episensr (version 0.7.1)

multidimBias: Multidimensional sensitivity analysis for different sources of bias

Description

Multidimensional sensitivity analysis for different sources of bias

Usage

multidimBias(exposed, case, type = c("exposure", "outcome", "confounder",
  "selection"), se = NULL, sp = NULL, bias = NULL, OR.sel = NULL,
  alpha = 0.05, dec = 4, print = TRUE)

Arguments

exposed

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

case

Outcome variable.

type

Implement analysis for exposure misclassification, outcome misclassification, unmeasured confounder, or selection bias.

se

Numeric vector of sensitivities.

sp

Numerci vector of specificities.

bias

List of bias parameters. The list is made of 3 vectors of the same length:

1. Prevalence of Confounder in Exposure+ population,
2. Prevalence of Confounder in Exposure- population, and
3. Relative risk between Confounder and Outcome.

OR.sel

Selection odds ratios, for selection bias implementation.

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.
• obs.measures
  A table of odds ratios and relative risk with confidence intervals.
• adj.measures
  Multidimensional corrected relative risk and/or odds ratio data.
• bias.parms
  Bias parameters.

References

Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.109--116, Springer.

Examples

multidimBias(matrix(c(45, 94, 257, 945),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "exposure",
se = c(1, 1, 1, .9, .9, .9, .8, .8, .8),
sp = c(1, .9, .8, 1, .9, .8, 1, .9, .8))
multidimBias(matrix(c(45, 94, 257, 945),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "outcome",
se = c(1, 1, 1, .9, .9, .9, .8, .8, .8),
sp = c(1, .9, .8, 1, .9, .8, 1, .9, .8))
multidimBias(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "confounder",
bias = list(seq(.72, .92, by = .02),
seq(.01, .11, by = .01), seq(.13, 1.13, by = .1)))
multidimBias(matrix(c(136, 107, 297, 165),
dimnames = list(c("Uveal Melanoma+", "Uveal Melanoma-"),
c("Mobile Use+", "Mobile Use -")),
nrow = 2, byrow = TRUE),
type = "selection",
OR.sel = seq(1.5, 6.5, by = .5))