0. Creating a synthetic dataset
Charlotte Boys
2023-06-13
Last updated: 2023-06-16
Checks: 7 0
Knit directory: eGFRslopes/
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Introduction
To demonstrate the inference of eGFR slopes with a joint modelling approach using open-source R packages, we first create a synthetic dataset. We simulate a patient cohort consisting of 100 patients sampled from two disease groups “Disease A” and “Disease B”.
The support functions which we use to simulate our data can be found
in the script code/syntheticData.R.
Load necessary libraries:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── 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 errorsLoad support functions:
source("code/syntheticData.R")Simulate the dataset
Simulate patient metadata for our synthetic cohort:
patient_metadata <- sim_cohort_metadata(n_patients = 100)knitr::kable(head(patient_metadata))| patient_id | sex | disease | age_at_biopsy |
|---|---|---|---|
| 1 | M | B | 29 |
| 2 | M | A | 79 |
| 3 | M | B | 35 |
| 4 | F | B | 60 |
| 5 | M | A | 43 |
| 6 | F | B | 80 |
Simulate longitudinal data:
longitudinal_data <- sim_cohort_longitudinal_data(n_patients = 100,
patient_metadata = patient_metadata)knitr::kable(head(longitudinal_data))| patient_id | years_from_biopsy | measurement | type | units | days_from_biopsy |
|---|---|---|---|---|---|
| 1 | 0.0054757 | 107.02514 | SCr | umol/L | 2 |
| 1 | 0.0191650 | 107.14072 | SCr | umol/L | 7 |
| 1 | 0.0273785 | 101.41421 | SCr | umol/L | 10 |
| 1 | 0.0301164 | 106.18664 | SCr | umol/L | 11 |
| 1 | 0.7091034 | 95.95474 | SCr | umol/L | 259 |
| 1 | 0.9637235 | 95.74575 | SCr | umol/L | 352 |
The simulated longitudinal data currently contains two types of measurements:
unique(longitudinal_data$type)[1] "SCr" "Dialysis"We have a range of follow-up times. Some dropout is random, due to time of biopsy relative to the end of follow-up, and we also have dropout not-at-random due to dialysis.
endpoint_date <- longitudinal_data %>%
dplyr::filter(type == "Dialysis") %>%
dplyr::transmute(patient_id = patient_id, endpoint_years = years_from_biopsy)
to_plot <- longitudinal_data %>%
dplyr::full_join(endpoint_date, by = "patient_id") %>%
dplyr::group_by(patient_id) %>%
dplyr::summarise(start = 0,
end = max(years_from_biopsy),
endpoint = min(endpoint_years))
to_plot %>%
dplyr::mutate(patient_id = forcats::fct_reorder(patient_id, end, .desc = T)) %>%
ggplot() +
geom_segment(aes(x=start,
xend=end,
y=patient_id,
yend=patient_id),
linewidth=1, lineend = "round") +
geom_point(aes(x = endpoint,
y = patient_id, color = "Dialysis" ),
na.rm=TRUE) +
labs(x = "Follow-up time", y = "Patient", color = "Endpoint reached") +
theme(axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
legend.position = c(0.85, 0.93))
Plot longitudinal SCr measurements for the first eight patients:
longitudinal_data %>%
dplyr::filter(patient_id %in% 1:8) %>%
ggplot(aes(x = years_from_biopsy,
y = measurement)) +
geom_point(size = 1) +
xlim(0, 20) +
facet_wrap(vars(patient_id), nrow = 2)
Save patient metadata and longitudinal data:
write.csv(patient_metadata, "data/simulated_metadata.csv", row.names = FALSE)
write.csv(longitudinal_data, "data/simulated_longitudinal_data.csv", row.names = FALSE)
sessionInfo()R version 4.3.0 (2023-04-21)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: Europe/Rome
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
[5] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0 tibble_3.2.1
[9] ggplot2_3.4.2 tidyverse_2.0.0 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] sass_0.4.6 utf8_1.2.3 generics_0.1.3 stringi_1.7.12
[5] hms_1.1.3 digest_0.6.31 magrittr_2.0.3 timechange_0.2.0
[9] evaluate_0.21 grid_4.3.0 fastmap_1.1.1 rprojroot_2.0.3
[13] jsonlite_1.8.5 processx_3.8.1 whisker_0.4.1 ps_1.7.5
[17] promises_1.2.0.1 httr_1.4.6 fansi_1.0.4 scales_1.2.1
[21] jquerylib_0.1.4 cli_3.6.1 rlang_1.1.1 munsell_0.5.0
[25] withr_2.5.0 cachem_1.0.8 yaml_2.3.7 tools_4.3.0
[29] tzdb_0.4.0 colorspace_2.1-0 httpuv_1.6.11 vctrs_0.6.2
[33] R6_2.5.1 lifecycle_1.0.3 git2r_0.32.0 fs_1.6.2
[37] pkgconfig_2.0.3 callr_3.7.3 pillar_1.9.0 bslib_0.4.2
[41] later_1.3.1 gtable_0.3.3 glue_1.6.2 Rcpp_1.0.10
[45] highr_0.10 xfun_0.39 tidyselect_1.2.0 rstudioapi_0.14
[49] knitr_1.43 farver_2.1.1 htmltools_0.5.5 labeling_0.4.2
[53] rmarkdown_2.22 compiler_4.3.0 getPass_0.2-2