of tocilizumab in patients with coronavirus disease 2019. Crit Care Explor, 2020, 2(11): e283.
19.	Al Kaabi N, Oulhaj A, Ganesan S, et al. Effectiveness of BBIBP-CorV vaccine against severe outcomes of COVID-19 in Abu Dhabi, United Arab Emirates. Nat Commun, 2022, 13(1): 3215.
20.	Li YP, Propert KJ, Rosenbaum PR. Balanced risk set matching. J Am Stat Assoc, 2001, 96(455): 870-882.
21.	Goodman-Bacon A. Difference-in-differences with variation in treatment timing. J Econom, 2021, 225(2): 254-277.
22.	Hurh K, Jeong SH, Kim SH, et al. Increased risk of all-cause, Alzheimer’s, and vascular dementia in adults with migraine in Korea: a population-based cohort study. J Headache Pain, 2022, 23(1): 108.
23.	Lu B. Propensity score matching with time-dependent covariates. Biometrics, 2005, 61(3): 721-728.
24.	Hansen BB. Full matching in an observational study of coaching for the SAT. J Am Stat Assoc, 2004, 99(467): 609-618.
25.	Abadie A, Imbens GW. On the failure of the bootstrap for matching estimators. Econometrica, 2008, 76(6): 1537-1557.
26.	Abadie A, Spiess J. Robust post-matching inference. J Am Stat Assoc, 2022, 117(538): 983-995.
27.	Otsu T, Rai Y. Bootstrap inference of matching estimators for average treatment effects. J Am Stat Assoc, 2017, 112(520): 1720-1732.
28.	Glazer AK, Pimentel SD. Robust inference for matching under rolling enrollment. J Causal Inference, 2023, 11(1): 20220055.
29.	Zurovac J, Barna M, Merrill A, et al. Transforming Clinical Practice Initiative Boosted Participation in Medicare Alternative Payment Models. J Ambul Care Manage, 2022, 45(3): 150-160.
30.	Rosenbaum PR. Observational studies. New York: Springer, 2002.
31.	Sun Y. New matching algorithm: Outlier first matching (OFM) and its performance on propensity score analysis (PSA) under new stepwise matching framework (SMF). New York: State University of New York at Albany, 2014.
32.	Pavia L, Di Blasi M, Cinquegrana A, et al. The influence of retention, turnover, and alliance on process and outcomes in rolling group psychotherapy for cocaine disorder. Int J Group Psychother, 2016, 66(4): 526-550.
33.	Watkins KE, Hunter SB, Hepner KA, et al. An effectiveness trial of group cognitive behavioral therapy for patients with persistent depressive symptoms in substance abuse treatment. Arch Gen Psychiatry, 2011, 68(6): 577-584.
34.	Meneguzzo P, Tenconi E, Todisco P, et al. Cognitive remediation therapy for anorexia nervosa as a rolling group intervention: data from a longitudinal study in an eating disorders specialized inpatient unit. Eur Eat Disord Rev, 2021, 29(5): 770-782.
35.	Keane MH. The impact of online art therapy groups for pediatric rheumatology patients. Rheumatology, 2022, 61(Sup 1): c133-c168.
36.	Rummans TA, Clark MM, Sloan JA, et al. Impacting quality of life for patients with advanced cancer with a structured multidisciplinary intervention: a randomized controlled trial. J Clin Oncol, 2006, 24(4): 635-642.
37.	Paddock SM, Hunter SB, Watkins KE. Analysis of rolling group therapy data using conditionally autoregressive priors. Ann Appl Stat, 2011, 5(2A): 605-627.
38.	Paddock SM, Leininger TJ, Hunter SB. Bayesian restricted spatial regression for examining session features and patient outcomes in open-enrollment group therapy studies. Stat Med, 2016, 35(1): 97-114.
39.	Burgette LF, Paddock SM. Bayesian models for semicontinuous outcomes in rolling admission therapy groups. Psychol Methods, 2017, 22(4): 725-742.
40.	Han B, Paddock SM, Burgette L. Causal inference under interference with prognostic scores for dynamic group therapy studies. Int J Biostat, 2021, 18(2): 439-453.

1. 刘颖欣, 王若婷, 黎国威. 匹配调整间接比较在医学研究中的应用. 中国循证医学杂志, 2022, 22(10): 1201-1205.
2. Schneeweiss S. A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf, 2010, 19(8): 858-868.
3. Schneeweiss S, Gagne JJ, Glynn RJ, et al. Assessing the comparative effectiveness of newly marketed medications: methodological challenges and implications for drug development. Clin Pharmacol Ther, 2011, 90(6): 777-790.
4. Seeger JD, Williams PL, Walker AM. An application of propensity score matching using claims data. Pharmacoepidemiol Drug Saf, 2005, 14(7): 465-476.
5. Pimentel SD, Forrow LV, Gellar J, et al. Optimal matching approaches in health policy evaluations under rolling enrolment. J R Stat Soc Ser A Stat Soc, 2020, 183(4): 1411-1435.
6. Abuadas FH, Petro-Nustas WJ, Abuadas MH. The effect of a health education intervention on jordanian participants' colorectal cancer knowledge, health perceptions, and screening practices. Cancer Nurs, 2018, 41(3): 226-237.
7. Witman A, Beadles C, Liu Y, et al. Comparison group selection in the presence of rolling entry for health services research: Rolling entry matching. Health Serv Res, 2019, 54(2): 492-501.
8. Hernán MA, Robins JM. Causal inference: what if. Boca Raton: Chapman & Hall/CRC. , 2020.
9. Rubin DB. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol, 1974, 66(5): 688-701.
10. 郑全庆. 临床流行病学. 西安: 西安交通大学出版社, 2007.
11. Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci, 2010, 25(1): 1-21.
12. 郭申阳, Fraser MW. 倾向值分析: 统计方法与应用. 重庆: 重庆大学出版社, 2012.
13. Lenis D, Ackerman B, Stuart EA. Measuring model misspecification: application to propensity score methods with complex survey data. Comput Stat Data Anal, 2018, 128: 48-57.
14. Imai K, Kim IS, Wang EH. Matching methods for causal inference with time-series cross-sectional data. Am J Pol Sci, 2023, 67(3): 587-605.
15. 范大超. 临床试验过程的分类及分期. 中国处方药, 2009, 89(8): 48-49.
16. 邱嘉平. 因果推断实用计量方法. 上海: 上海财经大学出版社, 2020.
17. Rosenbaum PR, Rubin DB. Propensity scores in the design of observational studies for causal effects. Biometrika, 2023, 110(1): 1-13.
18. Lewis TC, Adhikari S, Tatapudi V, et al. A propensity-matched cohort study of tocilizumab in patients with coronavirus disease 2019. Crit Care Explor, 2020, 2(11): e283.
19. Al Kaabi N, Oulhaj A, Ganesan S, et al. Effectiveness of BBIBP-CorV vaccine against severe outcomes of COVID-19 in Abu Dhabi, United Arab Emirates. Nat Commun, 2022, 13(1): 3215.
20. Li YP, Propert KJ, Rosenbaum PR. Balanced risk set matching. J Am Stat Assoc, 2001, 96(455): 870-882.
21. Goodman-Bacon A. Difference-in-differences with variation in treatment timing. J Econom, 2021, 225(2): 254-277.
22. Hurh K, Jeong SH, Kim SH, et al. Increased risk of all-cause, Alzheimer’s, and vascular dementia in adults with migraine in Korea: a population-based cohort study. J Headache Pain, 2022, 23(1): 108.
23. Lu B. Propensity score matching with time-dependent covariates. Biometrics, 2005, 61(3): 721-728.
24. Hansen BB. Full matching in an observational study of coaching for the SAT. J Am Stat Assoc, 2004, 99(467): 609-618.
25. Abadie A, Imbens GW. On the failure of the bootstrap for matching estimators. Econometrica, 2008, 76(6): 1537-1557.
26. Abadie A, Spiess J. Robust post-matching inference. J Am Stat Assoc, 2022, 117(538): 983-995.
27. Otsu T, Rai Y. Bootstrap inference of matching estimators for average treatment effects. J Am Stat Assoc, 2017, 112(520): 1720-1732.
28. Glazer AK, Pimentel SD. Robust inference for matching under rolling enrollment. J Causal Inference, 2023, 11(1): 20220055.
29. Zurovac J, Barna M, Merrill A, et al. Transforming Clinical Practice Initiative Boosted Participation in Medicare Alternative Payment Models. J Ambul Care Manage, 2022, 45(3): 150-160.
30. Rosenbaum PR. Observational studies. New York: Springer, 2002.
31. Sun Y. New matching algorithm: Outlier first matching (OFM) and its performance on propensity score analysis (PSA) under new stepwise matching framework (SMF). New York: State University of New York at Albany, 2014.
32. Pavia L, Di Blasi M, Cinquegrana A, et al. The influence of retention, turnover, and alliance on process and outcomes in rolling group psychotherapy for cocaine disorder. Int J Group Psychother, 2016, 66(4): 526-550.
33. Watkins KE, Hunter SB, Hepner KA, et al. An effectiveness trial of group cognitive behavioral therapy for patients with persistent depressive symptoms in substance abuse treatment. Arch Gen Psychiatry, 2011, 68(6): 577-584.
34. Meneguzzo P, Tenconi E, Todisco P, et al. Cognitive remediation therapy for anorexia nervosa as a rolling group intervention: data from a longitudinal study in an eating disorders specialized inpatient unit. Eur Eat Disord Rev, 2021, 29(5): 770-782.
35. Keane MH. The impact of online art therapy groups for pediatric rheumatology patients. Rheumatology, 2022, 61(Sup 1): c133-c168.
36. Rummans TA, Clark MM, Sloan JA, et al. Impacting quality of life for patients with advanced cancer with a structured multidisciplinary intervention: a randomized controlled trial. J Clin Oncol, 2006, 24(4): 635-642.
37. Paddock SM, Hunter SB, Watkins KE. Analysis of rolling group therapy data using conditionally autoregressive priors. Ann Appl Stat, 2011, 5(2A): 605-627.
38. Paddock SM, Leininger TJ, Hunter SB. Bayesian restricted spatial regression for examining session features and patient outcomes in open-enrollment group therapy studies. Stat Med, 2016, 35(1): 97-114.
39. Burgette LF, Paddock SM. Bayesian models for semicontinuous outcomes in rolling admission therapy groups. Psychol Methods, 2017, 22(4): 725-742.
40. Han B, Paddock SM, Burgette L. Causal inference under interference with prognostic scores for dynamic group therapy studies. Int J Biostat, 2021, 18(2): 439-453.

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