Scholarlink

<p>While mortality is often the main focus of cancer studies, non-fatal events, such as disease progression, can vitally impact patient outcomes. For example, recurrence after curative treatment is a crucial endpoint in lung cancer, affecting available second-line treatments and personalized care. Estimating the de-confounded effect of interventions on disease recurrence is a key aspect of assessing cancer treatments. However, semi-competing risks complicate causal inference when death precludes disease recurrence. Existing approaches for estimating causal quantities in semi-competing survival functions rely on strong assumptions and complex objective functions, which are difficult to estimate accurately. To address these challenges, we propose a deep learning approach for estimating the causal effect of treatment on non-fatal outcomes in the presence of dependent censoring and complex covariate relationships. Our three-stage approach involves (1) estimating the marginal survival function and (2) calculating pseudo-survival probabilities at fixed time points. These pseudo-survival probabilities serve as target values for developing causal estimators that are consistent and do not rely on common assumptions like proportional hazards. In the final stage, we (3) employ a deep neural network to link pseudo-outcomes, the causal variable, and additional confounders. This enables us to estimate survival average causal effects through outcome modeling and direct standardization. We evaluate our approach in simulation and apply it to the Boston Lung Cancer Study, where we examine the effect of surgical tumor resection in patients with early-stage non-small cell lung cancer.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A pseudo-value approach to causal deep learning of semi-competing risks

  • 期刊论文

  • 发表日期：

    2025年3月3日

查看全文
  • Stephen Salerno,
  • Yi Li

摘要

While mortality is often the main focus of cancer studies, non-fatal events, such as disease progression, can vitally impact patient outcomes. For example, recurrence after curative treatment is a crucial endpoint in lung cancer, affecting available second-line treatments and personalized care. Estimating the de-confounded effect of interventions on disease recurrence is a key aspect of assessing cancer treatments. However, semi-competing risks complicate causal inference when death precludes disease recurrence. Existing approaches for estimating causal quantities in semi-competing survival functions rely on strong assumptions and complex objective functions, which are difficult to estimate accurately. To address these challenges, we propose a deep learning approach for estimating the causal effect of treatment on non-fatal outcomes in the presence of dependent censoring and complex covariate relationships. Our three-stage approach involves (1) estimating the marginal survival function and (2) calculating pseudo-survival probabilities at fixed time points. These pseudo-survival probabilities serve as target values for developing causal estimators that are consistent and do not rely on common assumptions like proportional hazards. In the final stage, we (3) employ a deep neural network to link pseudo-outcomes, the causal variable, and additional confounders. This enables us to estimate survival average causal effects through outcome modeling and direct standardization. We evaluate our approach in simulation and apply it to the Boston Lung Cancer Study, where we examine the effect of surgical tumor resection in patients with early-stage non-small cell lung cancer.