calc_risk | R Documentation |
Computes the treatment efficacy (TE) and other functions of the risk in each treatment arm over the range of surrogate values observed in the data. TE(s) is defined as 1 - risk(s, z = 1)/risk(s, z = 0), where z is the treatment indicator. If any other variables are present in the risk model, then the risk is computed at their median value.
calc_risk(
psdesign,
contrast = "TE",
t,
sig.level = 0.05,
CI.type = "band",
n.samps = 5000,
bootstraps = TRUE,
newdata = NULL
)
psdesign |
A psdesign object. It must contain a risk model, an integration model, and estimated parameters. Bootstrapped parameters are optional |
contrast |
The contrast function, or the name of the contrast function. See details. |
t |
For time to event outcomes, a fixed time |
sig.level |
Significance level for bootstrap confidence intervals |
CI.type |
Character string, "pointwise" for pointwise confidence intervals, and "band" for simultaneous confidence band. |
n.samps |
The number of samples to take over the range of S.1 at which the contrast is calculated |
bootstraps |
If true, and bootstrapped estimates are present, will calculate bootstrap standard errors and confidence bands. |
newdata |
Vector of S values. If present, will calculate the contrast function at values of newdata instead of the observed S.1 |
The contrast function is a function that takes 2 inputs, the risk_0
and risk_1, and returns some one dimensional function of those two inputs.
It must be vectorized. Some built-in functions are "TE"
for treatment
efficacy = 1 - risk_1(s)/risk_0(s), "RR"
for relative risk =
risk_1(s)/risk_0(s), "logRR"
for log of the relative risk, and
"RD"
for the risk difference = risk_1(s) - risk_0(s).
A data frame containing columns for the S values, the computed contrast function at S, R0, and R1 at those S values, and optionally standard errors and confidence intervals computed using bootstrapped estimates.
## Not run:
# same result passing function name or function
calc_risk(binary.boot, contrast = "TE", n.samps = 20)
calc_risk(binary.boot, contrast = function(R0, R1) 1 - R1/R0, n.samps = 20)
## End(Not run)
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