Testing for treatment effect twice using internal and external controls in clinical trials [PDF]
Journal of Causal Inference, 2023 Leveraging external controls – relevant individual patient data under control from external trials or real-world data – has the potential to reduce the cost of randomized controlled trials (RCTs) while increasing the proportion of trial patients given ...
Yi Yanyao, Zhang Ying, Du Yu, Ye Ting
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Generalized coarsened confounding for causal effects: a large-sample framework [PDF]
Journal of Causal InferenceThere has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding.
Ghosh Debashis, Wang Lei
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Semiparametric discovery and estimation of interaction in mixed exposures using stochastic interventions [PDF]
Journal of Causal InferenceUnderstanding the complex interactions among multiple environmental exposures is critical for assessing their combined impact on health outcomes. This study introduces InterXshift, a novel semiparametric method that provides a nonparametric definition of
McCoy David B. +3 more
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Impact of supervisors' research style on young biomedical scientists' capacity development as measured by REDi, a novel index of crossdisciplinarity [PDF]
Frontiers in Research Metrics and Analytics, 2022 The challenge for medical schools in Japan is to develop research activities for innovation. This study aimed at analyzing the connection between the research output of “promising researchers” (next-generation leaders in terms of research activity) and ...
Akiko Hashiguchi +3 more
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Bounding causal effects with an unknown mixture of informative and non-informative missingness [PDF]
Journal of Causal InferenceIn experimental and observational data settings, researchers often have limited knowledge of the reasons for missing outcomes. To address this uncertainty, we propose bounds on causal effects for missing outcomes, accommodating the scenario where ...
Rubinstein Max +4 more
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Discovery of critical thresholds in mixed exposures and estimation of policy intervention effects [PDF]
Journal of Causal InferenceRegulations of chemical exposures often focus on individual substances, neglecting the amplified toxicity that can arise from multiple concurrent exposures.
McCoy David B. +3 more
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Assessing surrogate heterogeneity in real world data using meta-learners [PDF]
Journal of Causal InferenceSurrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes also extends to real-world public health and social science research, where randomized trials are often impractical ...
Knowlton Rebecca, Parast Layla
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Open Mathematics, 2022 In this article, we propose a Gompertz-two-parameter-Lindley distribution by mixing the frailty parameter of the Gompertz distribution with a two-parameter Lindley distribution. The structural properties of the model, such as shape properties, cumulative
Ou Xionghui, Lu Hezhi, Kong Jingsen
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Dependence Modeling, 2021 Consider a survival time study, where a sequence of possibly censored failure times is observed with d-dimensional covariate The main goal of this article is to establish the asymptotic normality of the kernel estimator of the relative error regression ...
Bouhadjera Feriel, Saïd Elias Ould
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Decomposition of the total effect for two mediators: A natural mediated interaction effect framework
Journal of Causal Inference, 2022 Mediation analysis has been used in many disciplines to explain the mechanism or process that underlies an observed relationship between an exposure variable and an outcome variable via the inclusion of mediators.
Gao Xin, Li Li, Luo Li
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