Cross-validation with the landmaRk package

Setup

In addition to the landmaRk package, we will also use tidyverse.

set.seed(123)
library(landmaRk)
#> Loading required package: survival
library(tidyverse)
#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
#> ✔ dplyr     1.1.4     ✔ readr     2.1.5
#> ✔ forcats   1.0.1     ✔ stringr   1.6.0
#> ✔ ggplot2   4.0.2     ✔ tibble    3.3.0
#> ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
#> ✔ purrr     1.2.0
#> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag()    masks stats::lag()
#> ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(survival)
library(prodlim)

Example: `aids` data

As in the first vignette, we use the epileptic dataset to perform landmarking analysis of time-to-event data with time-varying covariates. Here is the structure of the dataset.

library(JMbayes2)
#> Loading required package: nlme
#> 
#> Attaching package: 'nlme'
#> The following object is masked from 'package:dplyr':
#> 
#>     collapse
#> Loading required package: GLMMadaptive
#> Loading required package: splines
data("aids")
aids$patient <- as.numeric(aids$patient)
str(aids)
#> 'data.frame':    1405 obs. of  12 variables:
#>  $ patient: num  1 1 1 2 2 2 2 3 3 3 ...
#>  $ Time   : num  17 17 17 19 19 ...
#>  $ death  : int  0 0 0 0 0 0 0 1 1 1 ...
#>  $ CD4    : num  10.68 8.43 9.43 6.32 8.12 ...
#>  $ obstime: int  0 6 12 0 6 12 18 0 2 6 ...
#>  $ drug   : Factor w/ 2 levels "ddC","ddI": 1 1 1 2 2 2 2 2 2 2 ...
#>  $ gender : Factor w/ 2 levels "female","male": 2 2 2 2 2 2 2 1 1 1 ...
#>  $ prevOI : Factor w/ 2 levels "noAIDS","AIDS": 2 2 2 1 1 1 1 2 2 2 ...
#>  $ AZT    : Factor w/ 2 levels "intolerance",..: 1 1 1 1 1 1 1 1 1 1 ...
#>  $ start  : int  0 6 12 0 6 12 18 0 2 6 ...
#>  $ stop   : num  6 12 17 6 12 ...
#>  $ event  : num  0 0 0 0 0 0 0 0 0 1 ...

Initialising the landmarking analysis for cross-validation

As in the introductory vignette, the dataset epileptic comes in wide format. We split it into two dataframes, one for static covariates and one for dynamic covariates.

# DF with Static covariates
aids_dfs <- split_wide_df(
  aids,
  ids = "patient",
  times = "obstime",
  static = c("Time", "death", "drug", "gender", "prevOI"),
  dynamic = c("CD4"),
  measurement_name = "value"
)
static <- aids_dfs$df_static
head(static)
#>    patient  Time death drug gender prevOI
#> 1        1 16.97     0  ddC   male   AIDS
#> 4        2 19.00     0  ddI   male noAIDS
#> 8        3 18.53     1  ddI female   AIDS
#> 11       4 12.70     0  ddC   male   AIDS
#> 15       5 15.13     0  ddI   male   AIDS
#> 19       6  1.90     1  ddC female   AIDS

# DF with Dynamic covariates
dynamic <- aids_dfs$df_dynamic
head(dynamic[["CD4"]])
#>   patient obstime     value
#> 1       1       0 10.677078
#> 2       1       6  8.426150
#> 3       1      12  9.433981
#> 4       2       0  6.324555
#> 5       2       6  8.124038
#> 6       2      12  4.582576

As in the introductory vignette, we create an object of class LandmarkAnalysis, using the helper function of the same name. We now use the optional argument K to specify the number of cross-validations folds. In this example, we request five cross validation folds.

We then calculate the risk sets using compute_risk_sets().

landmarking_object <- LandmarkAnalysis(
  data_static = static,
  data_dynamic = dynamic,
  event_indicator = "death",
  ids = "patient",
  event_time = "Time",
  times = "obstime",
  measurements = "value",
  K = 5
)

landmarking_object <- landmarking_object |>
  compute_risk_sets(landmarks = c(6, 8))

Performance evaluation in hold-out dataset

Now that we have split the dataset into K=5 parts for cross-validation, we can use one of the five parts as test set and the remaining four parts as the training set. To do so, use the argument validation_fold to indicate the that you want to use as test set when calling fit_longitudinal(), predict_longitudinal(), fit_survival() and predict_survival().

landmarking_object <- landmarking_object |>
  fit_longitudinal(
    landmarks = c(6, 8),
    method = "lme4",
    formula = value ~ prevOI + obstime + (obstime | patient),
    dynamic_covariates = c("CD4"),
    validation_fold = 5
  ) |>
  predict_longitudinal(
    landmarks = c(6, 8),
    method = "lme4",
    dynamic_covariates = c("CD4"),
    validation_fold = 5,
  ) |>
  fit_survival(
    formula = Surv(event_time, event_status) ~ drug,
    landmarks = c(6, 8),
    horizons = 12 + c(6, 8),
    method = "coxph",
    dynamic_covariates = c("CD4"),
    validation_fold = 5
  ) |>
  predict_survival(
    landmarks = c(6, 8),
    horizons = 12 + c(6, 8),
    validation_fold = 5
  )

We can also use summary() to print parameter estimates. Note that this time the sample size is smaller, because we have held out part of the original dataset for validation.

summary(landmarking_object,
        type = "longitudinal",
        landmark = 6,
        dynamic_covariate = "CD4")
#> Linear mixed model fit by REML ['lmerMod']
#> Formula: value ~ prevOI + obstime + (obstime | patient)
#>    Data: dataframe
#> REML criterion at convergence: 4254.095
#> Random effects:
#>  Groups   Name        Std.Dev. Corr 
#>  patient  (Intercept) 4.1594        
#>           obstime     0.2385   -0.09
#>  Residual             1.6248        
#> Number of obs: 851, groups:  patient, 320
#> Fixed Effects:
#> (Intercept)   prevOIAIDS      obstime  
#>      10.321       -4.311       -0.176

summary(landmarking_object, type = "survival", landmark = 6, horizon = 18)
#> Call:
#> survival::coxph(formula = formula, data = x@survival_datasets[[paste0(landmarks, 
#>     "-", horizons)]], model = TRUE, x = TRUE)
#> 
#>           coef exp(coef) se(coef)     z     p
#> drugddI 0.2306    1.2594   0.2019 1.142 0.253
#> 
#> Likelihood ratio test=1.31  on 1 df, p=0.2522
#> n= 320, number of events= 99

Here are the in-sample performance metrics:

performance_metrics(
  landmarking_object,
  landmarks = c(6, 8),
  horizons = c(18, 20),
  auc_t = TRUE, c_index = FALSE,
  h_times = c(3, 6, 12)
)
#>      landmark horizon   Brier(9) Brier(12) Brier(18)    AUC(9)   AUC(12)
#> 6-18        6      18 0.07725955 0.1614473 0.2309560 0.5293336 0.5424304
#> 8-20        8      20 0.10304433 0.1683295 0.2449509 0.5118598 0.5396523
#>        AUC(18)
#> 6-18 0.4876483
#> 8-20 0.4661287

Out-of-sample performance metrics can be obtained by specifying train = FALSE:

performance_metrics(
  landmarking_object,
  landmarks = c(6, 8),
  horizons = c(18, 20),
  auc_t = TRUE, c_index = FALSE,
  h_times = c(3, 6, 12),
  train = FALSE
)
#>      landmark horizon   Brier(9) Brier(12) Brier(18)    AUC(9)   AUC(12)
#> 6-18        6      18 0.07650806 0.1743734 0.2385928 0.6400376 0.6126645
#> 8-20        8      20 0.08163087 0.1728349 0.2370645 0.6458753 0.6614730
#>        AUC(18)
#> 6-18 0.5646015
#> 8-20 0.4347708