View source: R/confounders.poly.R
confounders.poly  R Documentation 
Simple sensitivity analysis to correct for unknown or unmeasured polychotomous (3level) confounding without effect modification. Implementation for ratio measures (relative risk – RR, or odds ratio – OR) and difference measures (risk difference – RD).
confounders.poly(
case,
exposed,
type = c("RR", "OR", "RD"),
bias_parms = NULL,
alpha = 0.05
)
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:

alpha 
Significance level. 
A list with elements:
obs.data 
The analyzed 2 x 2 table from the observed data. 
cfder1.data 
The same table for Midlevel Confounder +. 
cfder2.data 
The same table for Highestlevel 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 MantelHaenszel estimates. 
bias.parms 
Input bias parameters. 
Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.59–78, Springer.
# The data for this example come from:
# Tyndall M.W., Ronald A.R., Agoki E., Malisa W., Bwayo J.J., NdinyaAchola 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;23:44953.
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))
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