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EBReport.qmd
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EBReport.qmd
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---
title: "EB GAIT"
subtitle: "Evidence Based Gait Analysis Interpretation Technique"
Author: ""
Date: ""
format:
html:
toc: true
toc-depth: 3
toc-location: left
toc-title: "Section"
output-file: "EBReport_MRN_YYYY-MM-DD.html"
fig-dpi: 600
embed-resources: true
theme:
- cosmo
grid:
body-width: 1050px
sidebar-width: 250px
margin-width: 0px
lightbox: true
knitr:
opts_chunk:
warning: false
message: false
cache.lazy: true
screenshot.force: true
fig.path: FIGURES/
html-table-processing: none
execute:
echo: false
warning: false
message: false
error: false
params:
# MRN: 606085
MRN: 485602
# Event_Date: "2018-06-21"
Event_Date: "2024-12-18"
deid: FALSE
usevideo: TRUE
usegait: TRUE
figsave: FALSE
custom_outcome: FALSE
editor_options:
chunk_output_type: console
---
```{r}
#| label: libraries-themes-etc
#| echo: false
#| message: false
#| warning: false
# Packages -----
options(java.parameters = "-Xmx32g")
library(bartMachine, verbose = F)
library(tidyverse, verbose = F)
library(lubridate, verbose = F)
library(patchwork, verbose = F)
library(ggtext, verbose = F)
library(RColorBrewer, verbose = F)
library(glue, verbose = F)
library(gt, verbose = F)
library(gtsummary, verbose = F)
library(ggpmisc, verbose = F)
library(MatchIt, verbose = F)
library(ggh4x, verbose = F)
library(extraDistr, verbose = F)
library(iml, verbose = F)
library(Hmisc, verbose = F)
library(colorspace, verbose = F)
library(MatchIt, verbose = F)
# User Functions -----
margin <- ggplot2::margin
summarize <- dplyr::summarize
select <- dplyr::select
formals(table)$useNA <- "ifany"
source("SCRIPTS/read_and_organize.R")
source("SCRIPTS/addnoise.R")
source("SCRIPTS/get_TD_NEW.R")
source("SCRIPTS/build_propensity_mod.R")
source("SCRIPTS/get_prop_model_vars.R")
source("SCRIPTS/prop_shap_plot.R")
source("SCRIPTS/prop_detail_plot.R")
source("SCRIPTS/pmatch_plot.R")
source("SCRIPTS/get_outcome_vars.R")
source("SCRIPTS/pmatch_data.R")
source("SCRIPTS/outcome_plots.R")
source("SCRIPTS/p_match_qual.R")
source("SCRIPTS/chkmatchlims.R")
source("SCRIPTS/test_pred_perf.R")
source("SCRIPTS/noisify_side.R")
source("SCRIPTS/getnoisevars.R")
source("SCRIPTS/prop_shap_table.R")
source("SCRIPTS/fmt_shap.R")
source("SCRIPTS/fmt_shapLR.R")
source("SCRIPTS/ptableLR.R")
source("SCRIPTS/getq.R")
source("SCRIPTS/fmt_chk.R")
source("SCRIPTS/get_varlabs.R")
source("SCRIPTS/plot_development.R")
source("SCRIPTS/get_meanscores.R")
source("SCRIPTS/pred_outcome_by_bart.R")
source("SCRIPTS/outcome_plots_BART.R")
source("SCRIPTS/get_pred_model_vars.R")
source("SCRIPTS/get_O2_Z.R")
source("SCRIPTS/get_outcome_thresh.R")
# Theme for ggplot2 -----
theme_mhs <- theme_minimal(base_size = 7)
theme_mhs <- function(bs){
theme_minimal(base_size = bs) %+replace%
theme(
panel.grid.minor = element_blank(),
panel.grid.major = element_line(linewidth = .1, color = "grey85")
)
}
theme_set(theme_mhs(7))
# List of surgeries -----
surglist <- c("Femoral_Derotation_Osteotomy", "Tibial_Derotation_Osteotomy", "Psoas_Release", "Hams_Lengthening", "Adductor_Release", "Gastroc_Soleus_Lengthening", "DFEO_Patellar_Advance", "Patellar_Advance", "Foot_and_Ankle_Bone", "Foot_and_Ankle_Soft_Tissue","Neural_Rhizotomy", "Rectus_Transfer")
# For trimming white space around figures -----
knitr::knit_hooks$set(crop = knitr::hook_pdfcrop)
```
```{r}
#| label: read-TD-make-labels
#| include: false
# Read and organize TD gait data -----
tdavg <- get_TD_NEW()
# Get labels (variable, and pos/neg directions) -----
list2env(get_varlabs(), envir = .GlobalEnv)
# Read outcome setup containing outcome choices for each surgery -----
if(params$custom_outcome){
outcome_setup <- readxl::read_xlsx("DATA/Outcome_Setup_Custom.xlsx", sheet = "Main")
} else {
outcome_setup <- readxl::read_xlsx("DATA/Outcome_Setup_Default.xlsx", sheet = "Main")
}
outcome_setup <-
outcome_setup %>%
mutate(
out1 = xlabs[out1],
out2 = xlabs[out2],
out3 = xlabs[out3],
out4 = xlabs[out4]
) %>%
pivot_longer(-surg)
```
```{r}
#| label: get-development-data
#| cache: false
# devdat <- readxl::read_xlsx("DATA/growth_curves.xlsx")
dd <- readxl::read_xlsx("DATA/development_curves.xlsx")
devdat <-
dd |>
pivot_longer(-c(vbl, sex, age), names_to = c("pct")) |>
mutate(name = vlabs[vbl])
```
```{r}
#| label: read-organize-data
#| include: false
# Get clinical data from RRFMP output -----
# Note that pre-processed data contains propensity scores...
if(file.exists("DATA/preprocessed_data_current.RDS")){
dat0 <- readRDS("DATA/FDpp_2024-05-25.RDS")
dat <- readRDS("DATA/preprocessed_data_current.RDS")
dat <- get_meanscores(dat, "Pre")
dat <- get_meanscores(dat, "Post")
dat <- get_O2_Z(dat)
} else {
dat0 <- readRDS("DATA/FDpp_2024-05-25.RDS")
dat <- read_and_organize(dat0)
dat <- get_meanscores(dat, "Pre")
dat <- get_meanscores(dat, "Post")
dat <- get_O2_Z(dat)
}
# Get all pre- data (used in GOAL table, etc...) -----
datpre <- readRDS("DATA/FD_2024-05-25.RDS")
datpre <- read_and_organize(datpre)
datpre <- get_meanscores(datpre, "Pre")
datpre <- get_O2_Z(datpre)
```
```{r}
#| label: load-or-build-propensity-models
#| include: false
# Loop over list -----
for (s in surglist) {
if(file.exists(glue("OUTPUT/Propensity_Models/prop_{s}.RDATA"))){
# Comment above ^^^ and Uncomment below vvv to re-run all propensity models
# if(FALSE){
load(glue("OUTPUT/Propensity_Models/prop_{s}.RDATA"))
assign(glue("mod_{s}"), slist[[1]])
assign(glue("opt_{s}"), slist[[2]])
load(glue("OUTPUT/Propensity_Models/prop_{s}_nogait.RDATA"))
assign(glue("mod_{s}_nogait"), slist[[1]])
assign(glue("opt_{s}_nogait"), slist[[2]])
} else {
# Set usegait to build with (TRUE) or without (FALSE) gait data -----
usegait = FALSE
surg <- glue("interval_{s}")
res <- build_propensity_mod(s, dat, fp2fn=1, usegait)
assign(glue("res_{s}"), res)
mod <- res$mod
opt <- res$opt
metrics <- res$metrics
slist <- list(mod, opt)
names(slist) <- c(glue("mod_{s}"), glue("opt_{s}"))
if(usegait){
fmod <- glue("OUTPUT/Propensity_Models/prop_{s}.RDATA")
} else {
fmod <- glue("OUTPUT/Propensity_Models/prop_{s}_nogait.RDATA")
}
save(slist, file = fmod)
}
}
```
```{r}
#| label: compute-all-propensities
#| cache: false
# Compute propensities -----
if(is.null(dat$p_Femoral_Derotation_Osteotomy)){
for (s in surglist) {
cat("Computing Propensity for Surgery ", s, "\n")
mod <- get(glue("mod_{s}"))
set.seed(42)
dat[[glue("p_{s}")]] <- predict(mod, dat[names(mod$X)])
mod <- get(glue("mod_{s}_nogait"))
set.seed(42)
dat[[glue("p_{s}_nogait")]] <- predict(mod, dat[names(mod$X)])
}
saveRDS(dat, glue("DATA/preprocessed_data_{as.Date(now())}.RDS"))
saveRDS(dat, glue("DATA/preprocessed_data_current.RDS"))
}
```
```{r}
#| label: read-patient-data
#| cache: false
source("SCRIPTS/read_c3d_one.R")
source("SCRIPTS/make_FD_one.R")
source("SCRIPTS/get_file_list.R")
library(DBI)
# Option to do analysis without gait data -----
# If usegait = TRUE but no gait data is present things still work
if(params$usegait){
# Read barefoot data (tt=17) and AFO data (tt=18) -----
datc3d <- read_c3d_one(params$MRN, tt=17)
datc3d_afo <- read_c3d_one(params$MRN, tt=18)
plotgait <- !is.null(datc3d)
} else {
datc3d <- NULL
datc3d_afo <- NULL
plotgait <- !is.null(datc3d)
}
# Build patient FD and FDpp data -----
# If there is ONLY afo data, use that -----
if(is.null(datc3d) & !is.null(datc3d_afo)){
datc3d <- datc3d_afo
afoonly <- TRUE
plotgait <- TRUE
}
fd <- make_FD_one(datc3d, params$MRN)
# Restrict to Current date and before (useful for processing old studies) -----
fd$FD <-
fd$FD %>%
filter(Event_Date <= params$Event_Date)
if(!is.null(fd$FDpp)){
fd$FDpp <-
fd$FDpp %>%
filter(Event_Date <= params$Event_Date)
}
# Add NA for missing variables - this is for limbs without gait data -----
if(params$usegait == FALSE){
vall <- names(datpre)
vFD <- names(fd$FD)
vadd <- vall[!(vall %in% vFD)]
fd$FD[vadd] <- NA
}
# Get visit of interest -----
xpt <-
fd$FD %>%
filter(
MRN == params$MRN,
year(Event_Date) == year(as.Date(params$Event_Date)),
month(Event_Date) == month(as.Date(params$Event_Date)),
day(Event_Date) == day(as.Date(params$Event_Date))
) |>
read_and_organize()
# Add average spa, sel, str, wb, nwb -----
xpt <- get_meanscores(xpt, "Pre")
# Format name and date -----
if(!params$deid){
ptname <- glue("{xpt$Child_First} {xpt$Child_Last}")[1]
ptdate <- format(as.Date(params$Event_Date), "%b %d, %Y")
ptmrn <- xpt$MRN[1]
} else {
ptname <- ifelse(xpt$Sex[1] == F, "Trillian Astra", "Arthur Dent")
ptdate <- format(as.Date(params$Event_Date), "%b %Y")
ptmrn <- xpt$Exam_ID[1]
}
```
## `r glue("{ptname}<br>{ptmrn}<br>{ptdate}")`
## Background
::: {#background .panel-tabset}
### Patient
```{r}
#| label: patient-summary
#| cache: false
if(!params$deid){
tit <- glue("{ptname} ({ptmrn}), Exam Date: {ptdate}")
} else {
tit <- glue("{ptname} ({ptmrn}), Exam Date: {ptdate}")
}
# Build patient summary data -----
tdat <-
xpt %>%
filter(SIDE == "L") |>
select(age, dx, dxmod, dxside, GMFCS_meas, FAQ) |>
mutate(age = round(age,1)) |>
rename_with(
.fn = ~ vlabs[.],
.cols = everything()
) %>%
rename("GMFCS" = "GMFCS (measured)")
ptsumm <-
tdat |>
gt() |>
tab_header(tit) |>
tab_style(
style = cell_text(align = "center"),
locations = list(cells_body(), cells_column_labels())
) |>
tab_style(
style = cell_text(weight = "bold"),
locations = cells_column_labels()
) |>
cols_width(
c(Age, Diagnosis, `Diagnosis Modifier`, GMFCS) ~ px(100),
`Functional Assessment Questionnaire` ~ 200
)
ptsumm
# Save for external use -----
if(params$figsave){
gtsave(ptsumm, "FIGURES/Patient_Summary.png")
}
```
### Patient History
```{r}
#| label: get-more-patient-info
#| cache: false
source("SCRIPTS/get_patient_info.R")
pi <- get_patient_info(xpt, params$deid)
```
::: {.panel-tabset .nav-pills}
#### Birth
```{r}
pi$tbirth
# Save for external use -----
if(params$figsave){
gtsave(pi$tbirth, "FIGURES/Birth_History.png")
}
```
#### Gait
```{r}
pi$tdev
# Save for external use -----
if(params$figsave){
gtsave(pi$tdev, "FIGURES/Gait_Development.png")
}
```
#### Treatment
```{r}
if(!is.null(pi$tsurg)) pi$tsurg
# Save for external use -----
if(params$figsave){
gtsave(pi$tsurg, "FIGURES/Treatment_History.png", expand = c(0, 40, 50, 0))
}
```
:::
### Gait Outcome Assessment List (GOAL)
::: {.panel-tabset .nav-pills}
#### GOAL Summary
```{r}
#| label: GOAL-survey
#| cache: false
source("SCRIPTS/build_goaltbl.R")
# Build goal table -----
t_goal <- build_goaltbl(xpt, datpre)
# Display if GOAL info exists -----
if(!is.null(t_goal)) t_goal
# Save for external use -----
if(params$figsave & !is.null(t_goal)){
gtsave(t_goal, "FIGURES/GOAL_Summary.png")
}
```
#### Very Important GOALs
```{r}
#| label: v-important-goals
#| cache: false
source("SCRIPTS/get_goal.R")
source("SCRIPTS/build_importanttbl.R")
# Get goal table data -----
goal <- get_goal(params)
# Build importance table -----
t_imp <- build_importanttbl(goal, params)
# Display if GOAL info exists -----
if(!is.null(t_imp)) t_imp
# Save for external use -----
if(params$figsave & !is.null(t_imp)){
gtsave(t_imp, "FIGURES/GOAL_Importance.png", expand = c(0, 40, 50, 0))
}
```
#### GOAL History
```{r}
#| label: display-goal
#| fig-height: 3.5
v <- c("TOTAL_Score", "Activities_Sports_Rec", "ADL_Indep", "Braces_Mobility",
"Gait_Func_Mobility", "Gait_Pattern_Appearance", "Image_Esteem",
"Pain_Discomfort_Fatigue")
cap = ""
p <- plot_development(fd, dat, v, v, cap, devdat)
p
```
:::
:::
## Video
```{r}
#| label: get-video
#| eval: params$usevideo
#| include: false
#| cache: false
source("SCRIPTS/compress_video.R")
# Generate list of all videos -----
temp <- fd$FD %>% arrange(rev(age))
ages <- temp$age %>% unique()
# Generate labels for videos -----
yrs <- trunc(ages)
mos <- round(12 * (ages - yrs))
vidlabs <- glue("{yrs}yr_{mos}mo")
vidlist <- glue("{vidlabs}.mp4")
vidfile <- temp %>% filter(SIDE == "L") %>% pull(Video_File)
# Remove existing local videos and compress new -----
temp <- list.files(pattern = ".mp4$")
# Use Deidentified video if appropriate -----
# Deidentified videos created seperately and stored in VIDEO/MRN/
if(params$deid & params$usevideo){
res <- str_extract(vidfile, "[^\\\\]+$")
vidfile <- glue("VIDEO/{params$MRN}/{res}")
compress_video(temp, vidlist, vidlabs)
}
if(!params$deid & !(all(vidlist %in% temp))){
compress_video(temp, vidlist, vidlabs)
vidname <- vidlist
}
```
```{r}
#| label: show-video
#| results: asis
#| panel: tabset
#| eval: false
# This is the old video tabs code. Leaving it here for now (26AUG2024)
# for (kk in 1:length(vidlist)) {
# cat("\n\n###", str_replace(vidlabs[kk], "_", " + "), " \n\n")
# cat("\n\n <video width=800 controls>
# <source src=", vidlist[kk], " type=video/mp4>
# </video> \n\n")
# }
```
```{r}
#| label: generate-html-video-code
#| cache: false
# If deidentified, fix mkr file name -----
# TODO
# Generate the code for the video tabs -----
source("SCRIPTS/build_video_tabs.R")
build_video_tabs(vidlist, vidfile)
```
```{r}
#| label: display-html-video-code
#| eval: params$usevideo
#| results: "asis"
#| echo: false
# Display the video tabs -----
# Quarto does not support chapters, so we generate HTML and display it directly
code <- readLines("video_tab_code.txt")
code <- paste(code, collapse = "\n")
cat(paste0("```{=html}\n", code, "\n```"))
```
## Treatment Assignment
::: {#treatment-assignment .panel-tabset}
### Propensity Profile
The likelihood that the limb would have undergone the indicated surgery based on historical practice standards.
> <center>{width="2in"}\
> *Meeting historical standards **does not** ensure a good outcome.\
> Check **Outcome Predictions** tab for additional information.*</center>
```{r}
#| label: compute-propensity-posteriors
#| cache: false
#| include: false
# Add NA for missing features - this is for limbs without gait data -----
vall <- names(datpre)
vx <- names(xpt)
vadd <- vall[!(vall %in% vx)]
xpt[vadd] <- NA
usegait <- params$usegait
post <- list()
for (s in surglist) {
set.seed(42)
if(usegait){
mod <- get(glue("mod_{s}"))
xpt[glue("p_{s}")] <- predict(mod, xpt[names(mod$X)])
post[[s]] <- bart_machine_get_posterior(mod, xpt[names(mod$X)])
} else {
mod <- get(glue("mod_{s}_nogait"))
xpt[glue("p_{s}")] <- predict(mod, xpt[names(mod$X)])
post[[s]] <- bart_machine_get_posterior(mod, xpt[names(mod$X)])
}
}
xL <- xpt |> filter(SIDE == "L")
xR <- xpt |> filter(SIDE == "R")
```
```{r}
#| label: display-propensity-profile
#| cache: false
source("SCRIPTS/get_prop.R")
# Get left and right propensity and format into L/R propensity table -----
propL <- get_prop(propLmean, post, surglist, "L")
propR <- get_prop(propRmean, post, surglist, "R")
proptbl <- ptableLR(propL, propR)
proptbl
# Save for external use -----
if(params$figsave){
gtsave(proptbl, "FIGURES/Propensity_Profile.png", expand = c(10, 10, 10, 10))
}
```
```{r}
#| label: compute-propensity-details
#| include: false
#| cache: false
# Build detail tables for each surgery using Shapley values -----
usegait <- params$usegait
shap_all <- list()
for (s in surglist) {
if(usegait){
mod <- get(glue("mod_{s}"))
res <- prop_shap_table(df=dat, sname=s, mod, x=xpt, varlabs)
shap_all[[s]] <- res
} else {
mod <- get(glue("mod_{s}_nogait"))
res <- prop_shap_table(df=dat, sname=s, mod, x=xpt, varlabs)
shap_all[[s]] <- res
}
}
```
```{r}
#| label: format-propensity-details
#| cache: false
ptprops <- bind_rows(propL, propR)
shap_tables <- list()
for (s in surglist) {
shap_tables[[s]] <- fmt_shapLR(shap_all, ptprops, s)
}
```
### Clinical Reasoning
Clinical factors underlying the historical practice standard, arranged from largest positive contributor ([**indications**]{style="color: #005600"}) to largest negative contributor ([**counter-indications**]{style="color: #841859"}). The top row shows the median (5%, 95%) surgery likelihood, color coded from [**high likelihood**]{style="color: #005600"} to [**low likelihood**]{style="color: #841859"}. The range in propensities accounts for measurement error variability in practice. The 🚶🏽‍♂️️ symbol indicates a kinematic variable<br> <br>
::: {.panel-tabset .nav-pills group="treatment"}
```{r}
#| label: display-propensity-details
#| results: asis
for (kk in 1:length(shap_tables)) {
cat("####", str_replace_all(surglist[kk], "_", " "), "\n\n")
print(shap_tables[[kk]])
cat("\n\n")
# Save for external use -----
if(params$figsave){
gtsave(shap_tables[[kk]], glue("FIGURES/Clin_Reasoning_{surglist[kk]}.png"), expand = c(10, 40, 10, 00))
}
}
```
:::
```{r}
#| label: build-all-outcome-models
#| eval: false
# This is the code to build the outcome prediction models -----
# Builds models for all default outcomes - Need to update with a "master list"
# Should run this if models or test data change. Default is eval: FALSE
# source("SCRIPTS/build_pred_outcome_BART.R")
#
# # Get all outcome variables -----
# v <- vector()
# for (ss in surglist) {
# v <- c(v, get_outcome_vars(ss))
# }
# v <- unique(v)
#
# # Compute Models -----
# usegait = TRUE
# bartlist <- v %>% map(\(v) build_pred_outcome_BART(dat, v, usegait), .progress = TRUE)
# names(bartlist) <- v
# Save bartlist into data
# saveRDS(bartlist, "DATA/outcome_models_bartlist.RDS")
```
```{r}
#| label: add-outcome-models-if-needed
#| eval: false
# This code chunk checks if a new outcome model is needed -----
# New outcome models can also be added manually
# (1) Add predictors to SCRIPTS/get_pred_vars.R
# (2) Adust vnew below to include new outcome name(s)
# (3) Run this chunk of code
# (4) This can also be used to *update* an outcome model
source("SCRIPTS/build_pred_outcome_BART.R")
bartlist <- readRDS("DATA/outcome_models_bartlist.RDS")
bartlist_nogait <- readRDS("DATA/outcome_models_bartlist_nogait.RDS")
# Find chosen for surgery s -----
# v <-
# outcome_setup %>%
# filter(surg == s) %>%
# arrange(name) %>%
# pull(value)
# Add (top) automatically or (bottom) manually
vnew <- NULL
# vnew <- v[!(v %in% names(bartlist))]
# vnew <- NULL
# vnew <- c("minsta_Ank_Ang_Sag")
# Fit and Save Models -----
if(length(vnew) > 0){
temp <- vnew %>% map(\(v) build_pred_outcome_BART(dat, v, usegait=TRUE), .progress = TRUE)
names(temp) <- vnew
bartlist <- c(bartlist, temp)
saveRDS(bartlist, "DATA/outcome_models_bartlist.RDS")
temp <- vnew %>% map(\(v) build_pred_outcome_BART(dat, v, usegait=FALSE), .progress = TRUE)
names(temp) <- vnew
bartlist_nogait <- c(bartlist_nogait, temp)
saveRDS(bartlist, "DATA/outcome_models_bartlist_nogait.RDS")
}
```
```{r}
#| label: predict-outcome-for-patient
#| cache: false
source("SCRIPTS/build_pred_outcome_BART.R")
bartlist <- readRDS("DATA/outcome_models_bartlist.RDS")
bartlist_nogait <- readRDS("DATA/outcome_models_bartlist_nogait.RDS")
# Find variables needed for BART models, and add NA for missing variables -----
# This is for kids without gait data
for (v in names(bartlist)) {
vmod <- names(bartlist[[v]]$mod$X)
vx <- names(xpt)
vadd <- vmod[!(vmod %in% vx)]
xpt[vadd] <- NA
}
usegait <- params$usegait
if(usegait){
bartlist_pred <- bartlist
} else {
bartlist_pred <- bartlist_nogait
}
# Predict outcomes all surgery x all variables -----
out_all <-
surglist %>%
map(\(s) pred_outcome_by_bart(xx=xpt, surglist, s, bartlist_pred, vlabs))
names(out_all) <- surglist
temp <- list()
for (ss in surglist) {
temp[[ss]] <- out_all[[ss]] %>% list_rbind()
}
# Arrange outcomes as tibble -----
out_all <- temp %>% list_rbind()
```
```{r}
#| label: save-patient-data
datsave <-
list(
xpt = xpt,
propL = propL,
propR = propR,
proptbl = proptbl,
shap_all = shap_all,
shap_tables = shap_tables,
out_all = out_all
)
# Save xpt, propL/R, etc... using params$MRN and params$Event_Date in name -----
saveRDS(datsave, glue("EBData_{params$MRN}_{params$Event_Date}.RDS"))
```
```{r}
#| label: build-outcome-plots
#| cache: false
# Loop over surgeries -----
p_s <- list()
jj <- 0
for (s in surglist) {
# Make outcome plot for each outcome variable for surgery s -----
v <-
outcome_setup %>%
filter(surg == s) %>%
arrange(name) %>%
pull(value)
# Option to customize outcome vars (now use DATA/Outcome_Setup_Custom.xlsx) -----
# if(s == "Femoral_Derotation_Osteotomy") v <-
# c("HIP_EXT_ROT", "meansta_Hip_Ang_Trn", "meansta_Pel_Ang_Sag", "meansta_Foo_Ang_Trn")
# Get propensity for setting title color -----
pvalL <-
propL |>
filter(Surgery == str_replace_all(s, "_", " ")) |>
pull(q50)/100
pvalR <-
propR |>
filter(Surgery == str_replace_all(s, "_", " ")) |>
pull(q50)/100
poutL <- list()
poutR <- list()
kk <- 0
# Loop over outcome variables -----
for (vv in v) {
kk <- kk + 1
poutL[[kk]] <-
outcome_plots_BART(
s = s,
outvar = vv,
xptsd = xL,
dref = dat,
vlabs = vlabs,
neglabs = neglabs,
poslabs = poslabs,
prop = pvalL,
out_all = out_all
)
poutR[[kk]] <-
outcome_plots_BART(
s = s,
outvar = vv,
xptsd = xR,
dref = dat,
vlabs = vlabs,
neglabs = neglabs,
poslabs = poslabs,
prop = pvalR,
out_all = out_all
)
}
# Assemble outcome plots -----
pL <- poutL[[1]] / poutL[[2]] / poutL[[3]] / poutL[[4]] &
theme(plot.margin = margin(c(15, 5, 15, 5)))
pR <- poutR[[1]] / poutR[[2]] / poutR[[3]] / poutR[[4]] &
theme(plot.margin = margin(c(15, 5, 15, 5)))
p_s[[s]] <- pL | pR
}
```
### Outcome Details
Plots show [*Change Scores*]{.underline}. [**Treated**]{style="color: #005600"} limbs underwent the surgery while [**Control**]{style="color: #841859"} limbs did not. The mean (point), 50% prediction interval (thick line), and 90% prediction interval (thin line) are shown.<br>
::: {.panel-tabset .nav-pills group="treatment"}
```{r}
#| label: display-outcome-plots
#| results: asis
#| fig-height: 6.5
for (kk in 1:length(p_s)) {
cat("####", str_replace_all(names(p_s)[[kk]], "_", " "), "\n\n")
print(p_s[[kk]])
cat("\n\n")
}
```
:::
:::
## Supporting Data
::: {#supporting-data .panel-tabset}
### Physical Examination
Cells are colored according to severity ([**none**]{style="color: #e2e2e2"}, [**mild**]{style="color: #E5C9CD"}, [**moderate**]{style="color: #E39AA8"}, [**severe**]{style="color: #D65979"}, [**not applicable**]{style="color: #f2cc64"}).
```{r}
#| label: build-physical-exam-tables
#| cache: false
source("SCRIPTS/petbl.R")
source("SCRIPTS/orthoseverity.R")
source("SCRIPTS/build_orthotbl.R")
source("SCRIPTS/build_spatbl.R")
source("SCRIPTS/build_strseltbl.R")
source("SCRIPTS/build_foottbl.R")
# Build contracture and torsion tables -----
meas <- c(
"HIP_FLEX", "HIP_EXT", "HIP_ABD_0", "HIP_ABD_90", "POP_ANG_UNI", "POP_ANG_BI",
"KNEE_EXT", "KNEE_FLEX", "EXTEN_LAG", "PATELLA_ALTA", "ANK_DORS_0", "ANK_DORS_90"
)
tblcon <- build_orthotbl(xpt, meas)
meas <- c(
"HIP_INT_ROT", "HIP_EXT_ROT", "ANTEVERSION", "EOS_FemTor",
"BIMAL", "SEC_TOE", "EOS_Bimal"
)
tbltor <- build_orthotbl(xpt, meas)
# Build neuro tables -----
tblstrsel <- build_strseltbl(xpt)
meas <- c(
"HIP_FLEX_SPAS", "ADDUCTOR_SPAS","HAMSTRING_SPAS", "RECT_FEM_SPAS",
"PLANTFLEX_SPAS", "POST_TIB_SPAS", "ANKLE_CLONUS"
)
tblspa <- build_spatbl(xpt, meas)
# Build foot tables -----
meas <- str_subset(names(xpt), "^WB")
tblwb <- build_foottbl(xpt, meas)
meas <- str_subset(names(xpt), "^NWB")
tblnwb <- build_foottbl(xpt, meas)
```
::: {.panel-tabset .nav-pills}
```{r}
#| label: display-physical-exam-tables
#| results: asis
temp <- c(
"Torsion", "Contracture", "Motor Control & Strength", "Spasticity",
"Weight-Bearing Foot", "Non-Weight-Bearing Foot")
pe <- list(tbltor, tblcon, tblstrsel, tblspa, tblwb, tblnwb)
for (kk in 1:length(temp)) {
cat("####", temp[kk], "\n\n")
print(pe[[kk]])
cat("\n\n")
# Save for external use -----
if(params$figsave){
xpnd <- ifelse(temp[kk]=="Motor Control & Strength", 40, 10)
gtsave(pe[[kk]], glue("FIGURES/PE_{temp[kk]}.png"), expand = c(10, xpnd, 10, 10))
}
}
```
:::
### Kinematics