Parametric survival models for a signature's risk score

survival_parametric() scores each sample of a SummarizedExperiment with the signature's joint Cox coefficients and fits a set of parametric accelerated-failure-time models of the event time on that score, using survreg. It returns an AIC comparison across the fitted distributions, the fitted survival curves (evaluated at the mean risk score) for overlay on the Kaplan-Meier estimate, and a plot() method that draws the KM curve together with the parametric curves.

survival_parametric(
  sig,
  se,
  dists = c("weibull", "exponential", "lognormal", "loglogistic")
)

Arguments

sig

An object of class "rnaSentry_signature" as returned by build_signature.

se

A SummarizedExperiment containing the signature genes in its rows and the survival metadata columns recorded in sig (sig$time_col and sig$event_col). Expression is taken from the same analysis assay used at signature-build time (see build_signature).

dists

Character vector of parametric distributions to fit, any subset of "weibull", "exponential", "lognormal" and "loglogistic". Defaults to all four.

Value

An object of class "rnaSentry_parametric" (a list) with elements:

genes

The signature genes scored.

score

Per-sample risk score (linear predictor).

fits

Named list of survreg fits, one per distribution.

table

Data.frame comparing distributions on loglik, npar, AIC, delta_AIC, weight and best, ordered by increasing AIC.

best

Name of the lowest-AIC distribution.

km_fit

The survfit object of the whole cohort.

curves

Named list of fitted survival curves (data.frame with time and survival), evaluated at the mean risk score.

sig_locked

Whether the input signature was locked.

created

Provenance timestamp.

flags

Data.frame of issues raised (e.g. a model that failed to fit, or near-tied AIC values), with columns check, severity, detail and stage.

Details

The stage is read-only: it works on both locked and unlocked signatures and records the signature's lock state in its output.

Examples

library(SummarizedExperiment)
set.seed(9)
counts <- matrix(rpois(450, lambda = 500), nrow = 30, ncol = 15,
                 dimnames = list(paste0("gene", 1:30), paste0("S", 1:15)))
sig_expr <- colMeans(counts[1:4, , drop = FALSE])
risk <- scale(sig_expr)[, 1] * 0.4
event_time <- rexp(15, rate = 0.03 * exp(0.8 * risk))
censor_time <- rexp(15, rate = 0.02)
time <- pmin(event_time, censor_time)
event <- as.integer(event_time < censor_time)
coldata <- S4Vectors::DataFrame(time = time, event = event,
                                 row.names = colnames(counts))
se <- SummarizedExperiment(assays = list(counts = counts), colData = coldata)
sig <- build_signature(se, "time", "event", top_n = 4, repeats = 1,
                       folds = 2, seed = 1)
#> Warning: Only 6 event(s) for a 3-gene signature (2.0 events per parameter), below 'min_events_per_parameter' = 5. Cross-validated concordance and hazard ratios are unstable at this event count; consider fewer genes or a larger cohort.
sp <- survival_parametric(sig, se)
sp
#> rnaSentry parametric models: 3-gene signature, 15 samples.
#> AIC comparison:
#>   weibull      AIC    44.18  delta   0.00  weight 0.733 *
#>   loglogistic  AIC    46.96  delta   2.79  weight 0.182
#>   lognormal    AIC    48.70  delta   4.53  weight 0.076
#>   exponential  AIC    53.06  delta   8.89  weight 0.009
#> Best model: weibull.
#> Signature not locked.
plot(sp)