Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching

LU Zhenzhen; ZHAO Enhui; HUANG Lihong

doi:10.16462/j.cnki.zhjbkz.2024.02.018

Volume 28 Issue 2

Feb. 2024

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2024 > 28(2): 241-248

LU Zhenzhen, ZHAO Enhui, HUANG Lihong. Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018

Citation:

LU Zhenzhen, ZHAO Enhui, HUANG Lihong. Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2024, 28(2): 241-248. doi: 10.16462/j.cnki.zhjbkz.2024.02.018

Citation:

PDF( 10999 KB)

Evaluation of the control effect for measured confounders between propensity score matching and disease risk score matching

doi: 10.16462/j.cnki.zhjbkz.2024.02.018

LU Zhenzhen^{1, 2},
ZHAO Enhui³,
HUANG Lihong^{1, 2
,
,}

1.
Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China
2.
School of Public Health, Fudan University, Shanghai 200032, China
3.
Department of Epidemiology and Health Statistics, Clinical Research Unit, Institute of Clinical Science, Zhongshan Hospital of Fudan University, Shanghai 200032, China

LU Zhenzhen and ZHAO Enhui contributed equally to this article

Funds:

National Natural Science Foundation of China 82273733

More Information

Corresponding author: HUANG Lihong, E-mail: huang.lihong@zs-hospital.sh.cn
Received Date: 2023-03-22
Rev Recd Date: 2023-07-04

Available Online: 2024-03-30

Publish Date: 2024-02-10

Abstract

Abstract

Objective To compare propensity score (PS) matching and disease risk score (DRS) matching in the scenarios of good PS overlap and poor PS overlap, and to investigate the optimal caliper width for DRS matching. Methods According to the different proportions of the test group and events as well as the different PS overlap situations, 6 scenarios were simulated to compare the balance of covariables and the bias before and after PS matching or DRS matching, followed by analysis of an actual case. Results In the scenarios with good PS overlap, the DRS overlap was also good, and PS matching was more accurate than DRS matching. The optimal caliper width was found to be 10%-20% of the PS standard deviation (SD) for PS matching, and the relative optimal caliper width was 0.5% of the DRS SD for DRS matching. In the scenarios with poor PS overlap, the DRS overlap was also poor, but the DRS matching was more accurate than PS matching. The optimal caliper width was found to be 15%-20% of the DRS SD. In addition, the improvement on the balance of covariates was consistent with the estimation bias of treatment effect. Conclusions When the overlap of PS is good, PS matching is preferred; when the overlap of PS is poor, DRS matching can be selected, and the optimal caliper width is 15%-20% of the DRS SD. In practical application, the control effect for measured a confounder can be evaluated according to the improvement on the covariable balance between groups.
- Propensity score,
- Disease risk score,
- Matching,
- Confounding bias

FullText(HTML)

References(16)

References

[1]	Paul, R, Rosenbaum, et al. 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.
[2]	Miettinen OS. Stratification by a multivariate confounder score [J]. Am J Epidemiol, 1976, 104(6): 609-620. DOI: 10.1080/0002889768507553.
[3]	Arbogast PG, Ray WA. Performance of disease risk scores, propensity scores, and traditional multivariable outcome regression in the presence of multiple confounders [J]. Am J Epidemiol, 2011, 174(5): 613-620. DOI: 10.1093/aje/kwr143.
[4]	Wang Y, Cai H, Li C, et al. Optimal caliper width for propensity score matching of three treatment groups: a Monte Carlo study [J]. PLoS One, 2013, 8(12): e81045. DOI: 10.1371/journal.pone.0081045.
[5]	Elze MC, Gregson J, Baber U, et al. Comparison of propensity score methods and covariate adjustment [J]. J Am Coll Cardiol, 2017, 69(3): 345-347. DOI: 10.1016/j.jacc.2016.10.060.
[6]	Benedetto U, Head SJ, Angelini GD, et al. Statistical primer: propensity score matching and its alternatives [J]. Eur J Cardiothorac Surg, 2018, 53(6): 1112-1117. DOI: 10.1093/ejcts/ezy167.
[7]	Austin PC. Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies [J]. Pharm Stat, 2011, 10(2): 150-161. DOI: 10.1002/pst.433.
[8]	黄丽红, 王永吉, 王素珍, 等. 倾向性评分方法及其规范化应用的统计学共识CSCO生物统计学专家委员会RWS方法学组[J]. 中国卫生统计, 2020, 37(6): 952-958. DOI: 10.3969/j.issn.1002-3674.2020.06.041. Huang LH, Wang YJ, Wang SZ, et al. Statistical consensus on propensity score method and its standardized application [J]. Chinese Journal of Health Statistics, 2020, 37(6): 952-958. DOI: 10.3969/j.issn.1002-3674.2020.06.041.
[9]	Connolly JG, Gagne JJ. Comparison of calipers for matching on the disease risk score [J]. Am J Epidemiol, 2016, 183(10): 937-948. DOI: 10.1093/aje/kwv302.
[10]	黄丽红, 赵杨, 魏永越, 等. 如何控制观察性疗效比较研究中的混杂因素: (一)已测量混杂因素的统计学分析方法[J]. 中华流行病学杂志, 2019, 40(12): 1645-1649. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024. Huang LH, Zhao Y, Wei YY, et al. Confounder adjustment in observational comparative effectiveness researches: (1) statistical adjustment approaches for measured confounder [J]. Chin J Epidemiol, 2019, 40(12): 1645-1649. DOI: 10.3760/cma.j.issn.0254-6450.2019.10.024.
[11]	Zhang D, Kim J. Use of propensity score and disease risk score for multiple treatments with time-to-event outcome: a simulation study [J]. J Biopharm Stat, 2019, 29(6): 1103-1115. DOI: 10.1080/10543406.2019.1584205.
[12]	Wyss R, Ellis AR, Brookhart MA, et al. Matching on the disease risk score in comparative effectiveness research of new treatments [J]. Pharmacoepidemiol Drug Saf, 2015, 24(9): 951-961. DOI: 10.1002/pds.3810.
[13]	Desai RJ, Glynn RJ, Wang S, et al. Performance of disease risk score matching in nested case-control studies: a simulation study [J]. Am J Epidemiol, 2016, 183(10): 949-957. DOI: 10.1093/aje/kwv269.
[14]	Li Y, Li L. Propensity score analysis methods with balancing constraints: a monte c arlo study [J]. Stat Methods Med Res, 2021, 30(4): 1119-1142. DOI: 10.1177/0962280220983512.
[15]	黄丽红, 陈峰. 倾向性评分方法及其应用[J]. 中华预防医学杂志, 2019, 53(7): 752-756. DOI: 10.3760/cma.j.issn.0253-9624.2019.07.017. Huang LH, Chen F. The propensity score method and its application [J]. Chin J Prev Med, 2019, 53(7): 752-756. DOI: 10.3760/cma.j.issn.0253-9624.2019.07.017.
[16]	Reiffel JA. Propensity-score matching: the "devil is in the details" where more may be hidden than you know [J]. Am J Med, 2020, 133(2): 178-181. DOI: 10.1016/j.amjmed.2019.08.055.