library("matchatr")
fit <- matcha(cohort, outcome = "d", exposure = "x",
design = case_cohort(subcohort = "sc", time = "t"),
confounders = ~z, estimator = "cch")
absolute_risk(fit, newdata = data.frame(x = 1, z = 0), times = c(1, 2, 3))Absolute risk from a sampled-cohort survival fit
Description
Estimates the cumulative incidence F_x(t) from a fitted survival design. Three engines are supported: the case-cohort pseudo-likelihood (“cch”, Borgan & Liestøl 1990) and the IPW nested case-control weighted Cox (“ipw_cox”, Samuelsen-weighted Breslow over the reused controls) both build F_x(t) = 1 − exp(−exp(β̂ᵀ x) Λ̂₀(t)) from an inverse-probability-weighted (IPW) Breslow cumulative baseline hazard Λ̂₀(t); the IPW nested case-control parametric AFT (“ipw_aft”, any of the Weibull / exponential / lognormal / loglogistic baselines) builds F_x(t) = G((log t − η̂)/σ̂) directly from the fitted survival curve, where η̂ is the AFT linear predictor, σ̂ the scale, and G the baseline error CDF. Pointwise confidence intervals use the delta method: for the Cox-type engines and the extreme-value AFT baselines this is the complementary log-log scale inverted to the risk scale; for the lognormal / loglogistic baselines it is the Wald interval on the standardised residual mapped through G.
Usage
absolute_risk(fit, ...)
## S3 method for class 'matchatr_fit'
absolute_risk(fit, newdata, times, conf_level = 0.95, ...)
Arguments
fit
|
A matchatr_fit object returned by matcha(). The case-cohort (“cch”), IPW nested case-control weighted Cox (“ipw_cox”), and IPW nested case-control Weibull AFT (“ipw_aft”) engines are supported.
|
…
|
Unused; present for S3 consistency. |
newdata
|
A data frame of covariate values at which to evaluate F_x(t). Must contain columns matching the exposure and confounders used in fit. Each row is one covariate pattern; the result table carries a row column that indexes back to newdata.
|
times
|
Non-empty numeric vector of evaluation times. Duplicates are dropped; times are sorted before evaluation. Times before the first event return F̂ = 0; times after the last event return the last Breslow value (step-function extrapolation). |
conf_level
|
Numeric in (0, 1). Confidence level for the pointwise intervals. Default 0.95.
|
Value
A matchatr_absolute_risk object. See absolute_risk.matchatr_fit for details on the return structure.
A matchatr_absolute_risk list with elements:
-
$estimates: adata.tablewith columnsrow(newdata row index),time,estimate(F̂_x(t)),ci_lower,ci_upper(delta-method CI on the probability scale). -
$times: the sorted evaluation times. -
$newdata: the supplied covariate patterns. -
$conf_level,$ci_method,$engine,$estimator,$method.
References
Borgan O, Liestøl K (1990). A note on confidence intervals and bands for the survival function based on transformations. Scandinavian Journal of Statistics 17(1):35-41.
Borgan O, Langholz B, Samuelsen SO, Goldstein L, Pogoda J (2000). Exposure stratified case-cohort designs. Lifetime Data Analysis 6(1):39-58.
Kang S, Lu W, Liu M (2017). Efficient estimation for accelerated failure time model under case-cohort and nested case-control sampling. Biometrics 73(1):114-123.
See Also
matcha(), contrast(), case_cohort(), sample_ncc()
Other contrasts: contrast(), excess_risk(), print.matchatr_absolute_risk(), print.matchatr_excess_risk(), tidy.matchatr_absolute_risk(), tidy.matchatr_excess_risk()