半競爭風險下因果中介效應分析基於模型的假設檢定

Model-Based Hypothesis Tests for the Causal Mediation of Semi-Competing Risks

何昀霖(Yun-Lin Ho)

指導教授 ： 黃彥棕

國立臺灣大學/理學院/應用數學科學研究所/碩士(2022年)

https://doi.org/10.6342/NTU202200127

摘要

在因果中介模型中，由於中介事件（例如：肝硬化）可能會被主事件（例如：肝癌）設限（censored），所以半競爭風險之因果中介效應分析在藥學研究裡已經成為一個重要的議題。在因果中介效應分析中將暴露到主事件的效應拆解成間接與直接效應，其中分別表示暴露到主事件之效應有無通過中介因子。這此我們根據所提模型建立檢定程序用於檢查是否有中介效應（間接效應）的存在。在反事實結果（counterfactual outcome）架構下，我們用計次過程來定義因果中介效應，並提出交集-聯集檢定（IUT檢定）來評估是否有足夠的統計證據證明中介效應的存在，我們先使用一個Cox風險等比模型和一系列的邏輯式迴歸模型來建構中介效應，再利用模型中的半母數參數估計來建構IUT檢定的統計檢定量。為了和我們提出的IUT檢定做比較，我們將IUT檢定和加權對數秩檢定（WLR檢定）做連結，之後再推導出兩個統計檢定之統計檢定量的大樣本性質，最後證明IUT檢定的檢定規模為 alpha，而且比WLR檢定有更高的檢定力。在數值模擬中，我們證明即使有干擾機制的存在，依據模型所提出的IUT檢定和WLR檢定也可以加入干擾因子做校正，而且依然能夠守住型一誤差的比率。分析結果根據我們的方法指出：B肝病毒和C肝病毒皆會透過肝硬化影響得到肝癌的風險。

關鍵字

因果中介模型 ； Cox風險等比模型 ； 無母數最大概似估計 ； 半競爭風險模型 ； 交集-聯集檢定 ； 加權對數秩檢定

並列摘要

Analyzing the causal mediation of semi-competing risks has become important in medical research. Semi-competing risks refers to a scenario wherein an intermediate event may be censored by a primary event but not vice versa. Causal mediation analyses decompose the effect of an exposure on the primary outcome into an indirect (mediation) effect: an effect mediated through a mediator, and a direct effect: an effect not through the mediator. Here we proposed a model-based testing procedure to examine the indirect effect of the exposure on the primary event through the intermediate event. Under the counterfactual outcome framework, we defined a causal mediation effect using counting process. To assess statistical evidence for the mediation effect, we performed an intersection--union test (IUT). The test statistic was developed from a semi-parametric estimator of the mediation effect using a Cox proportional hazards model and a series of logistic regression models. To conduct a comparison with the proposed IUT, we built a connection with a weighted log-rank test (WLR). Asymptotic properties of the two tests were derived, and the IUT was determined to be a size alpha test and statistically more powerful than the WLR. In numerical simulations, both the model-based IUT and WLR can properly adjust for confounding covariates, and the Type I error rates of the proposed methods are well protected, with the IUT being more powerful than the WLR. Our methods demonstrate the strongly significant effects of hepatitis B or C on the risk of liver cancer mediated through liver cirrhosis incidence in a prospective cohort study.

並列關鍵字

Causal mediation model ； Cox proportional hazards model ； Nonparametric maximum likelihood estimator ； Semi-competing risks ； Intersection-union test ； Weighted log-rank test

參考文獻

References

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