probsens.sel: Probabilistic sensitivity analysis for selection bias.

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Probabilistic sensitivity analysis for selection bias.

Description

Probabilistic sensitivity analysis to correct for selection bias.

Usage

probsens.sel(
  case,
  exposed,
  reps = 1000,
  or.parms = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "log-logistic", "log-normal"), parms = NULL),
  case.exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "logit-logistic", "logit-normal", "beta"), parms = NULL),
  case.nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "logit-logistic", "logit-normal", "beta"), parms = NULL),
  ncase.exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "logit-logistic", "logit-normal", "beta"), parms = NULL),
  ncase.nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "logit-logistic", "logit-normal", "beta"), parms = NULL),
  alpha = 0.05
)

Arguments

case	Outcome variable. If a variable, this variable is tabulated against.
exposed	Exposure 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, trapezoidal, log-logistic or log-normal) and the second its parameters as a vector: constant: constant value, uniform: min, max, triangular: lower limit, upper limit, mode, trapezoidal: min, lower mode, upper mode, max. log-logistic: shape, rate. Must be strictly positive, log-normal: meanlog, sdlog. This is the mean and standard deviation on the log scale.
case.exp	If or.parms not provided, defines the selection probability among case exposed. The first argument provides the probability distribution function and the second its parameters as a vector: constant: constant value, uniform: min, max, triangular: lower limit, upper limit, mode, trapezoidal: min, lower mode, upper mode, max. logit-logistic: location, scale, lower bound shift, upper bound shift, logit-normal: location, scale, lower bound shift, upper bound shift, beta: alpha, beta.
case.nexp	Same among cases non-exposed.
ncase.exp	Same among non-cases exposed.
ncase.nexp	Same among non-cases non-exposed.
alpha	Significance level.

Value

A list with elements:

obs.data	The analyzed 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.
reps	Number of replications.

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)))

episensr documentation built on Aug. 30, 2023, 5:09 p.m.