View source: R/probsens.irr.conf.R
probsens.irr.conf | R Documentation |
Probabilistic sensitivity analysis to correct for unmeasured confounding when person-time data has been collected.
probsens.irr.conf(
counts,
pt = NULL,
reps = 1000,
prev_exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
"beta"), parms = NULL),
prev_nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal", "normal",
"beta"), parms = NULL),
risk = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"log-logistic", "log-normal"), parms = NULL),
corr_p = NULL,
alpha = 0.05
)
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. | |||||||||
prev_exp |
List defining the prevalence of exposure among the exposed. The first argument provides the probability distribution function (constant, uniform, triangular, trapezoidal, truncated normal, or beta) and the second its parameters as a vector. Lower and upper bounds for truncated normal distribution cannot be les than zero.
| |||||||||
prev_nexp |
List defining the prevalence of exposure among the unexposed. | |||||||||
risk |
List defining the confounder-disease relative risk or the confounder-exposure odds ratio. The first argument provides the probability distribution function (constant,uniform, triangular, trapezoidal, log-logistic, or log-normal) and the second its parameters as a vector:
| |||||||||
corr_p |
Correlation between the exposure-specific confounder prevalences. | |||||||||
alpha |
Significance level. |
Correlations between prevalences of exposure classification among cases and controls can be specified and use the NORmal To Anything (NORTA) transformation (Li & Hammond, 1975).
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. |
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.conf_legacy()
as follows:
library(episensr) probsens.irr.conf <- probsens.irr.conf_legacy
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.
Other confounding:
confounders()
,
confounders.array()
,
confounders.evalue()
,
confounders.ext()
,
confounders.limit()
set.seed(123)
# Unmeasured confounding
probsens.irr.conf(matrix(c(77, 10000, 87, 10000),
dimnames = list(c("D+", "Person-time"), c("E+", "E-")), ncol = 2),
reps = 20000,
prev_exp = list("trapezoidal", c(.01, .2, .3, .51)),
prev_nexp = list("trapezoidal", c(.09, .27, .35, .59)),
risk = list("trapezoidal", c(2, 2.5, 3.5, 4.5)),
corr_p = .8)
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