ReportResults
Bernard
2020-10-21
Last updated: 2020-10-21
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Knit directory: 2020_LBPcausal/
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Load library
# Helper packages
library (tidyverse)
library (Rgraphviz)
library (corrr)
library (corrplot)
library (DataExplorer)
library (cowplot)
# Modelling
library (bnlearn)
library (qgraph)
# Parallel
library (doParallel)
# Tables
library (flextable)
library (officer)Import data
rm (list = ls())
load ("output/results_early2late.RData")
imp.data = impute (fit, data = df.bn, method = "bayes-lw")
var_order <- c("disability_early", "lbp_early", "lp_early", "pain_cope_early",
"sleep_early", "work_expect_early", "pain_persist_early",
"anx_early", "depress_early","fear_early",
"disability_late", "lbp_late", "lp_late", "pain_cope_late",
"sleep_late", "work_expect_late", "pain_persist_late",
"anx_late", "depress_late","fear_late")Descriptive plot
Mean & SD
df.plot <- df %>%
pivot_longer(cols = -c("id", "grp", "subgrp"),
names_to = "var",
values_to = "val") %>%
filter (var %in% var_order) %>%
mutate (var = factor (var, levels = var_order),
grp = factor (grp, labels = c("Advice", "IndvPT"))) %>%
group_by(grp, var) %>%
summarize (Mean = mean (val, na.rm = TRUE),
Sd = sd (val, na.rm = TRUE))`summarise()` regrouping output by 'grp' (override with `.groups` argument)f <- ggplot (df.plot) +
geom_point(aes (x = grp, y = Mean), colour = "black", fill = "black", stat = "identity") +
geom_errorbar(aes (x = grp, ymin = Mean - Sd, ymax = Mean + Sd), width = 0) +
facet_wrap(~ var, scales = "free") +
labs (x = "Group",
y = "Mean difference (SD)") +
theme(text = element_text(size=16)) +
theme_cowplot()
f
# tiff(width = 10, height = 8, units = "in", res = 100, file = "../manuscript/fig1.tiff")
# f
# dev.off()Plot missing data of subset data
f <- plot_missing(df.bn) +
labs (y = "Percentage missing",
x = "Variables") +
theme_cowplot()
# tiff(width = 10, height = 8, units = "in", res = 300, file = "../manuscript/sm_fig1.tiff")
# f
# dev.off()BN Results
demo.var <- c("grp")
# out.var <- grep ("outcome", names (df.bn), value = TRUE)
early.var <- grep ("early", names (df.bn), value = TRUE)
late.var <- grep ("late", names (df.bn), value = TRUE)
bootstr = custom.strength(boot_bl_rel, nodes = names(df.bn))
avg = averaged.network(bootstr, threshold = 0.5)
fit = bn.fit (avg, df.bn, method = "mle")
g = strength.plot(avg,
bootstr,
shape = "rectangle")
graph::nodeRenderInfo(g) = list(fontsize=18)
renderGraph(g)
arc_col <- data.frame(arcs = names (edgeRenderInfo(g)$col)) %>%
separate(arcs, c("parent", "child"), sep = "~")
coef_fit <- coef(fit)
coef_fit <- coef_fit[!map_lgl(coef_fit, is.matrix)]
coef_fit <- coef_fit[!map_lgl(coef_fit, is.table)]
coef_fit <- coef_fit %>%
unlist ()
coef_fit <- coef_fit[!grepl ("Intercept", names (coef_fit))]
coef_fit <- data.frame(arcs = names (coef_fit), coefs = coef_fit) %>%
separate(arcs, c ("child", "parent"), sep = "[.]")
new_col <- arc_col %>%
left_join(coef_fit, by = c("parent", "child")) %>%
mutate (coefs = replace_na(coefs,88)) %>%
mutate (col = ifelse (coefs < 0, "red",
ifelse (coefs == 88, "black", "blue"))) %>%
mutate (col = ifelse (parent == "pain_persist_early" & child == "pain_cope_early", "blue",
ifelse (parent == "lbp_early" & child == "pain_cope_early", "blue", col)))
new_arc_col <- new_col$col
names (new_arc_col) <- names (edgeRenderInfo(g)$col)
nodeRenderInfo(g)$fill[demo.var] = "cornsilk"
nodeRenderInfo(g)$fill[early.var] = "tan1"
nodeRenderInfo(g)$fill[late.var] = "gold"
#nodeRenderInfo(g)$fill[out.var] = "tomato"
edgeRenderInfo(g)$col <- new_arc_col
graph::nodeRenderInfo(g) = list(fontsize=14)
#tiff(width = 25, height = 15, units = "in", res = 300, file = "../manuscript/fig2.tiff")
renderGraph(g)
#dev.off()Correlation performance table
corr.df_ord <- corr.df[var_order]
correlation <- data.frame(Variable = names (corr.df_ord),
Value = corr.df_ord %>% round (2)) %>%
mutate (Strength = ifelse (abs (Value) <= 0.3, "negligible",
ifelse (abs(Value) > 0.3 & abs(Value <= 0.5), "low",
ifelse (abs(Value) > 0.5 & abs(Value <= 0.7), "moderate",
ifelse (abs(Value) > 0.7 & abs(Value <= 0.9), "high",
"very high")))))
correlation %>%
kableExtra::kable()| Variable | Value | Strength |
|---|---|---|
| disability_early | 0.72 | high |
| lbp_early | 0.68 | moderate |
| lp_early | 0.71 | high |
| pain_cope_early | 0.52 | moderate |
| sleep_early | 0.53 | moderate |
| work_expect_early | 0.44 | low |
| pain_persist_early | 0.62 | moderate |
| anx_early | 0.76 | high |
| depress_early | 0.69 | moderate |
| fear_early | 0.55 | moderate |
| disability_late | 0.73 | high |
| lbp_late | 0.71 | high |
| lp_late | 0.70 | moderate |
| pain_cope_late | 0.46 | low |
| sleep_late | 0.59 | moderate |
| work_expect_late | 0.42 | low |
| pain_persist_late | 0.65 | moderate |
| anx_late | 0.74 | high |
| depress_late | 0.65 | moderate |
| fear_late | 0.53 | moderate |
# ft <- flextable(correlation) %>%
# set_caption(paste0("Correlation between observed and predicted change values")) %>%
# autofit()
# my_path <- paste0("../manuscript/table1_corr.docx")
#
# my_doc <- read_docx() %>%
# body_add_flextable(ft)
#
# print (my_doc, target = my_path)Probing the expert system BN model
Evaluating the correlation between lbp_early-lbp_late relationship
See the correlation
set.seed(123)
sim <- cpdist(fit, nodes = c("lbp_early", "lbp_late"), n = 10^4,
evidence = (TRUE))
m <- lm(lbp_late ~ lbp_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = lbp_late ~ lbp_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-7.8249 -1.3806 0.0108 1.3642 7.3142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.334012 0.027806 -47.98 <2e-16 ***
lbp_early -0.431937 0.008416 -51.33 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.015 on 9998 degrees of freedom
Multiple R-squared: 0.2085, Adjusted R-squared: 0.2085
F-statistic: 2634 on 1 and 9998 DF, p-value: < 2.2e-16eqn <- bquote(italic(lbp_late) == .(b0) + .(b1)*italic(lbp_early) * "," ~~
r^2 == .(r2))
#tiff(width = 10, height = 8, units = "in", res = 300, file = "../manuscript/fig3.tiff")
plot(sim$lbp_early, sim$lbp_late,
ylab = "Late change LBP", xlab = "Early change LBP", col = "grey", cex.axis = 1.5, cex.lab = 1.5) +
abline(coef(m), lwd = 2) +
abline(v = 0, col = 2, lty = 2, lwd = 2) +
abline(h = 0, col = 2, lty = 2, lwd = 2) +
text(x = -5, y = 5, labels = eqn, cex = 1.2)
integer(0)#dev.off()Evaluating the mediating infuence of disability_early on the grp-lbp_early relationship
Influence of grp on lbp_early
set.seed(123)
sim <- cpdist(fit, nodes = c("grp", "lbp_early"), n = 10^4,
evidence = TRUE) # individualisedphysio, advice
m <- lm(lbp_early ~ grp, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = lbp_early ~ grp, data = sim)
Residuals:
Min 1Q Median 3Q Max
-8.6173 -1.6432 -0.0218 1.6649 9.8886
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.09537 0.03512 -59.657 <2e-16 ***
grpindividualisedphysio -0.41786 0.04815 -8.677 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.403 on 9998 degrees of freedom
Multiple R-squared: 0.007475, Adjusted R-squared: 0.007376
F-statistic: 75.3 on 1 and 9998 DF, p-value: < 2.2e-16eqn <- bquote(italic(lp_outcome) == .(b0) + .(b1)*italic(lp_late) * "," ~~
r^2 == .(r2))
sim %>%
mutate (grp = ifelse (grp == "advice", "Advice", "IndPT")) %>%
group_by(grp) %>%
summarize (Mean = mean (lbp_early),
Sd = sd (lbp_early)) %>%
ggplot () +
geom_bar(aes (x = grp, y = Mean), color = "black", stat = "identity") +
geom_errorbar(aes (x = grp, ymin = Mean - Sd, ymax = Mean ), width = 0.2) +
labs (x = "Group",
y = "Change in early LBP") +
theme_cowplot()`summarise()` ungrouping output (override with `.groups` argument)
Influence of grp on lbp_early when disability_early is constant
set.seed(123)
avg.mutilated = mutilated(avg, evidence = list(disability_early = 0))
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$disability_early = list(coef = c("(Intercept)" = 0), sd = 0)
sim <- cpdist(fitted.mutilated, nodes = c("grp", "lbp_early"), n = 10^4,
evidence = TRUE) # individualisedphysio, advice
m <- lm(lbp_early ~ grp, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = lbp_early ~ grp, data = sim)
Residuals:
Min 1Q Median 3Q Max
-7.073 -1.386 -0.015 1.360 8.758
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.31150 0.02958 -44.343 <2e-16 ***
grpindividualisedphysio 0.04672 0.04055 1.152 0.249
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.023 on 9998 degrees of freedom
Multiple R-squared: 0.0001328, Adjusted R-squared: 3.277e-05
F-statistic: 1.328 on 1 and 9998 DF, p-value: 0.2492sim %>%
mutate (grp = ifelse (grp == "advice", "Advice", "IndPT")) %>%
group_by(grp) %>%
summarize (Mean = mean (lbp_early),
Sd = sd (lbp_early)) %>%
ggplot () +
geom_bar(aes (x = grp, y = Mean), color = "black", stat = "identity") +
geom_errorbar(aes (x = grp, ymin = Mean - Sd, ymax = Mean ), width = 0.2) +
labs (x = "Group",
y = "Change in early LBP") +
theme_cowplot()
Evaluating the disability_early-depress_early relationship
See the correlation
set.seed(123)
sim <- cpdist(fit, nodes = c("disability_early", "depress_early"), n = 10^4,
evidence = (TRUE))
m <- lm(depress_early ~ disability_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = depress_early ~ disability_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-10.0691 -1.9157 -0.0429 1.9069 11.9347
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.632550 0.036458 -17.35 <2e-16 ***
disability_early 0.044540 0.002185 20.38 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.828 on 9998 degrees of freedom
Multiple R-squared: 0.03989, Adjusted R-squared: 0.0398
F-statistic: 415.4 on 1 and 9998 DF, p-value: < 2.2e-16eqn <- bquote(italic(depress_early) == .(b0) + .(b1)*italic(disability_early) * "," ~~
r^2 == .(r2))
#tiff(width = 10, height = 8, units = "in", res = 300, file = "../manuscript/fig4.tiff")
plot(sim$disability_early, sim$depress_early, ylab = "Early change depression", xlab = "Early change disability", col = "grey", cex.axis = 1.5, cex.lab = 1.5) +
abline(coef(m), lwd = 2) +
abline(v = 0, col = 2, lty = 2, lwd = 2) +
abline(h = 0, col = 2, lty = 2, lwd = 2) +
text(x = -20, y = 6, labels = eqn, cex = 1.2)
integer(0)#dev.off()Mediating influence of fear_early.
set.seed(123)
avg.mutilated = mutilated(avg, evidence = list(fear_early = 0))
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$fear_early= list(coef = c("(Intercept)" = 0), sd = 0)
sim <- cpdist(fitted.mutilated, nodes = c("disability_early", "depress_early"), n = 10^4,
evidence = (TRUE))
m <- lm(depress_early ~ disability_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = depress_early ~ disability_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-10.1033 -1.9252 -0.0339 1.9126 11.9575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.615390 0.036416 -16.90 <2e-16 ***
disability_early 0.041966 0.002183 19.23 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.824 on 9998 degrees of freedom
Multiple R-squared: 0.03566, Adjusted R-squared: 0.03556
F-statistic: 369.7 on 1 and 9998 DF, p-value: < 2.2e-16Mediating influence of lbp_early.
set.seed(123)
avg.mutilated = mutilated(avg, evidence = list(lbp_early = 0))
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$lbp_early = list(coef = c("(Intercept)" = 0), sd = 0)
sim <- cpdist(fitted.mutilated, nodes = c("disability_early", "depress_early"), n = 10^4,
evidence = (TRUE))
m <- lm(depress_early ~ disability_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = depress_early ~ disability_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-10.0558 -1.9166 -0.0348 1.9094 12.0480
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.558694 0.036407 -15.35 <2e-16 ***
disability_early 0.038628 0.002182 17.70 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.824 on 9998 degrees of freedom
Multiple R-squared: 0.03039, Adjusted R-squared: 0.03029
F-statistic: 313.3 on 1 and 9998 DF, p-value: < 2.2e-16Mediating influence of pain_persist_early.
set.seed(123)
avg.mutilated = mutilated(avg, evidence = list(pain_persist_early = 0))
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$pain_persist_early = list(coef = c("(Intercept)" = 0), sd = 0)
sim <- cpdist(fitted.mutilated, nodes = c("disability_early", "depress_early"), n = 10^4,
evidence = (TRUE))
m <- lm(depress_early ~ disability_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = depress_early ~ disability_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-10.3899 -1.8847 -0.0044 1.8833 11.2537
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.439643 0.035721 -12.31 <2e-16 ***
disability_early 0.027573 0.002141 12.88 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.77 on 9998 degrees of freedom
Multiple R-squared: 0.01632, Adjusted R-squared: 0.01622
F-statistic: 165.8 on 1 and 9998 DF, p-value: < 2.2e-16Mediating influence of fear_early, lbp_early, pain_persist_early
set.seed(123)
avg.mutilated = mutilated(avg, evidence = list(fear_early = 0, lbp_early = 0, pain_persist_early = 0))
fitted.mutilated = bn.fit (avg.mutilated , df.bn, method = "mle")
fitted.mutilated$fear_early = list(coef = c("(Intercept)" = 0), sd = 0)
fitted.mutilated$lbp_early = list(coef = c("(Intercept)" = 0), sd = 0)
fitted.mutilated$pain_persist_early = list(coef = c("(Intercept)" = 0), sd = 0)
g = strength.plot(avg.mutilated,
bootstr,
shape = "rectangle")
graph::nodeRenderInfo(g) = list(fontsize=18)
renderGraph(g)
sim <- cpdist(fitted.mutilated, nodes = c("disability_early", "depress_early"), n = 10^4,
evidence = (TRUE))
m <- lm(depress_early ~ disability_early, data = sim)
coefs <- coef(m)
b0 <- round(coefs[1], 2)
b1 <- round(coefs[2],2)
r2 <- round(summary(m)$r.squared, 2)
summary (m)
Call:
lm(formula = depress_early ~ disability_early, data = sim)
Residuals:
Min 1Q Median 3Q Max
-11.1248 -1.8079 -0.0121 1.7921 11.4957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.437358 0.035500 -12.32 <2e-16 ***
disability_early 0.024434 0.002132 11.46 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.752 on 9998 degrees of freedom
Multiple R-squared: 0.01296, Adjusted R-squared: 0.01286
F-statistic: 131.3 on 1 and 9998 DF, p-value: < 2.2e-16
sessionInfo()R version 3.6.2 (2019-12-12)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.1252
[2] LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252
attached base packages:
[1] grid parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] officer_0.3.12 flextable_0.5.10 doParallel_1.0.15
[4] iterators_1.0.12 foreach_1.5.0 qgraph_1.6.5
[7] bnlearn_4.5 cowplot_1.0.0 DataExplorer_0.8.1
[10] corrplot_0.84 corrr_0.4.2 Rgraphviz_2.30.0
[13] graph_1.64.0 BiocGenerics_0.32.0 forcats_0.5.0
[16] stringr_1.4.0 dplyr_1.0.1 purrr_0.3.4
[19] readr_1.3.1 tidyr_1.1.1 tibble_3.0.3
[22] ggplot2_3.3.2 tidyverse_1.3.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 uuid_0.1-4 backports_1.1.7
[4] Hmisc_4.4-1 BDgraph_2.62 systemfonts_0.2.3
[7] plyr_1.8.6 igraph_1.2.5 splines_3.6.2
[10] digest_0.6.25 htmltools_0.5.0 fansi_0.4.1
[13] magrittr_1.5 checkmate_2.0.0 cluster_2.1.0
[16] modelr_0.1.8 jpeg_0.1-8.1 colorspace_1.4-1
[19] blob_1.2.1 rvest_0.3.6 haven_2.3.1
[22] xfun_0.16 crayon_1.3.4 jsonlite_1.7.0
[25] survival_3.2-3 glue_1.4.1 kableExtra_1.1.0
[28] gtable_0.3.0 webshot_0.5.2 abind_1.4-5
[31] scales_1.1.1 DBI_1.1.0 Rcpp_1.0.5
[34] viridisLite_0.3.0 htmlTable_2.0.1 foreign_0.8-72
[37] Formula_1.2-3 stats4_3.6.2 htmlwidgets_1.5.1
[40] httr_1.4.2 RColorBrewer_1.1-2 lavaan_0.6-7
[43] ellipsis_0.3.1 pkgconfig_2.0.3 farver_2.0.3
[46] nnet_7.3-14 dbplyr_1.4.4 tidyselect_1.1.0
[49] labeling_0.3 rlang_0.4.7 reshape2_1.4.4
[52] later_1.1.0.1 munsell_0.5.0 cellranger_1.1.0
[55] tools_3.6.2 cli_2.0.2 generics_0.0.2
[58] broom_0.7.0 fdrtool_1.2.15 evaluate_0.14
[61] yaml_2.2.1 knitr_1.29 fs_1.5.0
[64] zip_2.1.0 glasso_1.11 pbapply_1.4-3
[67] nlme_3.1-142 whisker_0.4 xml2_1.3.2
[70] compiler_3.6.2 rstudioapi_0.11 png_0.1-7
[73] reprex_0.3.0 huge_1.3.4.1 pbivnorm_0.6.0
[76] stringi_1.4.6 highr_0.8 gdtools_0.2.2
[79] lattice_0.20-41 Matrix_1.2-18 psych_2.0.7
[82] vctrs_0.3.2 pillar_1.4.6 lifecycle_0.2.0
[85] networkD3_0.4 data.table_1.13.0 corpcor_1.6.9
[88] httpuv_1.5.4 R6_2.4.1 latticeExtra_0.6-29
[91] promises_1.1.1 gridExtra_2.3 codetools_0.2-16
[94] MASS_7.3-51.6 gtools_3.8.2 assertthat_0.2.1
[97] rprojroot_1.3-2 rjson_0.2.20 withr_2.2.0
[100] mnormt_1.5-5 hms_0.5.3 rpart_4.1-15
[103] rmarkdown_2.3 git2r_0.27.1 d3Network_0.5.2.1
[106] lubridate_1.7.9 base64enc_0.1-3