Published in Journal of biopharmaceutical statistics
Individuals can vary drastically in their response to the same treatment, and this heterogeneity has driven the push for more personalized medicine. Accurate and interpretable methods to identify subgroups that respond to the treatment differently from the population average are necessary to achieving this goal. The Virtual Twins (VT) method is a h...
Published in Biometrics
Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejection sampling method used in rerandomization is computationally inefficient. As a result, it is time-consuming for rerandomization to draw numerous independent assignments, ...
Published in Biometrics
Many spatial phenomena exhibit interference, where exposures at one location may affect the response at other locations. Because interference violates the stable unit treatment value assumption, standard methods for causal inference do not apply. We propose a new causal framework to recover direct and spill-over effects in the presence of spatial i...
Published in Biometrics
Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the pre...
Published in Biometrics
The hazard ratio (HR) is often reported as the main causal effect when studying survival data. Despite its popularity, the HR suffers from an unclear causal interpretation. As already pointed out in the literature, there is a built-in selection bias in the HR, because similarly to the truncation by death problem, the HR conditions on post-treatment...
Published in Biometrics
In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Convent...
Published in Biometrics
Performing causal inference in observational studies requires we assume confounding variables are correctly adjusted for. In settings with few discrete-valued confounders, standard models can be employed. However, as the number of confounders increases these models become less feasible as there are fewer observations available for each unique combi...
Los efectos de las coaliciones electorales en el comportamiento electoral continúan siendo un terreno inexplorado en la sociología política. No existen apenas estudios que analicen los efectos del tránsito del enfrentamiento electoral a la colaboración preelectoral. Nuestra investigación se centra en el caso español, analizando el efecto que produj...
Published in Frontiers in Public Health
Background Low back pain (LBP) is a common condition and a leading cause of health function loss worldwide. This study assessed the impact of occupational factors on LBP using Mendelian Randomization (MR) method, controlling for confounding variables. Methods Based on publicly available genome-wide association studies (GWAS), two-sample univariate ...
Published in Frontiers in Aging Neuroscience
Aims Observational studies have shown that sleep pattern is associated with age-related macular degeneration (AMD), but whether sleep pattern is a causal factor for AMD remains unclear. This study aims to use Mendelian randomization (MR) analysis to investigate the potential causal relationship between sleep traits and AMD. Methods This is a two-sa...
