Most cited
In this paper, we analyze the length-biased and partly interval-censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter ...
Mixture priors provide an intuitive way to incorporate historical data while accounting for potential prior-data conflict by combining an informative prior with a noninformative prior. However, prespecifying the mixing weight for each component remains a crucial challenge. Ideally, the mixing weight should reflect the ...
Q-learning has been one of the most commonly used methods for optimizing dynamic treatment regimes (DTRs) in multistage decision-making. Right-censored survival outcome poses a significant challenge to Q-Learning due to its reliance on parametric models for counterfactual estimation which are subject to misspecification ...
Mendelian randomization (MR) is a widely used method to estimate the causal effect of an exposure on an outcome by using genetic variants as instrumental variables. MR analyses that use variants from only a single genetic region ( cis -MR) encoding the protein target of a drug are able to provide supporting evidence for ...
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Efron’s 2-group model is widely used in large-scale multiple testing. This model assumes that test statistics are drawn independently from a mixture of a null and a non-null distribution. The marginal local false discovery rate (locFDR) is the probability that the hypothesis is null given its test ...
Numerous statistical models have been proposed for conducting meta-analysis of diagnostic accuracy studies when a gold standard is available. However, in real-world scenarios, the gold standard test may not be perfect due to several factors such as measurement error, non-availability, invasiveness, ...
Covariate-adaptive randomization (CAR) is widely adopted in clinical trials to ensure balanced treatment allocations across key baseline covariates. Although much research has focused on analyzing average treatment effects, the inference of relative risk under CAR experiments has been less ...
Causal weighted quantile treatment effects (WQTEs) complement standard mean-focused causal contrasts when interest lies at the tails of the counterfactual distribution. However, existing methods for estimating and inferring causal WQTEs assume complete data on all relevant factors, which is often ...
Co-Editors
Cécile Proust-Lima
Erica Moodie
Matthew Schofield
Executive Editor
Geert Molenberghs
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