Calculates effect estimates from models with all possible combinations of a list of variables specified by users. Effect estimates can be regression coefficients, odds ratios and hazard ratios depending on modeling methods. This approach can be used for assessing treatment effects in clinical trials or the effects of a risk factor in observational biomedical and epidemiological studies.
You can install the released version of allestimates from CRAN with:
install.packages("allestimates")Using diab_df data to assess the association between
marital status Married and and Diabetes as an
example. Several other factors might potentially influence the
association (odds ratio estimates) between Married and
Diabetes variables.
library(allestimates)
data(diab_df)
diab_df$Overweight = as.numeric(diab_df$BMI >= 25)
vlist <- c("Age", "Sex", "Education","Smoke", "BMI", "Income")
results <- all_speedglm(crude = "Diabetes ~ Married", xlist = vlist, data = diab_df)
#> estimate: Odds Ratio or Rate Ratio
#> Crude model: Diabetes ~ MarriedAll those possible effect estimates (odds ratios in this case) are
stored in an object results and can be used later for
further analysis and generating various graphs.
all_plot(results)
From
this all_plot graph, we can see that all estimated odds
ratio values fell in the left-side two quarters, with either a positive
or a negative association but p values in all possible models were
greater than 0.05.
all_plot2(results)
all_plot2 graph shows effect estimates with a specific
variable being included or not included in the model. This can be
helpful in combination with biological background knowledge to
understand potential confounding effects, uncertainly of the effect
estimates, and inappropriate inclusion of some variables.
Note: Interaction terms and other derived
variables can be listed. Those terms need to be generated first before
running an allestimates function.