Sensitivity analysis for unmeasured confounders based on external adjustment

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.

Usage

confounders.ext(RR, bias_parms = NULL)

Arguments

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:

1. the association between the confounder and the outcome (RR, relative risk),

2. the association between exposure category and the confounder (OR, odds ratio),

3. the prevalence of the confounder (between 0 and 1), and

4. the prevalence of the exposure (between 0 and 1).

Value

A list with elements:

model

Bias analysis performed.

bias.parms

Input bias parameters.

adj.measures

Output results, with bias as a percentage: (crude.RR - RR)/RR * 100.

References

Schneeweiss, S., 2006. Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics. Pharmacoepidemiol Drug Safety 15: 291-303.

Examples

# 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: 17-24.
confounders.ext(RR = 1, bias_parms = c(0.1, 0.9, 0.1, 0.4))
#> --Input bias parameters--
#>                                      
#> RR(Confounder-Disease):           0.1
#> OR(Exposure category-Confounder): 0.9
#> p(Confounder):                    0.1
#> p(Exposure):                      0.4
#> ---
#> 
#>                       
#> Crude RR     1.0093282
#> Percent bias 0.9328199