confounders.poly: Sensitivity analysis to correct for unknown or unmeasured...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Sensitivity analysis to correct for unknown or unmeasured polychotomous confounding without effect modification

Description

Simple sensitivity analysis to correct for unknown or unmeasured polychotomous (3-level) confounding without effect modification. Implementation for ratio measures (relative risk – RR, or odds ratio – OR) and difference measures (risk difference – RD).

Usage

confounders.poly(
  case,
  exposed,
  type = c("RR", "OR", "RD"),
  bias_parms = NULL,
  alpha = 0.05
)

Arguments

case	Outcome variable. If a variable, this variable is tabulated against.
exposed	Exposure variable.
type	Choice of implementation, with no effect measure modification for ratio measures (relative risk – RR; odds ratio – OR) or difference measures (risk difference – RD).
bias_parms	Numeric vector defining the bias parameters. This vector has 6 elements, in the following order: the association between the highest level confounder and the outcome, the association between the mid-level confounder and the outcome, the prevalence of the highest level confounder among the exposed (between 0 and 1), the prevalence of the highest level confounder among the unexposed (between 0 and 1), the prevalence of the mid-level confounder among the exposed (between 0 and 1), and the prevalence of the mid-level confounder among the unexposed (between 0 and 1).
alpha	Significance level.

Value

A list with elements:

obs.data	The analyzed 2 x 2 table from the observed data.
cfder1.data	The same table for Mid-level Confounder +.
cfder2.data	The same table for Highest-level Confounder +.
nocfder.data	The same table for Confounder -.
obs.measures	A table of relative risk with confidence intervals; Total and by confounders.
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.poly(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RR",
bias_parms = c(.4, .8, .6, .05, .2, .2))

confounders.poly(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "OR",
bias_parms = c(.4, .8, .6, .05, .2, .2))

confounders.poly(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RD",
bias_parms = c(-.4, -.2, .6, .05, .2, .2))

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