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Calculate the risk and functions of the risk

Source: R/summarize.R
calc_risk.Rd

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.

Usage

calc_risk(
  psdesign,
  contrast = "TE",
  t,
  sig.level = 0.05,
  CI.type = "band",
  n.samps = 5000,
  bootstraps = TRUE,
  newdata = NULL
)

Arguments

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 t may be provided to compute the cumulative distribution function. If not, the restricted mean survival time is used. Omit for binary outcomes.

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

Value

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.

Details

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).

Examples

if (FALSE) { # \dontrun{
# 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)
} # }