Coherent modeling of longitudinal causal effects on binary outcomes

Article Biology

Multiplicative effect modelling: the general case

J. Yin et al.

Summary: Generalized linear models, such as logistic regression, are widely used for modeling the association between a treatment and a binary outcome. However, the coefficients of logistic regression correspond to log odds ratios, whereas subject-matter scientists are often interested in relative risks. This paper proposes a novel binomial regression model that directly models the relative risk, addressing the limitations of previous models. The proposed methods are demonstrated to have desirable performance through Monte Carlo simulations and an analysis of survival rates for passengers on the Titanic.

BIOMETRIKA (2022)

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Article Biology

Estimating time-varying causal excursion effects in mobile health with binary outcomes

Tianchen Qian et al.

Summary: Advances in digital technology and wearables have allowed for the delivery of behavioral mobile health interventions to individuals in their everyday lives. Micro-randomized trials are increasingly used to provide data for constructing these interventions, focusing on longitudinal binary outcomes and examining the effects of time-varying interventions. Simulation studies are conducted to compare estimators for causal effects in micro-randomized trials with binary outcomes.

BIOMETRIKA (2021)

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Article Biology

Estimation of local treatment effects under the binary instrumental variable model

Linbo Wang et al.

Summary: Instrumental variables are commonly used to address unmeasured confounding in observational studies and imperfect randomized controlled trials. This paper focuses on estimating the local average treatment effect under the binary instrumental variable model, highlighting the challenges of causal estimation with a binary outcome and proposing novel modelling and estimation procedures for improvement.

BIOMETRIKA (2021)

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Article Statistics & Probability