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생존자료분석에서 성향 점수를 이용한 treatment delay effect 추정법에 대한 연구
Propensity score methods for estimating treatment delay effects
Korean J Appl Stat 2023;36(5):415-445
Published online October 31, 2023
© 2023 The Korean Statistical Society.

정주이a, 송현진a, 한승봉1,a
Jooyi Junga, Hyunjin Songa, Seungbong Han1,a

a고려대학교 의학통계학협동과정

aDepartment of Biostatistics, Korea University College of Medicine, Korea University
1Department of Biostatistics, Korea University College of Medicine, Korea University, 73 Goryeodae-ro, Seongbuk-gu, Seoul 02841, Korea. E-mail:
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea govern-ment (MSIT) [No. 2022R1F1A1063027] and the Korea Health Technology R&D Project through the Korea Health Indus-try Development Institute (KHIDI) funded by the Ministry of Health & Welfare, Republic of Korea [No. HI22C045400].
Received April 4, 2023; Revised May 1, 2023; Accepted May 8, 2023.
생존 자료에서 Hade 등 (2020) 은 시간-의존 교란 변수가 환자의 처치 시점에 영향을 미칠 때, 해당 효과를 보정하여 treatment delay effect를 올바르게 추정하기 위해 성향 점수 매칭 방법을 이용하였다. 이 때, treatment delay effect란 환자가 관심 있는 지연 시점만큼 늦게 처치를 받는 경우 제 때 받는 경우에 비해 사건 발생 위험에 미치는 영향을 의미한다. 본 연구에서는 또 다른 성향 점수 기반 모형인 Cox-MSM 모형 또한 해당 효과를 올바르게 추정할 수 있는지 모의 실험을 통해 확인 및 기존 매칭 모형과 비교하였다. 모의실험 결과, 세 가지 모형 모두 다양한 시나리오 내에서 treatment delay effect를 올바르게 추정함을 확인하였다. 특히 모든 시나리오 내에서 Cox-MSM의 제곱근평균제곱오차의 값이 가장 낮았으며, restricted Cox matching 모형에서 가장 큰 값을 가지는 것으로 나타났다. 결론적으로, 성향 점수에 기반하나 매칭이 아닌 방법 또한 treatment delay effect 적용이 가능하다는 결과를 제공한다. 추후 G-formula과 같이 성향 점수 기반이 아닌 모형에서도 적용이 가능한지에 대한 상세 연구가 필요하다고 사료된다.
Oftentimes, the time dependent treatment covariate and the time dependent confounders exist in observation studies. It is an important problem to correctly adjust for the time dependent confounders in the propensity score analysis. Recently, In the survival data, Hade et al. (2020) used a propensity score matching method to correctly estimate the treatment delay effect when the time dependent confounder affects time to the treatment time, where the treatment delay effects is defined to the delay in treatment reception. In this paper, we proposed the Cox model based marginal structural model (Cox-MSM) framework to estimate the treatment delay effect and conducted extensive simulation studies to compare our proposed Cox-MSM with the propensity score matching method proposed by Hade et al. (2020). Our simulation results showed that the Cox-MSM leads to more exact estimate for the treatment delay effect compared with two sequential matching schemes based on propensity scores. Example from study in treatment discontinuation in conjunction with simulated data illustrates the practical advantages of the proposed Cox-MSM.
주요어 : 생존 분석, 시간-의존 교란 변수, 성향 점수 매칭, treatment delay effect, Cox-MSM
Keywords : Cox marginal structural model, propensity score matching, survival analysis, time-dependent confounder, treatment delay effect
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