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

Description Usage Arguments Value References Examples

View source: R/confounders.poly.R

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

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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:

  1. the association between the highest level confounder and the outcome,

  2. the association between the mid-level confounder and the outcome,

  3. the prevalence of the highest level confounder among the exposed (between 0 and 1),

  4. the prevalence of the highest level confounder among the unexposed (between 0 and 1),

  5. the prevalence of the mid-level confounder among the exposed (between 0 and 1), and

  6. 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

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# 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. 20, 2021, 9:06 a.m.