episensr (version 0.7.1)

probsens.sel: Probabilistic sensitivity analysis for selection bias.

Description

Probabilistic sensitivity analysis to correct for selection bias.

Usage

probsens.sel(exposed, case, reps = 1000, or.parms = list(dist =
  c("constant", "uniform", "triangular", "trapezoidal", "logit-logistic",
  "logit-normal"), parms = NULL), alpha = 0.05, dec = 4, print = TRUE)

Arguments

exposed

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

case

Outcome variable.

reps

Number of replications to run.

or.parms

List defining the selection bias odds. The first argument provides the probability distribution function (constant, uniform, triangular, or trapezoidal) and the second its parameters as a vector:

1. Constant: constant value,
2. Uniform: min, max

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 odds ratio with confidence intervals.
• adj.measures
  A table of corrected 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:
# Stang A., Schmidt-Pokrzywniak A., Lehnert M., Parkin D.M., Ferlay J., Bornfeld N. et al.
# Population-based incidence estimates of uveal melanoma in Germany.
# Supplementing cancer registry data by case-control data.
# Eur J Cancer Prev 2006;15:165-70.
set.seed(123)
probsens.sel(matrix(c(136, 107, 297, 165),
dimnames = list(c("Melanoma+", "Melanoma-"), c("Mobile+", "Mobile-")), nrow = 2, byrow = TRUE),
reps = 20000,
or.parms = list("triangular", c(.35, 1.1, .43)))