episensr (version 0.7.1)

probsens: Probabilistic sensitivity analysis.

Description

Probabilistic sensitivity analysis to correct for exposure misclassification or outcome misclassification and random error.

Usage

probsens(exposed, case, type = c("exposure", "outcome"), reps = 1000,
  seca.parms = list(dist = c("constant", "uniform", "triangular",
  "trapezoidal", "logit-logistic", "logit-normal"), parms = NULL),
  seexp.parms = NULL, spca.parms = list(dist = c("constant", "uniform",
  "triangular", "trapezoidal", "logit-logistic", "logit-normal"), parms = NULL),
  spexp.parms = NULL, corr.se = NULL, corr.sp = NULL, discard = TRUE,
  alpha = 0.05, dec = 4, print = TRUE)

Arguments

exposed

Exposure variable. If a variable, this variable is tabulated against.

case

Outcome variable.

type

Choice of correction for exposure or outcome misclassification.

reps

Number of replications to run.

seca.parms

List defining the sensitivity of exposure classification among those with the outcome. The first argument provides the probability distribution function (constant, uniform, triangular, trapezoidal, logit-logistic, or logit-normal) and the second its param

seexp.parms

List defining the sensitivity of exposure classification among those without the outcome.

spca.parms

List defining the specificity of exposure classification among those with the outcome.

spexp.parms

List defining the specifity of exposure classification among those without the outcome.

corr.se

Correlation between case and non-case sensitivities.

corr.sp

Correlation between case and non-case specificities.

discard

A logical scalar. In case of negative adjusted count, should the draws be discarded? If set to FALSE, negative counts are set to zero.

alpha

Significance level.

dec

Number of decimals in the printout.

print

A logical scalar. Should the results be printed?

Value

• A list with elements:
• obs.data
  The analysed 2 x 2 table from the observed data.
• obs.measures
  A table of observed relative risk and odds ratio with confidence intervals.
• adj.measures
  A table of corrected relative risks and odds ratios.
• sim.df
  Data frame of random parameters and computed values.

References

Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.117--150, Springer.

Examples

# The data for this example come from:
# Greenland S., Salvan A., Wegman D.H., Hallock M.F., Smith T.J.
# A case-control study of cancer mortality at a transformer-assembly facility.
# Int Arch Occup Environ Health 1994; 66(1):49-54.
set.seed(123)
# Exposure misclassification, non-differential
probsens(matrix(c(45, 94, 257, 945),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "exposure",
reps = 20000,
seca.parms = list("trapezoidal", c(.75, .85, .95, 1)),
spca.parms = list("trapezoidal", c(.75, .85, .95, 1)))
# Exposure misclassification, differential
probsens(matrix(c(45, 94, 257, 945),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "exposure",
reps = 20000,
seca.parms = list("trapezoidal", c(.75, .85, .95, 1)),
seexp.parms = list("trapezoidal", c(.7, .8, .9, .95)),
spca.parms = list("trapezoidal", c(.75, .85, .95, 1)),
spexp.parms = list("trapezoidal", c(.7, .8, .9, .95)),
corr.se = .8,
corr.sp = .8)
# Disease misclassification
probsens(matrix(c(173, 602, 134, 663),
dimnames = list(c("BC+", "BC-"), c("Smoke+", "Smoke-")), nrow = 2, byrow = TRUE),
type = "outcome",
reps = 20000,
seca.parms = list("uniform", c(.8, 1)),
spca.parms = list("uniform", c(.8, 1)))