Simultaneous Multi-Bias Adjustment
Adust for exposure misclassification and outcome misclassification.
Adust for exposure misclassification and selection bias.
Adust for exposure misclassification.
Adust for exposure misclassification and outcome misclassification.
Adust for exposure misclassification and selection bias.
Adust for exposure misclassification.
Adust for outcome misclassification and selection bias.
Adust for outcome misclassification.
Adust for outcome misclassification and selection bias.
Adust for outcome misclassification.
Adust for selection bias.
Adust for uncontrolled confounding, exposure misclassification, and se...
Adust for uncontrolled confounding and exposure misclassification.
Adust for uncontrolled confounding, exposure misclassification, and se...
Adust for uncontrolled confounding and exposure misclassification.
Adust for uncontrolled confounding, outcome misclassification, and sel...
Adust for uncontrolled confounding and outcome misclassification.
Adust for uncontrolled confounding, outcome misclassification, and sel...
Adust for uncontrolled confounding and outcome misclassification.
Adust for uncontrolled confounding and selection bias.
Adust for uncontrolled confounding.
Represent observed causal data
Represent validation causal data
multibias: Simultaneous Multi-Bias Adjustment
Quantify the causal effect of a binary exposure on a binary outcome with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the concept of combining inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.