[논문]고차원 관측자료에서의 Q-학습 모형에 대한 이중강건성 연구

이효빈; 김예지; 조형준; 최상범

doi:10.5351/kjas.2021.34.3.309

고차원 관측자료에서의 Q-학습 모형에 대한 이중강건성 연구
Doubly-robust Q-estimation in observational studies with high-dimensional covariates 원문보기

응용통계연구 = The Korean journal of applied statistics, v.34 no.3, 2021년, pp.309 - 327

이효빈 (고려대학교 통계학과) , 김예지 (고려대학교 통계학과) , 조형준 (고려대학교 통계학과) , 최상범 (고려대학교 통계학과)

초록
AI-Helper

동적 치료 요법(dynamic treatment regimes; DTRs)은 다단계 무작위 시험에서 개인에 맞는 치료를 제공하도록 설계된 의사결정 규칙이다. 모든 개인이 동일한 유형의 치료를 처방받는 고전적인 방법과 달리 DTR은 시간이 지남에 따라 변할 수 있는 개별 특성을 고려한 환자 맞춤형 치료를 제공한다. 최적의 치료 규칙을 파악하기 위한 회귀 기반 알고리즘 중 하나인 Q-학습 방법은 쉽게 구현될 수 있기 때문에 더욱 인기를 끌고 있다. 그러나 Q-학습 알고리즘의 성능은 Q-함수를 제대로 설정했는지의 여부에 크게 의존한다. 본 논문에서는 고차원 데이터가 수집되는 DTRs 문제에 대한 다양한 이중강건 Q-학습 알고리즘을 연구하고 가중 최소제곱 추정 방법을 제안한다. 이중강건성(double-robustness)은 반응변수에 대한 모형 혹은 처리변수에 대한 모형 둘 중 하나만 제대로 설정되어도 불편추정량을 얻을 수 있음을 의미한다. 다양한 모의실험 연구를 통해 제안된 방법이 여러 시나리오 하에서도 잘 작동함을 확인하였으며 실제 데이터 예제를 통해 방법론에 대한 예시를 제시하였다.

Abstract ▼ AI-Helper

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

주제어

표/그림 (7)

표 Table 1: Five ﬁtting cases for blip function estimation on simulation 1
표 Table 2: Bias and MSE of parameter estimators in the blip function
표 Table 3: Simulation results of variable selection for parameter in the blip function
표 Table 4: The percent of selecting the correct treatment rules
표 Table 5: List of variables in the lindner center data
그림 Figure 1: Comparison of kernel density function of propensity score model according to machine learning method.
그림 Figure 2: Comparison of predicted cost for log-transformed costs when the optimal treatment is prescribed using the lasso penalty (above) and the adaptive-lasso penalty (below).

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