probsens.irr {episensr} | R Documentation |
Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.
Description
Probabilistic sensitivity analysis to correct for exposure misclassification
when person-time data has been collected.
Non-differential misclassification is assumed when only the two bias parameters
seca
and spca
are provided. Adding the 2 parameters
seexp
and spexp
(i.e. providing the 4 bias parameters)
evaluates a differential misclassification.
Usage
probsens.irr(
counts,
pt = NULL,
reps = 1000,
seca = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
"beta"), parms = NULL),
seexp = NULL,
spca = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
"beta"), parms = NULL),
spexp = NULL,
corr_se = NULL,
corr_sp = NULL,
alpha = 0.05
)
Arguments
counts |
A table or matrix where first row contains disease counts and second row contains person-time at risk, and first and second columns are exposed and unexposed observations, as:
| |||||||||
pt |
A numeric vector of person-time at risk. If provided, | |||||||||
reps |
Number of replications to run. | |||||||||
seca |
List defining the sensitivity of exposure classification among those with the outcome. The first argument provides the probability distribution function (uniform, triangular, trapezoidal, truncated normal, or beta) and the second its parameters as a vector. Lower and upper bounds of the truncated normal have to be between 0 and 1.
| |||||||||
seexp |
List defining the sensitivity of exposure classification among those without the outcome. | |||||||||
spca |
List defining the specificity of exposure classification among those with the outcome. | |||||||||
spexp |
List defining the specificity of exposure classification among those without the outcome. | |||||||||
corr_se |
Correlation between case and non-case sensitivities. | |||||||||
corr_sp |
Correlation between case and non-case specificities. | |||||||||
alpha |
Significance level. |
Details
Correlations between sensitivity (or specificity) of exposure classification among cases and controls can be specified and use the NORmal To Anything (NORTA) transformation (Li & Hammond, 1975).
Value
A list with elements:
obs_data |
The analyzed 2 x 2 table from the observed data. |
obs_measures |
A table of observed incidence rate ratio with exact confidence interval. |
adj_measures |
A table of corrected incidence rate ratios. |
sim_df |
Data frame of random parameters and computed values. |
Updated calculations
episensr 2.0.0 introduced updated calculations of probabilistic bias analyses
by (1) using the NORTA transformation to define a correlation between
distributions, and (2) sampling true prevalences and then sampling the
adjusted cell counts rather than just using the expected cell counts from a
simple quantitative bias analysis. This updated version should be preferred
but if you need to run an old analysis, you can easily revert to the
computation using probsens.irr_legacy()
as follows:
library(episensr) probsens.irr <- probsens.irr_legacy
References
Li, S.T., Hammond, J.L., 1975. Generation of Pseudorandom Numbers with Specified Univariate Distributions and Correlation Coefficients. IEEE Trans Syst Man Cybern 5:557-561.
See Also
Other misclassification:
misclass()
,
misclass_cov()
Examples
set.seed(123)
# Exposure misclassification, non-differential
probsens.irr(matrix(c(2, 67232, 58, 10539000),
dimnames = list(c("GBS+", "Person-time"), c("HPV+", "HPV-")), ncol = 2),
reps = 20000,
seca = list("trapezoidal", c(.4, .45, .55, .6)),
spca = list("constant", 1))