Survival-probability predictions for exposure-response TTE models

Usage

ertte_predict(object, newdata = NULL, time, conf_level = 0.95)

Arguments

object: An ertte model, as returned by ertte_model()
newdata: Data frame containing cases to be predicted. Defaults to the data the model was fitted to.
time: Numeric vector of times at which to compute survival probabilities
conf_level: Confidence level for the intervals

Value

A tibble with one row per combination of newdata row and time

Details

Computes the linear predictor (and its standard error) via predict(object, newdata, type = "linear", se.fit = TRUE), then converts to a survival probability S(t) = 1 - F((log(t) - mu) / scale), where F is the base distribution's CDF implied by object's dist (see ertte_model() Details). Confidence intervals are Wald intervals on mu (a qnorm() z-score times the standard error), back-transformed the same way – parameter uncertainty in scale is not propagated, matching the level of approximation used throughout this package (e.g. erglm_predict()'s equivalent in the companion erglm package). conf_level must be a single number between 0 and 1 (inclusive); other values error rather than silently producing a reversed or NaN interval.

Examples

mod <- ertte_model(survival::Surv(time, event) ~ aucss, ertte_data)
ertte_predict(mod, ertte_data[1:5, ], time = c(30, 60, 90))
#> # A tibble: 15 × 13
#>       id sex      age weight  dose treatment aucss cmaxss  time event
#>    <int> <fct>  <int>  <dbl> <dbl> <fct>     <dbl>  <dbl> <dbl> <dbl>
#>  1     1 Female    27     70   200 Drug      1114.  187.     30     1
#>  2     1 Female    27     70   200 Drug      1114.  187.     60     1
#>  3     1 Female    27     70   200 Drug      1114.  187.     90     1
#>  4     2 Female    27     59   100 Drug       561.   49.1    30     0
#>  5     2 Female    27     59   100 Drug       561.   49.1    60     0
#>  6     2 Female    27     59   100 Drug       561.   49.1    90     0
#>  7     3 Female    24     65     0 Placebo      0     0      30     0
#>  8     3 Female    24     65     0 Placebo      0     0      60     0
#>  9     3 Female    24     65     0 Placebo      0     0      90     0
#> 10     4 Female    29     63     0 Placebo      0     0      30     0
#> 11     4 Female    29     63     0 Placebo      0     0      60     0
#> 12     4 Female    29     63     0 Placebo      0     0      90     0
#> 13     5 Male      27     91   200 Drug      1416.  143.     30     1
#> 14     5 Male      27     91   200 Drug      1416.  143.     60     1
#> 15     5 Male      27     91   200 Drug      1416.  143.     90     1
#> # ℹ 3 more variables: fit_survival <dbl>, ci_lower <dbl>, ci_upper <dbl>