multidimBias: Multidimensional sensitivity analysis for different sources...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

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
)

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: Prevalence of Confounder in Exposure+ population, Prevalence of Confounder in Exposure- population, and 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?

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.

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))

episensr documentation built on Aug. 30, 2023, 5:09 p.m.