| 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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