episensr (version 0.7.1)

confounders.emm: Sensitivity analysis to correct for unknown or unmeasured confounding with effect modification

Description

Simple sensitivity analysis to correct for unknown or unmeasured confounding with effect measure modification. Implementation for ratio measures (relative risk -- RR, or odds ratio -- OR) and difference measures (risk difference -- RD).

Usage

confounders.emm(exposed, case, implement = c("RR", "OR", "RD"),
  type = c("RR", "OR", "RD"), p = NULL, RR.cd = NULL, OR.cd = NULL,
  RD.cd = NULL, alpha = 0.05, dec = 4, print = TRUE)

Arguments

exposed

Exposure variable. If a variable, this variable is tabulated against.

case

Outcome variable.

implement

Deprecated. Please use type instead.

type

Choice of implementation, with no effect measure modification for ratio measures (relative risk -- RR; odds ratio -- OR) or difference measures (risk difference -- RD).

p

Numeric vector defining the prevalence of the confounder. This vector has 2 elements between 0 and 1, in the following order:

1. Prevalence of the confounder among the exposed, and
2. Prevalence of the confounder among the unexposed.

RR.cd

Vector defining the confounder-disease relative risk. This vector has two elements between 0 and 1, in the following order:

1. Among the exposed, and
2. among the unexposed.

OR.cd

Vector defining the confounder-disease odds ratio. This vector has two elements between 0 and 1, in the folowing order:

1. Among the exposed, and
2. among the unexposed.

RD.cd

Vector dfining the confounder-disease risk difference. This vector has two elements between 0 and 1, in the following order:

1. Among the exposed, and
2. among the unexposed.

alpha

Significance level.

dec

Number of decimals in the printout.

print

A logical scalar. Should the results be printed?

Value

• A list with elements:
• obs.data
  The analysed 2 x 2 table from the observed data.
• cfder.data
  The same table for Confounder +.
• nocfder.data
  The same table for Confounder -.
• obs.measures
  A table of relative risk with confidence intervals; Total, for Confoundner +, and for Confounder -.
• adj.measures
  A table of Standardized Morbidity Ratio and Mantel-Haenszel estimates.
• bias.parms
  Input bias parameters.

References

Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.59--78, Springer.

Examples

# The data for this example come from:
# Tyndall M.W., Ronald A.R., Agoki E., Malisa W., Bwayo J.J., Ndinya-Achola J.O.
# et al.
# Increased risk of infection with human immunodeficiency virus type 1 among
# uncircumcised men presenting with genital ulcer disease in Kenya.
# Clin Infect Dis 1996;23:449-53.
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RR",
p = c(.8, .05),
RR.cd = c(.4, .7))
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "OR",
p = c(.8, .05),
OR.cd = c(.4, .7))
confounders.emm(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RD",
p = c(.8, .05),
RD.cd = c(-.6, -.3))