Description Usage Arguments Value References Examples
View source: R/confounders.ext.R
Sensitivity analysis to explore effect of residual confounding using simple algebraic transformation. It provides the relative risk adjusted for unmeasured confounders based on available external information (i.e. from the literature) on the relation between confounders and outcome.
1  confounders.ext(RR, bias_parms = NULL, dec = 2, print = TRUE)

RR 
"True" or fully adjusted exposure relative risk. 
bias_parms 
Numeric vector defining the necessary bias parameters. This vector has 4 elements, in the following order:

dec 
Number of decimals in the printout. 
print 
A logical scalar. Should the results be printed? 
A vector with elements:
RR 
True (adjusted) exposure relative risk. 
RR_CD 
The association between the confounder and the outcome. 
OR_EC 
The association between exposure category and the confounder. 
P_C 
The prevalence of the confounder. 
P_E 
The prevalence of the exposure. 
crude.RR 
Crude (observed) exposure relative risk. 
bias_perc 
The bias as a percentage: (crude.RR  RR)/RR * 100. 
Schneeweiss, S., 2006. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Safety 15: 291303.
1 2 3 4  # Schneeweiss, S, Glynn, R.J., Tsai, E.H., Avorn, J., Solomon, D.H. Adjusting for
# unmeasured confounders in pharmacoepidemiologic claims data using external
# information. Epidemiology 2005; 16: 1724.
confounders.ext(RR = 1, bias_parms = c(0.1, 0.9, 0.1, 0.4))

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