Analysis of a Randomized Trial to Prevent Vertical Transmission of HIV-1
预防 HIV-1 母婴传播随机试验的分析AI 解读 ↓
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预防 HIV-1 母婴传播随机试验的分析AI 解读 ↓
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We analyze data from a randomized trial of breastfeeding versus formula feeding in HIV-1-infected mothers and their infants. Estimates of infant HIV-1-free survival and the timing of vertical transmission of HIV-1 are complicated by the fact that the infants' HIV-1 status at death may be unknown, the time of HIV-1 infection may be interval censored, and the tests used to detect HIV-1 infection in the infants may be less than 100% sensitive or specific. To address these problems we develop nonparametric and semiparametric approaches to estimating the joint distribution of HIV-1 infection time and death time. The nonparametric estimator is based on a self-consistency algorithm and, in the current application, provides a unique estimate of the HIV-1-free survival at 24 months old. However, the estimate of the cumulative probability of HIV-1 infection over time is nonunique. To resolve the nonuniqueness of the nonparametric estimator a semiparametric approach is proposed in which the joint distribution of HIV-1 infection time and death time is decomposed into the marginal distribution of death time, f(t), and the conditional distribution of HIV-1 infection time given the death time, g(s\t). f(t) is estimated nonparametrically while a Weibull distribution is used to model g(s\t). The parameters of the Weibull distribution are allowed to vary slowly in t using splines. The two approaches are shown to be complimentary. Using these methods we find that the probability of HIV-1 infection is significantly lower and the 24-month HIV-1–free survival is significantly higher in the formula-feeding group.
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通过 Riesz 校准最优传输将随机试验效应迁移至现实人群
Randomized trials support causal inference, but differences between trial and target populations can limit the transportability of treatment effects to real-world settings. Many existing approaches model the propensity of trial participation and can therefore be sensitive to model misspecification and weak overlap of the covariate distributions. Optimal Transport (OT) offers a different route by comparing the trial and target populations directly in covariate space. We…
条件矩限制的局部稳健核设定检验
We develop kernel-based specification tests for semiparametric conditional moment models with high-dimensional nuisance parameters, extending existing conditional moment tests---which typically require asymptotically linear nuisance estimators---to accommodate modern machine-learning methods. The proposed locally robust kernel tests combine Neyman-orthogonal moments, cross-fitting, and reproducing kernel Hilbert space methods, yielding inference that is first-order…