A new machine-learning-based prediction of survival in patients with end-stage liver disease
Supplement
Sebastian Gibb1
Thomas Berg
Adam Herber
Berend Isermann
Thorsten Kaiser
Last updated: 2022-12-05
Checks: 7 0
Knit directory:
ampel-leipzig-meld/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | be55dc7 | Sebastian Gibb | 2022-12-05 | feat: add confidence bands to roc plots |
| html | 88f7b9f | Sebastian Gibb | 2022-12-01 | chore: rebuild site |
| Rmd | b4631f6 | Sebastian Gibb | 2022-12-01 | fix: typos |
| html | 5235082 | Sebastian Gibb | 2022-11-30 | chore: rebuild site |
| Rmd | e1aebf7 | Sebastian Gibb | 2022-11-30 | feat: add MELD 3.0 and adapt to reviewer’s suggestions |
| Rmd | fba81f5 | Sebastian Gibb | 2022-11-27 | feat: add MELD 3.0 score |
| Rmd | 95c0de8 | Sebastian Gibb | 2022-11-27 | refactor: change supplemental figure 2.1 according to reviewer’s suggestions |
| html | 1821ea1 | Sebastian Gibb | 2022-08-10 | chore: rebuild site |
| Rmd | 6315aaa | Sebastian Gibb | 2022-08-10 | refactor: drop DS from author list |
| html | 0d43824 | Sebastian Gibb | 2022-08-09 | chore: rebuild site |
| Rmd | bbdb3a2 | Sebastian Gibb | 2022-08-09 | fix: add dash between machine-learning |
| html | 1403bda | Sebastian Gibb | 2022-08-09 | chore: rebuild site |
| Rmd | 849ca09 | Sebastian Gibb | 2022-08-09 | refactor: add additional co-author |
| Rmd | d9f15ae | Sebastian Gibb | 2022-07-23 | fix: typo |
| html | 68ef9aa | Sebastian Gibb | 2022-07-22 | chore: rebuild site |
| Rmd | e6cf8a2 | Sebastian Gibb | 2022-07-22 | refactor: apply proofreading suggestions |
| html | 83e3c7a | Sebastian Gibb | 2022-07-19 | chore: rebuild site |
| Rmd | 8939c2c | Sebastian Gibb | 2022-07-19 | feat: extend supplement with survival plot |
| Rmd | 2155e58 | Sebastian Gibb | 2022-07-19 | refactor: create png and pdf just for non-html output |
| html | 65d58c4 | Sebastian Gibb | 2022-07-18 | chore: rebuild site |
| Rmd | a0f1d2e | Sebastian Gibb | 2022-07-18 | feat: generate png and pdfs for each plot |
| Rmd | 49d8c41 | Sebastian Gibb | 2022-07-18 | chore: suppress code output in supplement |
| Rmd | 9d4a890 | Sebastian Gibb | 2022-07-18 | feat: use 1200 dpi as required by labm for word output |
| html | fb43d01 | Sebastian Gibb | 2022-06-19 | chore: rebuild site |
| html | ebe29cf | Sebastian Gibb | 2022-06-16 | chore: rebuild site |
| Rmd | 2c8fd1a | Sebastian Gibb | 2022-06-15 | refactor: use short journal names |
| html | 8035219 | Sebastian Gibb | 2022-06-15 | chore: rebuild site |
| Rmd | 19d9fed | Sebastian Gibb | 2022-06-15 | feat: further work on the manuscript |
| html | d3e9462 | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| html | b20484a | Sebastian Gibb | 2022-06-06 | chore: rebuild site |
| Rmd | 01f934b | Sebastian Gibb | 2022-06-06 | feat: add first discussion draft |
| html | 983ec69 | Sebastian Gibb | 2022-03-17 | chore: rebuild site |
| html | 373e7d8 | Sebastian Gibb | 2021-10-20 | chore: rebuild site |
| html | df8964f | Sebastian Gibb | 2021-10-15 | chore: rebuild site |
| html | c66640c | Sebastian Gibb | 2021-09-14 | feat: first nnet tests |
| html | afa48d9 | Sebastian Gibb | 2021-08-07 | chore: rebuild site |
| Rmd | 3957af7 | Sebastian Gibb | 2021-08-02 | refactor: move common yaml headers into _site.yml |
| html | 3aab3e1 | Sebastian Gibb | 2021-08-01 | chore: rebuild site |
| Rmd | 91bfbd2 | Sebastian Gibb | 2021-08-01 | feat: add first article draft with pandoc filters |
1 Survival plot
tar_load(zlog_data)
srv <- Surv(zlog_data$DaysAtRisk, zlog_data$Deceased)
col <- palette.colors(nlevels(zlog_data$MeldCategory))
srvfit <- survfit(srv ~ 1)
times <- c(2, 7, 14, 30, 90)
## calculate risk tables
sm <- summary(srvfit, times = times)
nrisk <- as.matrix(sm$n.risk)
ncumevents <- as.matrix(cumsum(sm$n.event))
rownames(nrisk) <- rownames(ncumevents) <- times
## keep old graphic parameters and restore them afterwards
old.par <- par(no.readonly = TRUE)
layout(matrix(1:3, nrow = 3), height = c(5, 1, 1))
plot_surv(
srvfit,
main = "Kaplan-Meier survival estimate to day 90",
times = times,
xmax = 90,
col = col[2],
cex = 1.2
)
par(mar = c(1.1, 5.1, 1.1, 2.1))
plot_table(
nrisk, at = times, main = "Number at risk",
xaxis = FALSE, cex.text = 1, ylabels = FALSE
)
par(mar = c(1.1, 5.1, 1.1, 2.1))
plot_table(
ncumevents, at = times, main = "Cumulative number of events",
xaxis = FALSE, cex.text = 1, ylabels = FALSE
)
(#fig:survival.plot)Survival plot. Kaplan-Meier survival estimate for the first 90 days of the analyzed population.
| Version | Author | Date |
|---|---|---|
| 83e3c7a | Sebastian Gibb | 2022-07-19 |
par(old.par)2 Benchmark results of machine-learning algorithms
Figure 2.1: Benchmark results of machine-learning algorithms.
| Model | Harrell’s concordance index |
|---|---|
| ridge | 0.915 |
| elasticnet | 0.913 |
| ranger | 0.913 |
| penlasso | 0.911 |
| penridge | 0.909 |
| lasso | 0.909 |
| rfsrc | 0.906 |
| cox | 0.872 |
| deepsurv | 0.833 |
| xgboost | 0.828 |
| svmvanbelle | 0.823 |
| coxtime | 0.819 |
| svmregression | 0.809 |
3 Observed versus MELD-Na-expected 90-day mortality
| MELD category | Observed deaths (n) | Expected deaths (n) | Standardized mortality ratio (SMR) | Observed mortality (%) | Expected mortality (%) |
|---|---|---|---|---|---|
| [6,9] | 1 | 5.8 | 0.2 | 0.5 | 2.8 |
| [10,15) | 4 | 2.9 | 1.4 | 3.0 | 2.3 |
| [15,20) | 5 | 3.1 | 1.6 | 7.5 | 5.1 |
| [20,25) | 15 | 5.7 | 2.6 | 27.1 | 13.0 |
| [25,30) | 22 | 10.2 | 2.1 | 62.5 | 31.1 |
| [30,35) | 14 | 8.1 | 1.7 | 88.0 | 54.2 |
| [35,40) | 9 | 7.5 | 1.2 | 90.0 | 75.1 |
| [40,52) | 6 | 5.1 | 1.2 | 100.0 | 84.4 |
4 ROC curves
![Receiver operating characteristic (ROC) curve. Area under the time-dependent ROC curve (AUC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, MELD-Plus7, as described in [@blanche2013]. The dashed lines depict the corresponding 95% confidence bands calculated by threshold averaging as described in [@fawcett2004].](figure/supplement.Rmd/roc-cb-all-1.png)
Figure 4.1: Receiver operating characteristic (ROC) curve. Area under the time-dependent ROC curve (AUC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, MELD-Plus7, as described in (Blanche, Dartigues, and Jacqmin-Gadda 2013). The dashed lines depict the corresponding 95% confidence bands calculated by threshold averaging as described in (Fawcett 2004).
![Receiver operating characteristic (ROC) curve. Area under the time-dependent ROC curve (AUC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, MELD-Plus7, as described in [@blanche2013]. The dashed lines depict the corresponding 95% confidence bands for AMELD calculated by threshold averaging as described in [@fawcett2004]. The other confidence bands are hidden for easier readability.](figure/supplement.Rmd/roc-cb-ameld-1.png)
Figure 4.2: Receiver operating characteristic (ROC) curve. Area under the time-dependent ROC curve (AUC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, MELD-Plus7, as described in (Blanche, Dartigues, and Jacqmin-Gadda 2013). The dashed lines depict the corresponding 95% confidence bands for AMELD calculated by threshold averaging as described in (Fawcett 2004). The other confidence bands are hidden for easier readability.
5 AUROC trend
![Trend in the area under the time-dependent receiver operating characteristic curve (AUROC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, and MELD-Plus7, as described in [@blanche2013].](figure/supplement.Rmd/roc-plot-1.png)
Figure 5.1: Trend in the area under the time-dependent receiver operating characteristic curve (AUROC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, and MELD-Plus7, as described in (Blanche, Dartigues, and Jacqmin-Gadda 2013).
![Trend in the area under the time-dependent receiver operating characteristic curve (AUROC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, and MELD-Plus7, as described in [@blanche2013]. Identical to the figure above but without confidence bands.](figure/supplement.Rmd/roc-plot-wo-cb-1.png)
Figure 5.2: Trend in the area under the time-dependent receiver operating characteristic curve (AUROC) based on the nonparametric inverse probability of censoring weighting estimate (IPCW) for AMELD, MELD, MELD-Na, MELD 3.0, and MELD-Plus7, as described in (Blanche, Dartigues, and Jacqmin-Gadda 2013). Identical to the figure above but without confidence bands.
| Version | Author | Date |
|---|---|---|
| 5235082 | Sebastian Gibb | 2022-11-30 |
6 Variable importance
Figure 6.1: Variable importance by frequency of bootstrap selections.
Figure 6.2: Variable importance by logrank in random forest.
7 References
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-unknown-linux-gnu (64-bit)
Matrix products: default
BLAS/LAPACK: /gnu/store/ras6dprsw3wm3swk23jjp8ww5dwxj333-openblas-0.3.18/lib/libopenblasp-r0.3.18.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggplot2_3.3.6 viridis_0.6.2 viridisLite_0.4.0 mlr3viz_0.5.9
[5] mlr3proba_0.4.11 mlr3misc_0.10.0 mlr3_0.13.3 targets_0.12.1
[9] data.table_1.14.2 ameld_0.0.31 survival_3.3-1 glmnet_4.1-4
[13] Matrix_1.4-1
loaded via a namespace (and not attached):
[1] fs_1.5.2 bbotk_0.5.3 rprojroot_2.0.3
[4] numDeriv_2016.8-1.1 mlr3pipelines_0.4.1 tools_4.2.0
[7] backports_1.4.1 bslib_0.3.1 utf8_1.2.2
[10] R6_2.5.1 colorspace_2.0-3 withr_2.5.0
[13] tidyselect_1.1.2 gridExtra_2.3 processx_3.5.3
[16] compiler_4.2.0 git2r_0.30.1 cli_3.3.0
[19] ooplah_0.2.0 lgr_0.4.3 timeROC_0.4
[22] labeling_0.4.2 bookdown_0.26 sass_0.4.1
[25] scales_1.2.0 checkmate_2.1.0 mvtnorm_1.1-3
[28] pec_2022.05.04 callr_3.7.0 palmerpenguins_0.1.0
[31] mlr3tuning_0.13.1 stringr_1.4.0 digest_0.6.29
[34] mlr3extralearners_0.5.37 rmarkdown_2.14 param6_0.2.4
[37] paradox_0.9.0 set6_0.2.4 pkgconfig_2.0.3
[40] htmltools_0.5.2 parallelly_1.31.1 fastmap_1.1.0
[43] highr_0.9 rlang_1.0.2 shape_1.4.6
[46] jquerylib_0.1.4 generics_0.1.2 farver_2.1.0
[49] jsonlite_1.8.0 dplyr_1.0.9 magrittr_2.0.3
[52] Rcpp_1.0.8.3 munsell_0.5.0 fansi_1.0.3
[55] lifecycle_1.0.1 stringi_1.7.6 whisker_0.4
[58] yaml_2.3.5 grid_4.2.0 parallel_4.2.0
[61] dictionar6_0.1.3 listenv_0.8.0 promises_1.2.0.1
[64] crayon_1.5.1 lattice_0.20-45 splines_4.2.0
[67] timereg_2.0.2 knitr_1.39 ps_1.7.0
[70] pillar_1.7.0 igraph_1.3.1 uuid_1.1-0
[73] base64url_1.4 future.apply_1.9.0 codetools_0.2-18
[76] glue_1.6.2 evaluate_0.15 vctrs_0.4.1
[79] httpuv_1.6.5 foreach_1.5.2 distr6_1.6.9
[82] gtable_0.3.0 purrr_0.3.4 future_1.26.1
[85] xfun_0.31 prodlim_2019.11.13 later_1.3.0
[88] tibble_3.1.7 iterators_1.0.14 lava_1.6.10
[91] workflowr_1.7.0 globals_0.15.0 ellipsis_0.3.2