selection: Selection bias.
In episensr: Basic Sensitivity Analysis of Epidemiological Results

selection

R Documentation

Selection bias.

Description

selection() and probsens.sel() allow to provide adjusted measures of association corrected for selection bias.

Usage

selection(case, exposed, bias_parms = NULL, alpha = 0.05)

probsens.sel(
  case,
  exposed,
  reps = 1000,
  case_exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
    "beta"), parms = NULL),
  case_nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
    "beta"), parms = NULL),
  ncase_exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
    "beta"), parms = NULL),
  ncase_nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
    "normal", "beta"), parms = NULL),
  alpha = 0.05
)

Arguments

`case`	Outcome variable. If a variable, this variable is tabulated against.
`exposed`	Exposure variable.
`bias_parms`	Selection probabilities. Either a vector of 4 elements between 0 and 1 defining the following probabilities in this order can be provided: Selection probability among cases exposed (1), Selection probability among cases unexposed (2), Selection probability among noncases exposed (3), and Selection probability among noncases unexposed (4). or a single positive selection-bias factor which is the ratio of the exposed versus unexposed selection probabilities comparing cases and noncases ((14)/(23) from above).
`alpha`	Significance level.
`reps`	Number of replications to run.
`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. normal: truncated normal with lower bound, upper bound, mean, sd, 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.

Value

A list with elements:

`model`	Bias analysis performed.
`obs_data`	The analyzed 2 x 2 table from the observed data.
`corr_data`	The same table corrected for selection proportions.
`obs_measures`	A table of odds ratios and relative risk with confidence intervals.
`adj_measures`	Selection bias corrected measures of outcome-exposure relationship.
`bias_parms`	Input bias parameters: selection probabilities.
`selbias_or`	Selection bias odds ratio based on the bias parameters chosen.

A list with elements (for probsens.sel()):

`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.

Simple bias analysis with `selection()`

selection() allows you to run a simple sensitivity analysis to correct for selection bias using estimates of the selection proportions.

Probabilistic sensitivity analysis with `probsens.sel()`

probsens.sel() performs a summary-level probabilistic sensitivity analysis to correct for selection bias.

References

Fox, M.P, MacLehose, R.F., Lash, T.L., 2021 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.90–91, 274–279, 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.
selection(matrix(c(136, 107, 297, 165),
dimnames = list(c("UM+", "UM-"), c("Mobile+", "Mobile-")),
nrow = 2, byrow = TRUE),
bias_parms = c(.94, .85, .64, .25))


selection(matrix(c(136, 107, 297, 165),
dimnames = list(c("UM+", "UM-"), c("Mobile+", "Mobile-")),
nrow = 2, byrow = TRUE),
bias_parms = 0.43)
#
# 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(1234)
probsens.sel(matrix(c(139, 114, 369, 377),
dimnames = list(c("Melanoma+", "Melanoma-"), c("Mobile+", "Mobile-")), nrow = 2, byrow = TRUE),
reps = 5000,
case_exp = list("beta", c(139, 5.1)),
case_nexp = list("beta", c(114, 11.9)),
ncase_exp = list("beta", c(369, 96.1)),
ncase_nexp = list("beta", c(377, 282.9)))

episensr documentation built on June 8, 2025, 10:34 a.m.