selection: Sensitivity analysis to correct for selection bias.

In episensr: Basic Sensitivity Analysis of Epidemiological Results

Sensitivity analysis to correct for selection bias.

Description

Simple sensitivity analysis to correct for selection bias using estimates of the selection proportions.

Usage

selection(case, exposed, bias_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 [(1*4)/(2*3) from above].
alpha	Significance level.

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.

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)

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