Estimates and Confidence Intervals for Median and Mean Life in the Proportional Hazard Model
比例风险模型中中位与平均生存时间的估计及置信区间AI 解读 ↓
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比例风险模型中中位与平均生存时间的估计及置信区间AI 解读 ↓
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Estimation procedures and confidence intervals are given for the median and mean survival time in the proportional hazard regression model. For median survival, the methods apply to censored data. The procedures are based on Cox's partial likelihood estimates of the linear model parameters in the log linear proportional hazard model and on Breslow's estimate of the baseline hazard function. The asymptotic properties of these semiparametric estimates are developed and they are compared with the optimal parametric estimates for the Weibull regression model. For the parameter values considered, the more generally valid semiparametric estimate of mean survival loses little efficiency relative to the optimal parametric estimates in this model unless the Weibull shape parameter is close to zero. The efficiency loss for the semiparametric estimate of median survival is greater but not severe unless censoring is heavy. We also compare the optimal parametric estimates of mean and median survival with the Weibull shape parameter known and unknown and find that the efficiency loss for the unknown shape estimate is small.
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…
针对协变量完全缺失的增广迁移回归学习
Abstract Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing-data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this…
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…