Last updated: 2022-11-30

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Knit directory: ampel-leipzig-meld/

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Rmd e1aebf7 Sebastian Gibb 2022-11-30 feat: add MELD 3.0 and adapt to reviewer’s suggestions
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Rmd 95c0de8 Sebastian Gibb 2022-11-27 refactor: change supplemental figure 2.1 according to reviewer’s suggestions
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Rmd 849ca09 Sebastian Gibb 2022-08-09 refactor: add additional co-author
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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
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html 65d58c4 Sebastian Gibb 2022-07-18 chore: rebuild site
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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
)
Survival plot. Kaplan-Meier survival estimate for the first 90 days of the analyzed population.

(#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

Benchmark results of machine-learning algorithms.

Figure 2.1: Benchmark results of machine-learning algorithms.

Version Author Date
83e3c7a Sebastian Gibb 2022-07-19
65d58c4 Sebastian Gibb 2022-07-18
ebe29cf Sebastian Gibb 2022-06-16
8035219 Sebastian Gibb 2022-06-15
Table 2.1: Benchmark results. Ranking of the 13 tested 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

Table 3.1: Observed vs. MELD-Na-expected 90-day mortality. MELD-Na mortality values are taken from VanDerwerken et al. (2021) and the mean value was calculated for each MELD-Na category.All patients censored before day 90 are ignored for the calculation of the MELD-Na-expected deaths. SMR, Standardized mortality ratio = observed deaths/expected deaths.
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 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 4.1: 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).

Version Author Date
83e3c7a Sebastian Gibb 2022-07-19
65d58c4 Sebastian Gibb 2022-07-18
ebe29cf Sebastian Gibb 2022-06-16
8035219 Sebastian Gibb 2022-06-15
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 4.2: 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.

5 Variable importance

Variable importance by frequency of bootstrap selections.

Figure 5.1: Variable importance by frequency of bootstrap selections.

Version Author Date
83e3c7a Sebastian Gibb 2022-07-19
65d58c4 Sebastian Gibb 2022-07-18
ebe29cf Sebastian Gibb 2022-06-16
8035219 Sebastian Gibb 2022-06-15
Variable importance by logrank in random forest.

Figure 5.2: Variable importance by logrank in random forest.

Version Author Date
83e3c7a Sebastian Gibb 2022-07-19
65d58c4 Sebastian Gibb 2022-07-18
ebe29cf Sebastian Gibb 2022-06-16
8035219 Sebastian Gibb 2022-06-15

6 References

Blanche, P., J.-F. Dartigues, and H. Jacqmin-Gadda. 2013. “Estimating and Comparing Time-Dependent Areas Under Receiver Operating Characteristic Curves for Censored Event Times with Competing Risks.” Stat Med 32 (30): 5381–97. https://doi.org/10.1002/sim.5958.
VanDerwerken, D. N., N. L. Wood, D. L. Segev, and S. E. Gentry. 2021. “The Precise Relationship Between Model for End-Stage Liver Disease and Survival Without a Liver Transplant.” Hepatology 74 (2): 950–60. https://doi.org/10.1002/hep.31781.

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_0.0.30      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          

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