probsens.irr: Probabilistic sensitivity analysis for exposure...

In episensr: Basic Sensitivity Analysis of Epidemiological Results

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: Exposed Unexposed Cases a b Person-time N1 N0
pt	A numeric vector of person-time at risk. If provided, counts must be a numeric vector of disease counts.
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. constant: constant value, uniform: min, max, triangular: lower limit, upper limit, mode, trapezoidal: min, lower mode, upper mode, max, normal: lower bound, upper bound, mean, sd, beta: alpha, beta.
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))

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