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Simple sensitivity analysis to correct for selection bias using estimates of the selection proportions.
selection(case, exposed, bias_parms = NULL, alpha = 0.05)A list with elements:
Bias analysis performed.
The analyzed 2 x 2 table from the observed data.
The same table corrected for selection proportions.
A table of odds ratios and relative risk with confidence intervals.
Selection bias corrected measures of outcome-exposure relationship.
Input bias parameters: selection probabilities.
Selection bias odds ratio based on the bias parameters chosen.
Outcome variable. If a variable, this variable is tabulated against.
Exposure variable.
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].
Significance level.
# 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)
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