confounders | R Documentation |
Simple sensitivity analysis to correct for unknown or unmeasured confounding without effect modification. Implementation for ratio measures (relative risk – RR, or odds ratio – OR) and difference measures (risk difference – RD).
confounders(
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 3 necessary bias parameters. This vector has 3 elements, in the following order:
|
alpha |
Significance level. |
The analytic approach uses the "relative risk due to confounding" as defined by
Miettinen (1972), i.e. RR_{adj} = \frac{RR_{crude}}{RR_{conf}}
where RR_adj
is the standardized (adjusted) risk ratio, RR_crude is the crude risk ratio, and
RR_conf is the relative risk component attributable to confounding by the
stratification factors. The output provides both RR_adj (SMR or Mantel-Haenszel)
and the RR_conf.
A list with elements:
obs.data |
The analyzed 2 x 2 table from the observed data. |
cfder.data |
The same table for Confounder +. |
nocfder.data |
The same table for Confounder -. |
obs.measures |
A table of relative risk with confidence intervals; for Total, Confounder +, and Confounder -. |
adj.measures |
A table of Standardized Morbidity Ratio and Mantel-Haenszel 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.
Miettinen, 1971. Components of the Crude Risk Ratio. Am J Epidemiol 96(2):168-172.
# 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(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RR",
bias_parms = c(.63, .8, .05))
confounders(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "OR",
bias_parms = c(.63, .8, .05))
confounders(matrix(c(105, 85, 527, 93),
dimnames = list(c("HIV+", "HIV-"), c("Circ+", "Circ-")),
nrow = 2, byrow = TRUE),
type = "RD",
bias_parms = c(-.37, .8, .05))
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