Inference of Tamoxifen's Effects on Prevention of Breast Cancer From a Randomized Controlled Trial
随机对照试验中他莫昔芬预防乳腺癌效应的推断AI 解读 ↓
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随机对照试验中他莫昔芬预防乳腺癌效应的推断AI 解读 ↓
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The largest randomized, double-blind, placebo-controlled chemoprevention trial, the National Surgical Adjuvant Breast and Bowel Project's Breast Cancer Prevention Trial (NSABP-BCPT), evaluated the efficacy of tamoxifen in the prevention of breast cancer among women at high risk of developing the disease. The trial has reported a reduction of breast cancer incidence for the tamoxifen group. However, the effect of tamoxifen on the time to diagnosis of the disease over the six-year follow-up of the trial has not been fully explored in literature. We propose a flexible semiparametric model to assess the effects of tamoxifen on the incidence of breast cancer as well as time to the diagnosis of the disease, separately, in the framework of a cure-rate model. We used an estimating equation approach to estimating the unknown parameters, and assessed the semiparametric model assumption with a test based on the area between two survival curves. On the NSABP-BCPT data, we found that the treatment of tamoxifen has a substantial effect in the reduction of invasive breast cancer events in estrogen receptor (ER)-positive tumors, but has no effect on ER-negative tumors. Among women who were diagnosed to have ER-positive breast cancer during the study follow-up, there was little difference in terms of time to diagnosis between the two arms. However, tamoxifen may advance the time to breast cancer diagnosis for ER-negative breast cancer, while the incidence of ER-negative tumors is similar in the two treatment arms.
We develop a semiparametric continuous time inferential framework for testing cumulative treatment effects, including both immediate and carryover effects. The latent state process is modeled by an ordinary differential equation (ODE) whose drift depends on state and treatment, while the observed data are noisy measurements of this latent state at irregular, multi resolution time points. We define the average treatment effect (ATE) as the difference of expected cumulative…
嵌套工具变量分析:转换者平均处理效应及可推广性
Instrumental variables (IVs) are widely used to estimate causal effects from non-randomized data. A canonical example is a randomized trial with noncompliance, in which the randomized treatment assignment serves as an IV for the non-ignorable treatment received. Under a monotonicity assumption, a valid IV nonparametrically identifies the average treatment effect among a latent complier subgroup, whose generalizability is often under debate. In many studies, there exist…
We study nonparametric inference for a continuous-treatment causal dose-response curve and its first derivative under unmeasured confounding, using treatment- and outcome-inducing proxies. We establish a proximal doubly robust pseudo-outcome whose conditional mean given treatment equals the dose-response curve whenever either bridge function is correctly specified. Their (smoothed) efficient influence function representations lead to cross-fitted local-polynomial estimators…
前门模型下因果效应的灵活非参数推断
Abstract Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed covariates, it fails in the presence of unmeasured confounding. The front-door criterion offers an alternative by leveraging variables that fully mediate the treatment effect and are unaffected by unmeasured confounders of the treatment-outcome pair. We develop novel one-step and targeted…
使用 AI 学习表征的实用双重机器学习
Text, images, and other rich covariates are increasingly compressed into AI-learned representations and then used as controls in causal analysis. We study when this approach is valid and develop a practical framework for causal inference with learned representations. For a broad class of estimands, an imperfect representation distorts the target causal parameter by the product of two representation errors: one in the outcome regression and one in the balancing weight (or…
The bootstrap is widely used to construct confidence intervals but may perform poorly for some estimators. We consider semiparametric estimators that use cross-validation to estimate nuisance pa- rameters and can be computationally expensive in large datasets. We illustrate potential problems with bootstrapping with replacement when cross-validation is used and extend the cheap bootstrap confidence interval to subsampling without replacement for asymptotically linear…