episensr (version 1.3.0)

multidimBias: Multidimensional sensitivity analysis for different sources of bias

Description

Multidimensional sensitivity analysis for different sources of bias, where the bias analysis is repeated within a range of values for the bias parameter(s).

Usage

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

Value

A list with elements:

obs.data

The analyzed 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.

Arguments

case

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

exposed

Exposure variable.

type

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

se

Numeric vector of sensitivities. Parameter used with exposure or outcome misclassification.

sp

Numeric vector of specificities. Parameter used with exposure or outcome misclassification. Should be the same length as `se`.

bias_parms

List of bias parameters used with unmeasured confounder. 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

Deprecated; please use OR_sel instead.

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?

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_parms = 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))