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Supplementary_Fig8_supdrug_hz_poisson.R
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Supplementary_Fig8_supdrug_hz_poisson.R
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# Script for visualizing the regression model
# Supp Fig 8: vitamin D supplement/prescription and herpes zoster
# data:
source("./master_script.R")
# Load additional library:
lapply(c("ggpubr","grid","gridExtra","forcats"), require, character.only=T)
options(digits = 3, scipen = 999)
# survival objects for 2nd exposure
# hz_surv <- survival::Surv(time = as.numeric(bd_i$time_in)/365.25,
# time2 = as.numeric(bd_i$time_out)/365.25,
# event = bd_i$hz)
# Vitamin D supplementation ----
# crude----
vd_supp_crude_glm <- glm(hz~all_supp + offset(log(fu_yr)),
data = bd_i, family = "poisson")
vd_supp_crude_glm %>% idr.display() # check output
# Poisson regression predicting hz with offset = NULL
#
# IDR(95%CI) P(Wald's test) P(LR-test)
# all_supp: 1_vitD and mineral supplement vs 0_no vitD supplement 0.91 (0.81,1.02) 0.121 0.116
#
# Log-likelihood = -14378.3598
# No. of observations = 64630
# AIC value = 28760.7197
vd_supp_crude <- vd_supp_crude_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T) %>%
filter(term != "(Intercept)") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_supp_crude %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_supp_crude$model <- "Crude"
# add n
vd_supp_crude_n <- vd_supp_crude_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_supp_crude %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_supp_crude_n$`(Intercept)` - vd_supp_crude_n$`all_supp1_vitD and mineral supplement`),
term == "all_supp1_vitD and mineral supplement" ~ vd_supp_crude_n$`all_supp1_vitD and mineral supplement`
))
rm(vd_supp_crude_glm, vd_supp_crude_n)# house keeping
# partial ----
vd_supp_partial_glm <- glm(hz~all_supp + offset(log(fu_yr)) + sex + age_c,
data = bd_i, family = "poisson")
vd_supp_partial_glm %>% idr.display()
# Poisson regression predicting hz with offset = NULL
# crude IDR(95%CI) adj. IDR(95%CI) P(Wald's test) P(LR-test)
# all_supp: 1_vitD and mineral supplement vs 0_no vitD supplement 0.91 (0.81,1.02) 0.97 (0.86,1.09) 0.625 0.623
#
# sex 0.89 (0.82,0.96) 0.87 (0.8,0.94) < 0.001 < 0.001
#
# age_c (cont. var.) 1.03 (1.02,1.03) 1.03 (1.02,1.03) < 0.001 < 0.001
#
# Log-likelihood = -14331.1419
# No. of observations = 64630
# AIC value = 28670.2837
vd_supp_partial <- vd_supp_partial_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T) %>%
filter(term == "all_supp1_vitD and mineral supplement") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_supp_partial %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_supp_partial$model <- "Partially adjusted" # marked the model
# add N
vd_supp_partial_n <- vd_supp_partial_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_supp_partial %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_supp_partial_n$`(Intercept)` - vd_supp_partial_n$`all_supp1_vitD and mineral supplement`),
term == "all_supp1_vitD and mineral supplement" ~ vd_supp_partial_n$`all_supp1_vitD and mineral supplement`
))
rm(vd_supp_partial_glm, vd_supp_partial_n) # house keeping
# Full adjusted model ----
vd_supp_full_glm <- glm(hz~ all_supp + offset(log(fu_yr)) + sex + age_c + ethnic +
bmi_group + drink_freq_c + smoke_stat + imd_bd_q + regions +
season_c + asthma + ckd+ copd + depress + dm + ibd + ra + sle + immunosuppression,
data = bd_i, family = "poisson")
vd_supp_full_glm %>% idr.display()
# Poisson regression predicting hz with offset = NULL
#
# crude IDR(95%CI) adj. IDR(95%CI) P(Wald's test)
# all_supp: 1_vitD and mineral supplement vs 0_no vitD supplement 0.9 (0.8,1.01) 0.97 (0.86,1.1) 0.626
#
# sex 0.89 (0.82,0.96) 0.85 (0.79,0.93) < 0.001
#
# age_c (cont. var.) 1.03 (1.02,1.03) 1.02 (1.02,1.03) < 0.001
#
# ethnic: ref.=1_white
# 2_mixed 0.95 (0.54,1.68) 1.05 (0.6,1.86) 0.862
# 3_asian 0.9 (0.66,1.23) 1.04 (0.75,1.44) 0.804
# 4_black 0.78 (0.5,1.19) 0.93 (0.6,1.44) 0.748
# 5_chinese 0.67 (0.25,1.78) 0.83 (0.31,2.22) 0.71
# 6_others 1 (0.67,1.51) 1.12 (0.74,1.69) 0.605
#
# bmi_group: ref.=1_healthy weight
# 0_underweight 0.87 (0.46,1.62) 0.88 (0.47,1.65) 0.693
# 2_over weight 1 (0.92,1.1) 0.99 (0.9,1.08) 0.803
# 3_obese 1.02 (0.92,1.13) 0.99 (0.89,1.1) 0.847
#
# drink_freq_c: ref.=never
# sometimes 0.95 (0.81,1.11) 0.97 (0.83,1.14) 0.735
# weekly 1.02 (0.88,1.18) 1.08 (0.93,1.25) 0.306
# daily 0.95 (0.81,1.12) 0.98 (0.83,1.16) 0.799
#
# smoke_stat: ref.=non-smoker
# ex-smoker 1.15 (1.06,1.25) 1.11 (1.02,1.21) 0.012
# current-smoker 0.9651 (0.8276,1.1253) 1.0001 (0.8549,1.17) 0.999
#
# imd_bd_q: ref.=least_deprived
# 2_deprived 1.03 (0.91,1.17) 1.03 (0.91,1.17) 0.633
# 3_deprived 1.17 (1.04,1.33) 1.19 (1.05,1.35) 0.006
# 4_deprived 1.1 (0.98,1.25) 1.12 (0.99,1.27) 0.078
# most_deprived 1.03 (0.91,1.17) 1.05 (0.92,1.21) 0.446
#
# regions: ref.=East Midlands
# London 0.93 (0.78,1.12) 0.94 (0.78,1.13) 0.497
# North East 1.06 (0.91,1.23) 1.06 (0.91,1.24) 0.427
# North West 1.24 (1.05,1.46) 1.23 (1.04,1.45) 0.014
# South East 1.07 (0.8,1.43) 1.13 (0.84,1.51) 0.419
# South West 0.99 (0.77,1.27) 1.02 (0.79,1.3) 0.906
# West Midlands 1.06 (0.87,1.29) 1.04 (0.85,1.28) 0.673
# Yorkshire and The Humber 0.93 (0.81,1.07) 0.92 (0.8,1.05) 0.224
# Wales 1.68 (1.43,1.96) 1.68 (1.43,1.97) < 0.001
# Scotland 0.71 (0.6,0.85) 0.72 (0.61,0.86) < 0.001
#
# season_c: ref.=2_Summer
# 1_Spring 1.01 (0.91,1.12) 0.97 (0.87,1.08) 0.577
# 3_Autumn 0.91 (0.81,1.01) 0.9 (0.81,1.01) 0.085
# 4_Winter 1.041 (0.931,1.1639) 1.0003 (0.8905,1.1237) 0.996
#
# asthma 1.12 (1.01,1.24) 1.06 (0.95,1.18) 0.279
#
# ckd: CKD vs No CKD 1.63 (1.19,2.24) 1.33 (0.97,1.83) 0.079
#
# copd: COPD vs No COPD 1.57 (1.32,1.87) 1.34 (1.12,1.62) 0.002
#
# depress: depression vs No depression 0.9861 (0.8774,1.1082) 0.9924 (0.8814,1.1173) 0.899
#
# dm: Have DM vs No DM 0.99 (0.85,1.16) 0.91 (0.77,1.07) 0.25
#
# ibd: Inflammatory bowel disease vs No inflammatory bowel diseaase 1.58 (1.32,1.89) 1.47 (1.23,1.77) < 0.001
#
# ra: RA vs No RA 1.61 (1.29,2.01) 1.21 (0.96,1.53) 0.108
#
# sle: SLE vs No SLE 1.54 (0.85,2.79) 1.23 (0.68,2.24) 0.494
#
# immunosuppression: Immunosuppression vs Not immunosuppressive 1.89 (1.61,2.21) 1.59 (1.34,1.88) < 0.001
#
# P(LR-test)
# all_supp: 1_vitD and mineral supplement vs 0_no vitD supplement 0.625
#
# sex < 0.001
#
# age_c (cont. var.) < 0.001
#
# ethnic: ref.=1_white 0.987
# 2_mixed
# 3_asian
# 4_black
# 5_chinese
# 6_others
#
# bmi_group: ref.=1_healthy weight 0.976
# 0_underweight
# 2_over weight
# 3_obese
#
# drink_freq_c: ref.=never 0.103
# sometimes
# weekly
# daily
#
# smoke_stat: ref.=non-smoker 0.034
# ex-smoker
# current-smoker
#
# imd_bd_q: ref.=least_deprived 0.039
# 2_deprived
# 3_deprived
# 4_deprived
# most_deprived
#
# regions: ref.=East Midlands < 0.001
# London
# North East
# North West
# South East
# South West
# West Midlands
# Yorkshire and The Humber
# Wales
# Scotland
#
# season_c: ref.=2_Summer 0.265
# 1_Spring
# 3_Autumn
# 4_Winter
#
# asthma 0.281
#
# ckd: CKD vs No CKD 0.093
#
# copd: COPD vs No COPD 0.003
#
# depress: depression vs No depression 0.899
#
# dm: Have DM vs No DM 0.244
#
# ibd: Inflammatory bowel disease vs No inflammatory bowel diseaase < 0.001
#
# ra: RA vs No RA 0.117
#
# sle: SLE vs No SLE 0.508
#
# immunosuppression: Immunosuppression vs Not immunosuppressive < 0.001
#
# Log-likelihood = -13664.3283
# No. of observations = 62416
# AIC value = 27412.6565
vd_supp_full <- vd_supp_full_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T, n_digits = 2) %>%
filter(term == "all_supp1_vitD and mineral supplement") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_supp_full %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_supp_full$model <- "Fully adjusted" # marked the model
# add N
vd_supp_full_n <- vd_supp_full_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_supp_full %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_supp_full_n$`(Intercept)` - vd_supp_full_n$`all_supp1_vitD and mineral supplement`),
term == "all_supp1_vitD and mineral supplement" ~ vd_supp_full_n$`all_supp1_vitD and mineral supplement`
))
rm(vd_supp_full_glm, vd_supp_full_n) # clean
fig3a_data <- bind_rows(vd_supp_crude, vd_supp_partial,vd_supp_full)
rm(list = ls(pattern = "supp"))# hosue keeping
fig3a_data %>% write_csv("dataset_wd/fig3a_data.csv")
# fig3a_data <- read_csv("dataset_wd/fig3a_data.csv")
# Fig3a: data management for plotting: ----
fig3a_data<- fig3a_data %>%
mutate(conf.high2 = format(conf.high, nmall=2)) %>% # 2 digits strings
mutate(conf.low2 = format(conf.low, nmall=2)) %>% # 2 digits strings
mutate(conf.rr = format(estimate, nmall=2)) %>% # 2 digits strings
mutate(ci = paste(conf.low2, conf.high2, sep = ",")) %>% # lci, hci
mutate(ci_l = paste0("(", ci)) %>% # (lci, hci
mutate(ci_r = paste0(ci_l, ")")) %>% # (lci, hci)
mutate(rr = paste(conf.rr, ci_r, sep = " ") ) %>% # rr (lci, hci)
mutate(RR = ifelse(estimate==1.00, "1 (Reference)",rr)) %>% # set 1.0 into 1 (reference)
mutate(vd_intake=case_when(
term == "all_supp1_vitD and mineral supplement" ~ "Taking supplements",
term == "No_supp" ~ "No supplements")) %>% # rename for labelling
mutate(model_order=case_when(
model == "Crude" ~ 1,
model == "Partially adjusted" ~ 2,
model == "Fully adjusted" ~3)) %>% # for facet order
mutate(Model = ifelse(vd_intake =="No supplements",
model, NA)) %>% # for table labelling
arrange( model_order, # arrange order by vd status
match(vd_intake, c("No supplements", "Taking supplements"))) %>%
dplyr::select(model_order, Model, vd_intake, N, estimate, conf.low, conf.high, RR)
# deal with factors issues:
fig3a_data[,1:3] <- map(fig3a_data[,1:3], as.factor) # factor coercion
fig3a_data$model_order %<>% factor(
labels = c("Crude", "Partially adjusted", "Fully adjusted")) # labelled for facet
fig3a_data$vd_intake %<>% relevel(ref = "Taking supplements")
fig3a_data$order <- 6:1
# Fig 3a: main foreat plot ----
fig3a <- fig3a_data %>%
ggplot(aes(y = order, x = estimate,
xmin=conf.low, xmax=conf.high, color=vd_intake)) +
xlim(0.8,1.8)+
geom_point(size=5, shape=19) +
geom_errorbarh(height=.3) +
geom_vline(xintercept=1, lty=2) +
ggtitle(" ")+
theme_classic2() +
theme(plot.title = element_text(size = 20 ,lineheight=.001),
text = element_text(size = 20),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.y = element_blank(),
legend.position = "none",
axis.title.x = element_text(colour = "black", size = 20),
axis.title.y = element_blank(),
axis.text.x = element_text(colour = "black", size = 20),
axis.text.y = element_blank())+ xlab("Rate ratio")
fig3a
# Fig 3a: left table ----
fig3a_table_l <- fig3a_data %>%
ggplot(aes(y = order)) + xlim(0,2) +
geom_text(aes(x = 0, label = Model ),lineheight = 0.001,
hjust = 0, size = 6, colour = "black") +
geom_text(aes(x = 0.8, label = vd_intake),lineheight = 0.001,
hjust = 0 ,size = 6, colour = "black") +
geom_text(aes(x = 1.8, label = N),lineheight = 0.001,
hjust = 0 ,size = 6, colour = "black") +
theme_classic2() +
ggtitle(" Model Vitamin D intake N")+ # use title for header
theme(plot.title = element_text(size = 20 ,lineheight=.001, face="bold"),
strip.text.x = element_blank(),
strip.text.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title.x = element_text(colour = "white", size = 22), # make the text invisible
axis.title.y = element_blank(),
axis.line = element_line(colour = "white"), # make the lines invisible
axis.text.x = element_text(colour = "white", size = 22), # make the text invisible
axis.text.y = element_blank(),
axis.ticks = element_blank()) + xlab(" ")
fig3a_table_l
# Fig 3a: right table ----
fig3a_table_r <- fig3a_data %>%
ggplot(aes(y = order)) + xlim(0,0.5) +
geom_text(aes(x = 0, label = RR),lineheight = 0.01, hjust = 0, size = 6, colour = "black") +
theme_classic2( ) +
ggtitle(" RR(95%CI)")+
theme(plot.title = element_text(size = 20 ,lineheight=.001, face="bold"),
strip.text.x = element_blank(),
strip.text.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title.x = element_text(colour = "white", size = 22),# make the text invisible
axis.title.y = element_blank(),
axis.text.x = element_text(colour = "white", size = 22), # make the text invisible
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(colour = "white"), # make the lines invisible
panel.background=element_blank(),
plot.background=element_blank()) + xlab(" ")
fig3a_table_r
# Fig3a: combine tables and forest plot ----
fig3a_combine <- grid.arrange(fig3a_table_l, fig3a, fig3a_table_r, ncol = 3)
# W:1800 H:400
# Fig 3b Vitamin D prescription and HZ ----
# crude ----
vd_drug_crude_glm <- glm(hz~vd_prescription + offset(log(fu_yr)),
data = bd_i, family = "poisson")
vd_drug_crude_glm %>% idr.display()
# Poisson regression predicting hz with offset = NULL
#
# IDR(95%CI) P(Wald's test) P(LR-test)
# vd_prescription: Had vitamin D prescription vs No vitamin D prescription 1.48 (1.27,1.71) < 0.001 < 0.001
#
# Log-likelihood = -36341.7258
# No. of observations = 177572
# AIC value = 72687.4515
vd_drug_crude <- vd_drug_crude_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T) %>%
filter(term != "(Intercept)") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_drug_crude %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_drug_crude$model <- "Crude"
# add N
vd_drug_crude_n <- vd_drug_crude_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_drug_crude %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_drug_crude_n$`(Intercept)` -vd_drug_crude_n$`vd_prescriptionHad vitamin D prescription`),
term == "vd_prescriptionHad vitamin D prescription" ~ vd_drug_crude_n$`vd_prescriptionHad vitamin D prescription`
))
rm(vd_drug_crude_glm,vd_drug_crude_n)
# Partially adjusted for sex and age
vd_drug_partial_glm <- glm(hz~vd_prescription + offset(log(fu_yr)) + sex + age_c,
data = bd_i, family = "poisson")
vd_drug_partial_glm %>% idr.display()
# Poisson regression predicting hz with offset = NULL
#
# crude IDR(95%CI) adj. IDR(95%CI) P(Wald's test)
# vd_prescription: Had vitamin D prescription vs No vitamin D prescription 1.48 (1.27,1.71) 1.22 (1.05,1.42) 0.009
#
# sex 0.82 (0.78,0.87) 0.81 (0.77,0.85) < 0.001
#
# age_c (cont. var.) 1.03 (1.03,1.04) 1.03 (1.03,1.04) < 0.001
#
# P(LR-test)
# vd_prescription: Had vitamin D prescription vs No vitamin D prescription 0.011
#
# sex < 0.001
#
# age_c (cont. var.) < 0.001
#
# Log-likelihood = -36086.8578
# No. of observations = 177572
# AIC value = 72181.7157
vd_drug_partial <- vd_drug_partial_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T) %>%
filter(term == "vd_prescriptionHad vitamin D prescription") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_drug_partial %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_drug_partial$model <- "Partially adjusted"
# add N
vd_drug_partial_n <- vd_drug_partial_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_drug_partial %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_drug_partial_n$`(Intercept)`-vd_drug_partial_n$`vd_prescriptionHad vitamin D prescription`),
term == "vd_prescriptionHad vitamin D prescription" ~ vd_drug_partial_n$`vd_prescriptionHad vitamin D prescription`
))
rm(vd_drug_partial_glm,vd_drug_partial_n) # clean
# Fully adjusted
vd_drug_full_glm <- glm(hz~ vd_prescription + offset(log(fu_yr)) + sex + age_c + ethnic +
bmi_group + drink_freq_c + smoke_stat + imd_bd_q + regions +
season_c + asthma + ckd+ copd + depress + dm + ibd + ra + sle + immunosuppression,
data = bd_i, family = "poisson")
vd_drug_full_glm %>% idr.display()
# Poisson regression predicting hz with offset = NULL
#
# crude IDR(95%CI) adj. IDR(95%CI)
# vd_prescription: Had vitamin D prescription vs No vitamin D prescription 1.49 (1.28,1.73) 1.09 (0.93,1.27)
#
# sex 0.82 (0.78,0.87) 0.8 (0.76,0.85)
#
# age_c (cont. var.) 1.03 (1.03,1.04) 1.03 (1.03,1.04)
#
# ethnic: ref.=1_white
# 2_mixed 0.93 (0.65,1.34) 1.07 (0.74,1.54)
# 3_asian 0.79 (0.64,0.97) 0.94 (0.75,1.16)
# 4_black 0.71 (0.53,0.95) 0.87 (0.65,1.17)
# 5_chinese 0.9 (0.52,1.55) 1.09 (0.63,1.88)
# 6_others 0.78 (0.59,1.04) 0.88 (0.66,1.17)
#
# bmi_group: ref.=1_healthy weight
# 0_underweight 1.08 (0.76,1.54) 1.07 (0.75,1.53)
# 2_over weight 1.04 (0.98,1.11) 1.02 (0.96,1.08)
# 3_obese 1.04 (0.97,1.11) 0.98 (0.92,1.05)
#
# drink_freq_c: ref.=never
# sometimes 0.9725 (0.8817,1.0727) 1.0075 (0.9123,1.1128)
# weekly 0.94 (0.86,1.03) 1.03 (0.94,1.13)
# daily 0.93 (0.84,1.03) 0.96 (0.87,1.07)
#
# smoke_stat: ref.=non-smoker
# ex-smoker 1.19 (1.13,1.26) 1.12 (1.06,1.18)
# current-smoker 0.97 (0.89,1.06) 1.02 (0.93,1.11)
#
# imd_bd_q: ref.=least_deprived
# 2_deprived 0.9966 (0.9197,1.0801) 0.997 (0.9196,1.081)
# 3_deprived 1.07 (0.99,1.15) 1.08 (0.99,1.16)
# 4_deprived 1.03 (0.96,1.12) 1.05 (0.97,1.14)
# most_deprived 0.99 (0.91,1.07) 1.02 (0.93,1.1)
#
# regions: ref.=East Midlands
# London 0.89 (0.79,1) 0.92 (0.81,1.03)
# North East 1.02 (0.92,1.12) 1.03 (0.94,1.14)
# North West 1.18 (1.06,1.32) 1.17 (1.05,1.31)
# South East 0.94 (0.77,1.14) 0.99 (0.81,1.2)
# South West 1.05 (0.9,1.23) 1.08 (0.92,1.27)
# West Midlands 1 (0.88,1.13) 1.02 (0.9,1.16)
# Yorkshire and The Humber 0.98 (0.89,1.07) 0.99 (0.9,1.08)
# Wales 1.59 (1.44,1.76) 1.62 (1.46,1.79)
# Scotland 0.73 (0.66,0.81) 0.76 (0.68,0.84)
#
# season_c: ref.=2_Summer
# 1_Spring 1 (0.94,1.07) 0.97 (0.9,1.04)
# 3_Autumn 0.96 (0.89,1.03) 0.96 (0.89,1.03)
# 4_Winter 1.023 (0.9523,1.099) 0.992 (0.9207,1.0689)
#
# asthma 1.21 (1.13,1.29) 1.12 (1.05,1.2)
#
# ckd: CKD vs No CKD 1.85 (1.51,2.28) 1.41 (1.14,1.74)
#
# copd: COPD vs No COPD 1.64 (1.45,1.84) 1.28 (1.13,1.45)
#
# depress: depression vs No depression 1.09 (1.01,1.17) 1.07 (0.99,1.15)
#
# dm: Have DM vs No DM 1.16 (1.05,1.28) 1.03 (0.93,1.14)
#
# ibd: Inflammatory bowel disease vs No inflammatory bowel diseaase 1.44 (1.27,1.64) 1.29 (1.13,1.47)
#
# ra: RA vs No RA 1.74 (1.48,2.04) 1.24 (1.05,1.47)
#
# sle: SLE vs No SLE 1.71 (1.09,2.68) 1.29 (0.82,2.03)
#
# immunosuppression: Immunosuppression vs Not immunosuppressive 1.96 (1.76,2.18) 1.57 (1.4,1.76)
#
# P(Wald's test) P(LR-test)
# vd_prescription: Had vitamin D prescription vs No vitamin D prescription 0.289 0.294
#
# sex < 0.001 < 0.001
#
# age_c (cont. var.) < 0.001 < 0.001
#
# ethnic: ref.=1_white 0.825
# 2_mixed 0.732
# 3_asian 0.557
# 4_black 0.354
# 5_chinese 0.756
# 6_others 0.383
#
# bmi_group: ref.=1_healthy weight 0.597
# 0_underweight 0.702
# 2_over weight 0.476
# 3_obese 0.577
#
# drink_freq_c: ref.=never 0.273
# sometimes 0.882
# weekly 0.524
# daily 0.503
#
# smoke_stat: ref.=non-smoker < 0.001
# ex-smoker < 0.001
# current-smoker 0.703
#
# imd_bd_q: ref.=least_deprived 0.227
# 2_deprived 0.942
# 3_deprived 0.072
# 4_deprived 0.213
# most_deprived 0.723
#
# regions: ref.=East Midlands < 0.001
# London 0.152
# North East 0.514
# North West 0.005
# South East 0.894
# South West 0.328
# West Midlands 0.792
# Yorkshire and The Humber 0.739
# Wales < 0.001
# Scotland < 0.001
#
# season_c: ref.=2_Summer 0.578
# 1_Spring 0.36
# 3_Autumn 0.228
# 4_Winter 0.833
#
# asthma 0.001 0.001
#
# ckd: CKD vs No CKD 0.001 0.002
#
# copd: COPD vs No COPD < 0.001 < 0.001
#
# depress: depression vs No depression 0.084 0.087
#
# dm: Have DM vs No DM 0.57 0.571
#
# ibd: Inflammatory bowel disease vs No inflammatory bowel diseaase < 0.001 < 0.001
#
# ra: RA vs No RA 0.012 0.015
#
# sle: SLE vs No SLE 0.27 0.289
#
# immunosuppression: Immunosuppression vs Not immunosuppressive < 0.001 < 0.001
#
# Log-likelihood = -34519.0292
# No. of observations = 171349
# AIC value = 69122.0583
vd_drug_full <- vd_drug_full_glm %>%
tidy(conf.int = T, conf.level = 0.95, exponentiate = T, n_digits = 3) %>%
filter(term == "vd_prescriptionHad vitamin D prescription") %>%
dplyr::select(term, estimate, conf.low, conf.high)
vd_drug_full %<>% add_row(
term = "No_supp",
estimate = 1,
conf.low=1,
conf.high=1) # add reference
vd_drug_full$model <- "Fully adjusted"
# add N
vd_drug_full_n <- vd_drug_full_glm %>% model.matrix() %>%
colSums()%>% # show the total number in each level
as.data.frame() %>% t %>% as.data.frame() # transpose it make it easier to select
vd_drug_full %<>% mutate(N=case_when(
term == "No_supp" ~ (vd_drug_full_n$`(Intercept)`-vd_drug_full_n$`vd_prescriptionHad vitamin D prescription`),
term == "vd_prescriptionHad vitamin D prescription" ~ vd_drug_full_n$`vd_prescriptionHad vitamin D prescription`
))
rm(vd_drug_full_n, vd_drug_full_glm) # clean
# combine data
fig3b_data <- bind_rows(vd_drug_crude, vd_drug_partial, vd_drug_full) # combine output tables
fig3b_data %>% write_csv("dataset_wd/fig3b_data.csv")
# fig3b_data <- read_csv("dataset_wd/fig3b_data.csv")
rm(list = ls(pattern = "drug"))
# data management for plotting: ----
fig3b_data <- fig3b_data %>%
mutate(conf.high2 = format(conf.high, nmall=2)) %>% # 2 digits strings
mutate(conf.low2 = format(conf.low, nmall=2)) %>% # 2 digits strings
mutate(conf.rr = format(estimate, nmall=2)) %>% # 2 digits strings
mutate(ci = paste(conf.low2, conf.high2, sep = ",")) %>% # lci, hci
mutate(ci_l = paste0("(", ci)) %>% # (lci, hci
mutate(ci_r = paste0(ci_l, ")")) %>% # (lci, hci)
mutate(rr = paste(conf.rr, ci_r, sep = " ") ) %>% # rr (lci, hci)
mutate(RR = ifelse(estimate==1.00, "1 (Reference)",rr)) %>% # set 1.0 into 1 (reference)
mutate(vd_intake=case_when(
term == "vd_prescriptionHad vitamin D prescription" ~ "Receiving prescriptions",
term == "No_supp" ~ "No prescriptions")) %>% # rename for labelling
mutate(model_order=case_when(
model == "Crude" ~ 1,
model == "Partially adjusted" ~ 2,
model == "Fully adjusted" ~3)) %>% # for facet order
mutate(Model = ifelse(vd_intake =="No prescriptions",
model, NA)) %>% # for table labelling
arrange( model_order, # arrange order by vd status
match(vd_intake, c("No prescriptions", "Receiving prescriptions"))) %>%
dplyr::select(model_order, Model, vd_intake,N , estimate, conf.low, conf.high, RR)
# deal with factors issues:
fig3b_data[,1:3] <- map(fig3b_data[,1:3], as.factor) # factor coercion
fig3b_data$model_order %<>% factor(
labels = c("Crude", "Partially adjusted", "Fully adjusted")) # labelled for facet
fig3b_data$vd_intake %<>% relevel(ref = "Receiving prescriptions")
fig3b_data$order <- 6:1
# Fig3b: forest plot ----
fig3b <- fig3b_data %>%
ggplot(aes(y = order, x = estimate,
xmin=conf.low, xmax=conf.high, color=vd_intake)) +
xlim(0.8,1.8)+
geom_point(size=5, shape=19) +
geom_errorbarh(height=.3) +
geom_vline(xintercept=1, lty=2) +
ggtitle(" ")+
theme_classic2() +
theme(plot.title = element_text(size = 20 ,lineheight=.001),
text = element_text(size = 20),
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background = element_blank(),
strip.text.y = element_blank(),
legend.position = "none",
axis.title.x = element_text(colour = "black", size = 20),
axis.title.y = element_blank(),
axis.text.x = element_text(colour = "black", size = 20),
axis.text.y = element_blank())+ xlab("Rate ratio")
fig3b
# fig3b: left table ----
fig3b_table_l <- fig3b_data %>%
ggplot(aes(y =order)) + xlim(0,2) +
geom_text(aes(x = 0, label = Model ),lineheight = 0.001,
hjust = 0, size = 6, colour = "black") +
geom_text(aes(x = 0.8, label = vd_intake),lineheight = 0.001,
hjust = 0 ,size = 6, colour = "black") +
geom_text(aes(x = 1.8, label = N),lineheight = 0.001,
hjust = 0 ,size = 6, colour = "black") +
theme_classic2() +
ggtitle(" Model Vitamin D intake N")+ # use title for header
theme(plot.title = element_text(size = 20 ,lineheight=.001, face="bold"),
strip.text.x = element_blank(),
strip.text.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title.x = element_text(colour = "white", size = 22), # make the text invisible
axis.title.y = element_blank(),
axis.line = element_line(colour = "white"), # make the lines invisible
axis.text.x = element_text(colour = "white", size = 22), # make the text invisible
axis.text.y = element_blank(),
axis.ticks = element_blank()) + xlab(" ")
fig3b_table_l
# fig3b: right table ----
fig3b_table_r <- fig3b_data %>%
ggplot(aes(y = order)) + xlim(0,0.5) +
geom_text(aes(x = 0, label = RR),lineheight = 0.01, hjust = 0, size = 6, colour = "black") +
theme_classic2( ) +
ggtitle(" RR(95%CI)")+
theme(plot.title = element_text(size = 20 ,lineheight=.001, face="bold"),
strip.text.x = element_blank(),
strip.text.y = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title.x = element_text(colour = "white", size = 22),# make the text invisible
axis.title.y = element_blank(),
axis.text.x = element_text(colour = "white", size = 22), # make the text invisible
axis.text.y = element_blank(),
axis.ticks = element_blank(),
axis.line = element_line(colour = "white"), # make the lines invisible
panel.background=element_blank(),
plot.background=element_blank()) + xlab(" ")
fig3b_table_r
# Fig3b: combine tables and forestplot
fig3b_combine <- grid.arrange(fig3b_table_l, fig3b, fig3b_table_r, ncol = 3)
# W:1800 H:400
# Combine two Figures using ggpubr ---
ggarrange(fig3a_combine, fig3b_combine,
labels = c("a", "b"),
ncol = 1, nrow = 2,
align = "v",
widths = 1)
# output size: W: 1500 H:1000