Multidimensional sensitivity analysis for different sources of bias, where the bias analysis is repeated within a range of values for the bias parameter(s).
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
)
A list with elements:
The analyzed 2 x 2 table from the observed data.
A table of odds ratios and relative risk with confidence intervals.
Multidimensional corrected relative risk and/or odds ratio data.
Bias parameters.
Outcome variable. If a variable, this variable is tabulated against.
Exposure variable.
Implement analysis for exposure misclassification, outcome misclassification, unmeasured confounder, or selection bias.
Numeric vector of sensitivities. Parameter used with exposure or outcome misclassification.
Numeric vector of specificities. Parameter used with exposure or outcome misclassification. Should be the same length as `se`.
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.
Deprecated; please use OR_sel instead.
Selection odds ratios, for selection bias implementation.
Significance level.
Number of decimals in the printout.
A logical scalar. Should the results be printed?
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))
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