Problem
Research questions and friction points this paper is trying to address.
sequential target trial emulation
per-protocol effects
treatment deviations
loss to follow-up
cumulative weights
From Cumulative Weights to Marginal Density Ratios: Per-Protocol Estimation in Sequential Target Trial Emulation
🤖 AI Summary
本文提出了一种基于边际密度比的新方法来解决序列目标试验模拟中的依从性问题，以更稳定地估计效果。
📝 Abstract
Sequential target trial emulation evaluates eligibility at multiple baseline times to emulate a sequence of randomized trials using observational data. Estimating per-protocol effects in this setting is challenging because treatment deviations and loss to follow-up induce selection among individuals who remain observed and adherent over time. Conventional inverse-probability methods address this selection using cumulative weights constructed from estimated adherence and censoring probabilities, but these weights can be highly variable, leading to unstable and imprecise effect estimates. We propose a different approach based on marginal density ratios (MDRs). The MDR directly compares the state distribution among individuals who would remain event-free under a target treatment strategy with the corresponding distribution among observed-adherent individuals. We use longitudinal g-computation to generate the target risk sets and a probabilistic classifier to estimate density ratios for reweighting the observed outcomes. Building on this approach, we also develop a doubly robust extension. Favorable performance across the simulation study suggests that MDR weighting is a promising alternative to cumulative longitudinal weights when its identification assumptions are plausible.
Innovation
Methods, ideas, or system contributions that make the work stand out.
marginal density ratios
sequential target trial emulation
per-protocol effects
longitudinal g-computation
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