confounders.ext: Sensitivity analysis for unmeasured confounders based on...

Description Usage Arguments Value References Examples

View source: R/confounders.ext.R

Description

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

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confounders.ext(RR, bias_parms = NULL, dec = 2, print = TRUE)

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).

dec

Number of decimals in the printout.

print

A logical scalar. Should the results be printed?

Value

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.

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

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# 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))

episensr documentation built on Aug. 20, 2021, 9:06 a.m.