Probabilistic sensitivity analysis to correct for selection bias.
Usage
probsens.sel(
case,
exposed,
reps = 1000,
or.parms = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"log-logistic", "log-normal"), parms = NULL),
case.exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"logit-logistic", "logit-normal", "beta"), parms = NULL),
case.nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"logit-logistic", "logit-normal", "beta"), parms = NULL),
ncase.exp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"logit-logistic", "logit-normal", "beta"), parms = NULL),
ncase.nexp = list(dist = c("constant", "uniform", "triangular", "trapezoidal",
"logit-logistic", "logit-normal", "beta"), parms = NULL),
alpha = 0.05
)Arguments
- case
Outcome variable. If a variable, this variable is tabulated against.
- exposed
Exposure variable.
- reps
Number of replications to run.
- or.parms
List defining the selection bias odds. 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:
constant: constant value,
uniform: min, max,
triangular: lower limit, upper limit, mode,
trapezoidal: min, lower mode, upper mode, max.
log-logistic: shape, rate. Must be strictly positive,
log-normal: meanlog, sdlog. This is the mean and standard deviation on the log scale.
- case.exp
If or.parms not provided, defines the selection probability among case exposed. The first argument provides the probability distribution function and the second its parameters as a vector:
constant: constant value,
uniform: min, max,
triangular: lower limit, upper limit, mode,
trapezoidal: min, lower mode, upper mode, max.
logit-logistic: location, scale, lower bound shift, upper bound shift,
logit-normal: location, scale, lower bound shift, upper bound shift,
beta: alpha, beta.
- case.nexp
Same among cases non-exposed.
- ncase.exp
Same among non-cases exposed.
- ncase.nexp
Same among non-cases non-exposed.
- alpha
Significance level.
Value
A list with elements:
- obs.data
The analyzed 2 x 2 table from the observed data.
- obs.measures
A table of observed odds ratio with confidence intervals.
- adj.measures
A table of corrected odds ratios.
- sim.df
Data frame of random parameters and computed values.
- reps
Number of replications.
References
Lash, T.L., Fox, M.P, Fink, A.K., 2009 Applying Quantitative Bias Analysis to Epidemiologic Data, pp.117--150, Springer.
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.
set.seed(123)
probsens.sel(matrix(c(136, 107, 297, 165),
dimnames = list(c("Melanoma+", "Melanoma-"), c("Mobile+", "Mobile-")), nrow = 2, byrow = TRUE),
reps = 20000,
or.parms = list("triangular", c(.35, 1.1, .43)))
#> --Observed data--
#> Outcome: Melanoma+
#> Comparing: Mobile+ vs. Mobile-
#>
#> Mobile+ Mobile-
#> Melanoma+ 136 107
#> Melanoma- 297 165
#>
#> 2.5% 97.5%
#> Observed Odds Ratio: 0.7061267 0.5143958 0.9693215
#> ---
#> Median 2.5th percentile
#> Odds Ratio -- systematic error: 1.1858399 0.7167006
#> Odds Ratio -- systematic and random error: 1.1805330 0.6466106
#> 97.5th percentile
#> Odds Ratio -- systematic error: 1.8171632
#> Odds Ratio -- systematic and random error: 2.0544802