Landmark event-probability predictions for exposure-response TTE models

Reduces the survival curve for an exposure-response time-to-event model to a binary landmark event probability at a single fixed time.

Usage

ertte_landmark(object, newdata = NULL, landmark_time, conf_level = 0.95, ...)

Arguments

object: An ertte model.
newdata: Data frame containing cases to be predicted. Defaults to the data the model was fitted to.
landmark_time: A single positive number.
conf_level: Confidence level for the intervals. Defaults to .95.
...: Passed to ertte_predict().

Value

A tibble with one row per row of newdata. In addition to storing the original columns from newdata, it contains a column storing the landmark_time itself, a fit_resp column with the estimated probability of observing the event at or before the landmark time, and ci_lower, and ci_upper columns specifying the confidence interval.

Details

In some time-to-event analyses it is convenient to reduce a time-to-event endpoint to a binary landmark response: for every row in the data, the landmark response describes the modelled probability that the event occurs at or before a specified landmark_time defined by the analyst. The landmark event probability is simply one minus the survival probability at that time, and is computed using ertte_predict(). However, unlike the typical usage of ertte_predict() in which a vector of times is passed, landmark_time must be a single fixed value – a landmark is by definition evaluated at one time.

Because the event probability is a decreasing transform of the survival probability (1 - survival), the confidence interval is built by swapping ertte_predict()'s bounds: its lower survival bound becomes the upper event-probability bound, and vice versa.

Examples

mod <- ertte_aft(Surv(time, event) ~ aucss, ertte_data)
ertte_landmark(mod, ertte_data[1:5, ], landmark_time = 180)
#> # A tibble: 5 × 14
#>      id sex      age weight  dose treatment aucss cmaxss event admin_censor
#>   <int> <fct>  <int>  <dbl> <dbl> <fct>     <dbl>  <dbl> <dbl>        <dbl>
#> 1     1 Female    27     70   200 Drug      1114.  187.      1          180
#> 2     2 Female    27     59   100 Drug       561.   49.1     0          180
#> 3     3 Female    24     65     0 Placebo      0     0       0          180
#> 4     4 Female    29     63     0 Placebo      0     0       0          180
#> 5     5 Male      27     91   200 Drug      1416.  143.      1          180
#> # ℹ 4 more variables: landmark_time <dbl>, fit_resp <dbl>, ci_lower <dbl>,
#> #   ci_upper <dbl>

mod_cox <- ertte_coxph(Surv(time, event) ~ aucss, ertte_data)
ertte_landmark(mod_cox, ertte_data[1:5, ], landmark_time = 180)
#> # A tibble: 5 × 14
#>      id sex      age weight  dose treatment aucss cmaxss event admin_censor
#>   <int> <fct>  <int>  <dbl> <dbl> <fct>     <dbl>  <dbl> <dbl>        <dbl>
#> 1     1 Female    27     70   200 Drug      1114.  187.      1          180
#> 2     2 Female    27     59   100 Drug       561.   49.1     0          180
#> 3     3 Female    24     65     0 Placebo      0     0       0          180
#> 4     4 Female    29     63     0 Placebo      0     0       0          180
#> 5     5 Male      27     91   200 Drug      1416.  143.      1          180
#> # ℹ 4 more variables: landmark_time <dbl>, fit_resp <dbl>, ci_lower <dbl>,
#> #   ci_upper <dbl>