Case Study: Causal inference for observational data using modelbased

GitHub - NickCH-K/causaldata: Packages of Example Data for The Effect

Packages of Example Data for The Effect. Contribute to NickCH-K/causaldata development by creating an account on GitHub.

Bayes-by Shower

Sensitivity Analyses for Unmeasured Confounders - Current Epidemiology Reports

Purpose of Review This review expands on sensitivity analyses for unmeasured confounding techniques, demonstrating state-of-the-art methods as well as specifying which should be used under various scenarios, depending on the information about a potential unmeasured confounder available to the researcher. Recent Findings Methods to assess how sensitive an observed estimate is to unmeasured confounding have been developed for decades. Recent advancements have allowed for the incorporation of measured confounders in these assessments, updating the methods used to quantify the impact of an unmeasured confounder, whether specified in terms of the magnitude of the effect from a regression standpoint, for example, as a risk ratio, or with respect to the percent of variation in the outcome or exposure explained by the unmeasured confounder. Additionally, single number summaries, such as the E-value or robustness value, have been proposed to allow for ease of computation when less is known about a specific potential unmeasured confounder. Summary This paper aimed to provide methods and tools to implement sensitivity to unmeasured confounder analyses appropriate for various research settings depending on what is known or assumed about a potential unmeasured confounder. We have provided mathematical justification, recommendations, as well as R code to ease the implementation of these methods.