General guidelines for choosing a statistical analysis based on the number of dependent variables, the nature of your independent variables, and whether the dependent variable is an interval variable, ordinal or categorical variable, and whether it is normally distributed. From UCLA's Institute for Digital Research and Education.
Computes the E-value, defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to fully explain away a specific exposure-outcome association. You can alternatively conduct these analyses using the R package EValue.
In this article, we’ll describe the Cox regression model and provide practical examples using R software. The Cox proportional-hazards model is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables.
Tidy, analyze, and plot causal directed acyclic graphs (DAGs). ggdag uses the powerful dagitty package to create and analyze structural causal models and plot them using ggplot2 and ggraph in a consistent and easy manner.
Interpreting the logistic regression’s coefficients is somehow tricky. Looking at some examples beside doing the math helps getting the concept of odds, odds ratios and consequently getting more familiar with the meaning of the regression coefficients. The following examples are mainly taken from IDRE UCLE FAQ Page and they are recreated with R.
The Department of Statistics operates a free consulting service for members of the campus community. Advanced graduate students, under faculty supervision, consult by appointment in the fall and spring semesters. We do not run the consulting service during the summer.