logisticCoefs | R Documentation |
This is an implementation of an iterative bisection procedure that can be used to determine coefficient values for a target population prevalence as well as a target risk ratio, risk difference, or AUC. These coefficients can be used in a subsequent data generation process to simulate data with these desire characteristics.
logisticCoefs(
defCovar,
coefs,
popPrev,
rr = NULL,
rd = NULL,
auc = NULL,
tolerance = 0.001,
sampleSize = 1e+05,
trtName = "A"
)
defCovar |
A definition table for the covariates in the underlying population. This tables specifies the distribution of the covariates. |
coefs |
A vector of coefficients that reflect the relationship between each of the covariates and the log-odds of the outcome. |
popPrev |
The target population prevalence of the outcome. A value between 0 and 1. |
rr |
The target risk ratio, which must be a value between 0 and 1/popPrev. Defaults to NULL. |
rd |
The target risk difference, which must be between -(popPrev) and (1 - popPrev). Defaults to NULL |
auc |
The target AUC, which must be a value between 0.5 and 1.0 . Defaults to NULL. |
tolerance |
The minimum stopping distance between the adjusted low and high endpoints. Defaults to 0.001. |
sampleSize |
The number of units to generate for the bisection algorithm. The default is 1e+05. To get a reliable estimate, the value should be no smaller than the default, though larger values can be used, though computing time will increase. |
trtName |
If either a risk ratio or risk difference is the target statistic, a treatment/exposure variable name can be provided. Defaults to "A". |
If no specific target statistic is specified, then only the intercept is returned along with the original coefficients. Only one target statistic (risk ratio, risk difference or AUC) can be specified with a single function call; in all three cases, a target prevalence is still required.
A vector of parameters including the intercept and covariate coefficients for the logistic model data generating process.
Austin, Peter C. "The iterative bisection procedure: a useful tool for determining parameter values in data-generating processes in Monte Carlo simulations." BMC Medical Research Methodology 23, no. 1 (2023): 1-10.
## Not run:
d1 <- defData(varname = "x1", formula = 0, variance = 1)
d1 <- defData(d1, varname = "b1", formula = 0.5, dist = "binary")
coefs <- log(c(1.2, 0.8))
logisticCoefs(d1, coefs, popPrev = 0.20)
logisticCoefs(d1, coefs, popPrev = 0.20, rr = 1.50, trtName = "rx")
logisticCoefs(d1, coefs, popPrev = 0.20, rd = 0.30, trtName = "rx")
logisticCoefs(d1, coefs, popPrev = 0.20, auc = 0.80)
## End(Not run)
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