Multiple Bias Analysis in Causal Inference
Represent bias parameters
Represent observed causal data
Represent validation causal data
Simultaneously adjust for multiple biases
Create a Forest Plot comparing observed and adjusted effect estimates
multibias: Multiple Bias Analysis in Causal Inference
Print method for data_observed objects
Print method for data_validation objects
Summary method for data_observed objects
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>.
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