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01_analysis_final_first_paper_revision.Rmd
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---
title: "PHOSP-COVID report"
author: "PHOSP-COVID collaborative"
output:
word_document:
toc: no
toc_depth: '3'
html_document:
code_folding: hide
toc: yes
toc_depth: 3
toc_float: yes
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
if (knitr::is_html_output()){
knitr::opts_chunk$set(echo = TRUE,
warning = FALSE,
message = FALSE)
} else {
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
message = FALSE)
}
library(finalfit)
library(knitr)
library(tidyverse)
library(rmarkdown)
summary_factorlist <- purrr::partial(finalfit::summary_factorlist, na_to_prop = FALSE)
```
```{r eval=FALSE, include=FALSE}
# For exploration
library(readr) # loaded with tidyverse anyway
datadir = "/home/common/phosp/raw/"
timestamp = "2021-09-08_1203"
phosp = read_rds(paste0(datadir, "phosp_", timestamp, ".rds"))
lastrun = Sys.time()
source("/home/eharrison/phosp_clean/02_functions.R")
source("/home/eharrison/phosp_clean/03_prep.R")
source("/home/eharrison/phosp_clean/05_kco.R")
save.image(paste("full_image_", timestamp, ".Rdata"), compress = TRUE)
```
```{r}
timestamp = "2021-09-08_1203"
load(paste("full_image_", timestamp, ".Rdata"))
```
### Tier 2 cohort with < 240 days follow-up
```{r}
# Filter at 240 days follow up.
## It would have been more correct to use the date: crf3b_date_visit or psq_date
## But after discussion left as is.
study_id_240 = phosp %>%
group_by(study_id) %>%
fill(crf1a_date_dis, crf3a_visit_date, .direction = "downup") %>%
select(study_id, crf1a_date_dis, crf3a_visit_date) %>%
distinct(study_id, .keep_all = TRUE) %>%
mutate(discharge2review = (crf3a_visit_date - crf1a_date_dis) %>% as.numeric()) %>%
filter(discharge2review <= 240 | is.na(discharge2review)) %>%
pull(study_id)
```
All analyses below are for tier 2 patients discharged on or before 30th Nov 2020 who have respiratory support data.
Added: remove those with missing sex/gender.
```{r}
keep = phosp_hosp %>%
filter(tier == 2) %>%
# filter(study_id %in% study_id_before_end_nov) %>%
filter(study_id %in% study_id_240) %>%
drop_na(crf1a_resp_support_4levels) %>%
filter(!is.na(crf1a_sex)) %>%
filter(study_id != "28-31") %>% # Temp remove as no 3 m data coming through
pull(study_id)
phosp_hosp = phosp_hosp %>%
filter(study_id %in% keep)
phosp_3m = phosp_3m %>%
left_join(phosp_hosp %>% select(study_id, crf1a_resp_support_4levels)) %>%
filter(study_id %in% keep)
phosp = phosp %>%
filter(study_id %in% keep)
phosp_wt = phosp_wt %>%
filter(study_id %in% keep)
```
```{r}
# Any joins can be temporarily done here, then moved to cleaning.
phosp_hosp = phosp_hosp %>%
left_join(phosp %>%
filter(redcap_event_name == "3 Months (1st Research Visit)") %>%
select(study_id, crf3a_bmi, crf3a_bmi_2levels, crf3a_bmi_5levels) %>%
drop_na(crf3a_bmi)
)
```
```{r}
# Temp variable changes / collapses
## Pull psq_recovered from 6 week data if missing at 3 months
phosp_hosp = phosp_hosp %>%
left_join(phosp_3m %>% select(study_id, psq_recovered, eq5d5l_summary_delta, discharge_to_3m_review)) %>%
left_join(phosp_6w %>% select(study_id,
psq_recovered_6w = psq_recovered,
eq5d5l_summary_delta_6w = eq5d5l_summary_delta)) %>%
mutate(
psq_recovered = case_when(
is.na(psq_recovered) & !is.na(psq_recovered_6w) ~ psq_recovered_6w,
TRUE ~ psq_recovered
) %>% fct_recode("No" = "Not sure") %>%
fct_relevel("No"),
eq5d5l_summary_delta = case_when(
is.na(eq5d5l_summary_delta) & !is.na(eq5d5l_summary_delta_6w) ~ eq5d5l_summary_delta_6w,
TRUE ~ eq5d5l_summary_delta)
) %>%
ff_relabel_df(phosp_hosp)
phosp_3m = phosp_3m %>%
select(-psq_recovered, -eq5d5l_summary_delta) %>%
left_join(phosp_hosp %>% select(study_id, psq_recovered, eq5d5l_summary_delta)) %>%
ff_relabel_df(phosp_3m)
```
```{r}
# Smoking
phosp_smoking = phosp_3m %>%
select(study_id, patient_sq_n) %>%
left_join(phosp_6w %>% select(study_id, patient_sq_n_6w = patient_sq_n)) %>%
mutate(patient_sq_n= case_when(
is.na(patient_sq_n) & !is.na(patient_sq_n_6w) ~ patient_sq_n_6w,
TRUE ~ patient_sq_n
) %>%
ff_label("Smoking")
) %>%
select(study_id, patient_sq_n)
phosp_hosp = phosp_hosp %>%
left_join(phosp_smoking) %>%
mutate(patient_sq_n = ff_label(patient_sq_n, "Smoking"))
```
## Table 1: Comparison of participant demographics, pre-existing co-morbidities, and admission characteristics stratified by severity of acute illness.
NOTE: discharge to review updated here. Other variables should be the same.
```{r}
explanatory = c("swab_pcr_result",
"discharge_to_3m_review",
"age_admission",
"crf1a_sex",
"crf1b_eth_5levels",
"imd_quintile",
"crf3a_bmi",
"crf3a_bmi_2levels",
"crf3a_bmi_5levels",
"patient_sq_n",
# Comorbidities
"no_comorbid",
"no_comorbid_3levels",
"crf1a_com_card", "crf1a_com_neupsy",
"crf1a_com_res", "crf1a_com_rheu",
"crf1a_com_gast", "crf1a_com_mer",
"crf1a_com_mh", "crf1a_com_diab",
"crf1a_com_id",
"crf1a_admission_duration",
"crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac")
dependent = "crf1a_resp_support_4levels"
phosp_hosp %>%
summary_factorlist(dependent, explanatory,
na_include = TRUE, na_include_dependent = TRUE,
add_col_totals = TRUE, cont_nonpara = c(2, 4:20), p = TRUE, digits = c(1, 1, 4, 1),
total_col = TRUE) %>%
ff_remove_ref() %>%
ff_column_totals(phosp_hosp, dependent) %>%
ff_row_totals(phosp_hosp, dependent, explanatory, missing_column = FALSE) %>%
mytable()
```
```{r}
phosp_hosp %>%
group_by(crf1a_resp_support_4levels) %>%
filter(is.na(age_admission)) %>%
count(age_admission)
phosp_hosp %>%
group_by(crf1a_resp_support_4levels) %>%
filter(is.na(crf1a_admission_duration)) %>%
count(age_admission)
```
## Table 2: Proportion unchanged, worse or better in terms of overall visual analogue score and EQ5D prior to hospitalisation and at follow-up stratified by severity of acute illness.
NOTE: psq_recovered numbers updated here.
```{r}
dependent = "crf1a_resp_support_4levels"
explanatory_differences = c("psq_recovered",
"eq5d5l_summary_pre",
"eq5d5l_summary",
"eq5d5l_summary_delta",
"eq5d5l_summary_delta_change",
# Pull delta_change names
phosp %>%
select(matches("eq5d5l_q.*_delta_change")
) %>%
names(),
"eq5d5l_utility_index_pre",
"eq5d5l_utility_index",
"eq5d5l_utility_index_delta",
"patient_sq_l_t_disability",
"patient_sq_l_t__new_disability")
phosp_3m %>%
left_join(phosp_hosp %>% select(study_id, crf1a_resp_support_4levels)) %>%
summary_factorlist(dependent, explanatory_differences,
na_include = TRUE, na_include_dependent = TRUE,
add_col_totals = TRUE, digits = c(2, 2, 4, 1),
total_col = TRUE, p = TRUE) %>%
mytable()
```
## Table 3: Health-related quality of life, physiological and functional status at follow-up stratified for severity of acute illness.
```{r}
phosp_wt_table = phosp_wt %>%
filter(wt_type == "Incremental shuttle walk test (ISWT) Modified 15 level") %>%
filter(wt_visit == "3 months") %>%
drop_na(wt_distance) %>%
mutate(wt_iswt_predicted_perc = replace_na(wt_iswt_predicted_perc, 0)) %>% # to deal with max returning inf
group_by(study_id) %>% # More than one test per patient
mutate(value_max = if_else(row_number() == which.max(wt_iswt_predicted_perc), TRUE, FALSE)) %>%
filter(value_max) %>%
select(study_id, wt_distance, wt_iswt_predicted_perc) %>%
as.data.frame() %>%
ff_relabel_df(phosp_wt)
phosp_pft_table = phosp %>%
filter(redcap_event_name== "3 Months (1st Research Visit)") %>%
filter(redcap_repeat_instrument == "Pulmonary Functional Tests") %>%
filter(pft_spm_done == "Yes") %>%
select(study_id, starts_with("pft_")) %>%
drop_na(pft_fev1) %>%
group_by(study_id) %>%
mutate(value_max = if_else(row_number() == which.max(pft_fev1), TRUE, FALSE)) %>%
filter(value_max) %>%
select("pft_fev1_perc_pred",
"pft_fev1",
"pft_fev1_perc_pred_80",
"pft_fvc_perc_pred",
"pft_fvc",
"pft_fvc_perc_pred_80",
"pft_fev1_fvc",
"pft_fev1_fvc_70") %>%
as.data.frame() %>%
ff_relabel_df(phosp)
# Do kco and tlco extraction and percentage normal separately.
phosp_kco_table = phosp %>%
filter(redcap_event_name== "3 Months (1st Research Visit)") %>%
filter(redcap_repeat_instrument == "Pulmonary Functional Tests") %>%
filter(pft_spm_done == "Yes") %>%
select(study_id, pft_tlco, pft_kco) %>%
drop_na(pft_kco) %>%
left_join(phosp_kco %>% select(study_id, tlcoM_SI_pred, kcoM_SI_pred)) %>%
mutate(
pft_tlco_pred = (100 * pft_tlco / tlcoM_SI_pred) %>% ff_label("TLCO % predicted"),
pft_kco_pred = (100 * pft_kco / kcoM_SI_pred) %>% ff_label("KCO % predicted"),
pft_tlco_pred_80 = case_when(
pft_tlco_pred < 80 ~ "Yes",
pft_tlco_pred >= 80 ~ "No",
) %>%
ff_label("TLCO predicted <80%"),
pft_kco_pred_80 = case_when(
pft_kco_pred < 80 ~ "Yes",
pft_kco_pred >= 80 ~ "No",
) %>%
ff_label("KCO predicted <80%"),
) %>%
ff_relabel_df(phosp)
phosp_pft_table = phosp_pft_table %>%
left_join(phosp_kco_table)
```
```{r}
phosp_bloods_table = phosp %>%
filter(redcap_event_name== "3 Months (1st Research Visit)") %>%
filter(redcap_repeat_instrument == "Laboratory Results Collection Log - Routine blood tests") %>%
group_by(study_id) %>%
summarise(
# This needs to summarise over multiple blood tests per visit.
# Take max values and yes over no.
# Convoluted by necessity.
# note case_when doesn't currently work with any. Known issue Feb 21
bnp_result = ifelse(all(is.na(bnp_result)), NA, max(bnp_result, na.rm = TRUE)),
pnbnp_result = ifelse(all(is.na(pnbnp_result)), NA, max(pnbnp_result, na.rm = TRUE)),
bnp_summary = ifelse(any(bnp_summary == "Yes", na.rm = TRUE), "Yes",
ifelse(any(bnp_summary == "No", na.rm = TRUE), "No",
NA)),
hba1c_result = ifelse(all(is.na(hba1c_result)), NA, max(hba1c_result, na.rm = TRUE)),
hba1c_summary = ifelse(any(hba1c_summary == "Yes", na.rm = TRUE), "Yes",
ifelse(any(hba1c_summary == "No", na.rm = TRUE), "No",
NA)),
egfr_result = ifelse(all(is.na(egfr_result)), NA, max(egfr_result, na.rm = TRUE)),
egfr_summary = ifelse(any(egfr_summary == "Yes", na.rm = TRUE), "Yes",
ifelse(any(egfr_summary == "No", na.rm = TRUE), "No",
NA)),
ddi_result = ifelse(all(is.na(ddi_result)), NA, max(ddi_result, na.rm = TRUE)),
ddi_summary = ifelse(any(ddi_summary == "Yes", na.rm = TRUE), "Yes",
ifelse(any(ddi_summary == "No", na.rm = TRUE), "No",
NA)),
crp_result = ifelse(all(is.na(crp_result)), NA, max(crp_result , na.rm = TRUE)),
crp_summary = ifelse(any(crp_summary == "Yes", na.rm = TRUE), "Yes",
ifelse(any(crp_summary == "No", na.rm = TRUE), "No",
NA)),
) %>%
ff_relabel_df(phosp)
# Added 28/07/2021
## CRP>5
phosp_bloods_table = phosp_bloods_table %>%
mutate(
crp_summary5 = case_when(
crp_result > 5 ~ "Yes",
crp_result <= 5 ~ "No"
) %>%
ff_label("CRP (>5 mg/L)")
)
explanatory_hrqol = c(
"symptom_any",
"symptom_count",
"gad7_summary_2levels",
"phq9_summary_2levels",
"pcl5_summary_2levels",
"dyspnoea12_summary",
#"facit_v4_summary",
"facit_item_total",
"bpi_severity_summary",
"bpi_interference_summary",
"sppb_score",
"sppb_score_summary",
"wt_distance",
"wt_iswt_predicted_perc",
"rcf_score_summary",
"mocal_total_summary",
"mocal_total_corrected_summary",
"pft_fev1_perc_pred",
"pft_fev1_perc_pred_80",
"pft_fev1_fvc_70",
"pft_fvc_perc_pred_80",
"pft_tlco",
"pft_tlco_pred",
"pft_tlco_pred_80",
"pft_kco",
"pft_kco_pred",
"pft_kco_pred_80",
"bnp_summary",
"hba1c_summary",
"egfr_summary",
"ddi_result",
"ddi_summary",
"crp_summary5",
"crp_summary"
)
phosp_3m %>%
left_join(phosp_wt_table) %>%
left_join(phosp_pft_table) %>%
left_join(phosp_bloods_table) %>%
left_join(phosp_hosp %>% select(study_id, crf1a_resp_support_4levels)) %>%
select(dependent, explanatory_hrqol) %T>%
{data_in <<- .} %>%
summary_factorlist(dependent, explanatory_hrqol,
na_include = TRUE, na_include_dependent = TRUE,
total_col = TRUE, cont_nonpara = c(2),
add_col_totals = TRUE, add_row_totals = TRUE, include_row_missing_col = FALSE,
p = TRUE, digits = c(1, 1, 4, 1)) %>%
mytable()
```
## Table 4: Occupation status prior to hospitalisation and at follow-up stratified by acute illness
```{r}
explanatory_occupation = c(
# "crf1b_healthcare_worker", "crf1b_caring",
"social_status_before", # "social_status_after",
"working_status_change", "working_less",
"no_longer_working", "health_reasons",
"employer_reasons", "other_reasons"
# "patient_sq_q", "patient_sq_q_today", "patient_sq_q_change",
# "patient_sq_p_smoking", "patient_sq_p_drinking",
# "patient_sq_p_eating", "patient_sq_p_activity"
)
phosp_3m %>%
left_join(phosp_hosp %>% select(study_id, crf1a_resp_support_4levels, crf1b_healthcare_worker, crf1b_caring)) %>%
summary_factorlist(dependent, explanatory_occupation,
na_include = TRUE, na_include_dependent = TRUE,
total_col = TRUE,
add_col_totals = TRUE, add_row_totals = TRUE, include_row_missing_col = FALSE,
p = TRUE, digits = c(1, 1, 4, 1)) %>%
mytable()
```
## Table 5: Characteristics associated with recovery versus persistent symptoms and disability
```{r}
dependent = "psq_recovered"
phosp_hosp %>%
summary_factorlist(dependent, explanatory,
na_include = TRUE, na_include_dependent = TRUE,
add_col_totals = TRUE, cont_nonpara = c(2, 4:20),
total_col = TRUE) %>%
ff_remove_ref() %>%
ff_column_totals(phosp_hosp, dependent) %>%
dependent_label(phosp_hosp, dependent) %>%
mytable()
```
```{r}
## Multiple imputation
library(mice)
library(finalfit)
library(purrr)
```
```{r eval=FALSE, include=FALSE}
# Run mice
set.seed(1)
explanatory = c("age_admission",
"crf1a_sex",
"crf1b_eth_5levels",
"imd_quintile",
"crf3a_bmi",
"crf1a_resp_support_4levels",
# Comorbidities - # None of these are imputed as missing considered absent
"no_comorbid",
"no_comorbid_3levels",
"crf1a_com_card", "crf1a_com_neupsy",
"crf1a_com_res", "crf1a_com_rheu",
"crf1a_com_gast", "crf1a_com_mer",
"crf1a_com_mh", "crf1a_com_diab",
"crf1a_com_id",
"crf1a_admission_duration",
"crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac",
"psq_recovered",
"study_id",
"redcap_data_access_group",
"discharge_to_3m_review")
# Choose not to impute missing values
# for explanatory variable of interest and
# outcome variable.
# But include in algorithm for imputation.
predM = phosp_hosp %>%
select(explanatory) %>%
missing_predictorMatrix(
drop_from_imputed = c("study_id"),
drop_from_imputer = c("study_id", "redcap_data_access_group", "discharge_to_3m_review"))
# This is needed to drop from imputed!
m0 = mice(phosp_hosp %>%
select(explanatory), maxit=0)
m0$method[c("study_id")] = ""
phosp_hosp_imputed = phosp_hosp %>%
select(explanatory) %>%
mice(m = 10, maxit = 10, predictorMatrix = predM, method = m0$method)
plot(phosp_hosp_imputed)
summary(phosp_hosp_imputed)
saveRDS(phosp_hosp_imputed, file = paste0("phosp_hosp_imputed_", timestamp, ".rds"))
```
```{r}
# Load mice sets
phosp_hosp_imputed = readRDS(paste0("phosp_hosp_imputed_", timestamp, ".rds"))
```
## Table 6A: Original: Multivariable logistic regression (with BMI 2 levels WIHOUT discharge_to_review)
### REVISED
```{r}
## Decide about metrcs later.
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac")
explanatory_multi = c("age_admission", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels")
fit_cc = phosp_hosp %>%
finalfit(dependent, explanatory, keep_fit_id = TRUE)
fit_multi_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)")
)
) %>%
purrr::map(~ glmmulti(.x, dependent, explanatory)) %>%
as.mira() %>%
pool()
# Multilevel
library(broom.mixed)
fit_mixed_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)"))
) %>%
purrr::map(~ glmmixed(.x, dependent, explanatory, random_effect = "redcap_data_access_group")) %>%
as.mira() %>%
pool()
fit_multi_imputed = fit_multi_imputed_pool%>%
fit2df(estimate_name = "OR (multivariable imputation)", exp = TRUE)
fit_mixed_imputed = fit_mixed_imputed_pool%>%
fit2df(estimate_name = "OR (multilevel imputation)", exp = TRUE)
fit_cc %>%
# ff_merge(fit_uni_imputed) %>%
ff_merge(fit_multi_imputed) %>%
ff_merge(fit_mixed_imputed, last_merge = TRUE) %>%
# dplyr::relocate(1, 2, 3, 4, 5, 7, 6, 8) %>%
mytable()
```
## Figure: Multivariable logistic regression (multiple imputation) (with BMI 2 level WIHHOUT discharge_to_review)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
## New figure without abx and anticoag: Multivariable logistic regression (multiple imputation) (with BMI 2 level WIHHOUT discharge_to_review)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
## Table 6B: Multivariable logistic regression (with BMI 2 levels AND discharge_to_review)
### REVISED
```{r}
## Decide about metrcs later.
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac", "discharge_to_3m_review")
explanatory_multi = c("age_admission", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels")
fit_cc = phosp_hosp %>%
finalfit(dependent, explanatory, keep_fit_id = TRUE)
fit_multi_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)")
)
) %>%
purrr::map(~ glmmulti(.x, dependent, explanatory)) %>%
as.mira() %>%
pool()
# Multilevel
library(broom.mixed)
fit_mixed_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)"))
) %>%
purrr::map(~ glmmixed(.x, dependent, explanatory, random_effect = "redcap_data_access_group")) %>%
as.mira() %>%
pool()
fit_multi_imputed = fit_multi_imputed_pool%>%
fit2df(estimate_name = "OR (multivariable imputation)", exp = TRUE)
fit_mixed_imputed = fit_mixed_imputed_pool%>%
fit2df(estimate_name = "OR (multilevel imputation)", exp = TRUE)
fit_cc %>%
# ff_merge(fit_uni_imputed) %>%
ff_merge(fit_multi_imputed) %>%
ff_merge(fit_mixed_imputed, last_merge = TRUE) %>%
# dplyr::relocate(1, 2, 3, 4, 5, 7, 6, 8) %>%
mytable()
```
## Figure: Multivariable logistic regression (multiple imputation) (with BMI 2 level)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac", "discharge_to_3m_review")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
## New figure: Multivariable logistic regression (multiple imputation) (with BMI 2 level)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "discharge_to_3m_review")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
# Models with out imputing outcome (for information, suggest not using)
```{r eval=FALSE, include=FALSE}
# Run mice
set.seed(1)
explanatory = c("age_admission",
"crf1a_sex",
"crf1b_eth_5levels",
"imd_quintile",
"crf3a_bmi",
"crf1a_resp_support_4levels",
# Comorbidities - # None of these are imputed as missing considered absent
"no_comorbid",
"no_comorbid_3levels",
"crf1a_com_card", "crf1a_com_neupsy",
"crf1a_com_res", "crf1a_com_rheu",
"crf1a_com_gast", "crf1a_com_mer",
"crf1a_com_mh", "crf1a_com_diab",
"crf1a_com_id",
"crf1a_admission_duration",
"crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac",
"psq_recovered",
"study_id",
"redcap_data_access_group",
"discharge_to_3m_review")
# Choose not to impute missing values
# for explanatory variable of interest and
# outcome variable.
# But include in algorithm for imputation.
predM = phosp_hosp %>%
select(explanatory) %>%
missing_predictorMatrix(
drop_from_imputed = c("study_id", "psq_recovered"),
drop_from_imputer = c("study_id", "redcap_data_access_group", "discharge_to_3m_review"))
# This is needed to drop from imputed!
m0 = mice(phosp_hosp %>%
select(explanatory), maxit=0)
m0$method[c("study_id", "psq_recovered")] = ""
phosp_hosp_imputed = phosp_hosp %>%
select(explanatory) %>%
mice(m = 10, maxit = 10, predictorMatrix = predM, method = m0$method)
plot(phosp_hosp_imputed)
summary(phosp_hosp_imputed)
saveRDS(phosp_hosp_imputed, file = paste0("phosp_hosp_imputed_", timestamp, "_2.rds"))
```
```{r}
# Load mice sets
phosp_hosp_imputed = readRDS(paste0("phosp_hosp_imputed_", timestamp, "_2.rds"))
```
## Table 6C: Multivariable logistic regression (with BMI 2 levels WIHOUT discharge_to_review, outcome not imputed)
### REVISED
```{r}
## Decide about metrcs later.
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac")
explanatory_multi = c("age_admission", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels")
fit_cc = phosp_hosp %>%
finalfit(dependent, explanatory, keep_fit_id = TRUE)
fit_multi_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)")
)
) %>%
purrr::map(~ glmmulti(.x, dependent, explanatory)) %>%
as.mira() %>%
pool()
# Multilevel
library(broom.mixed)
fit_mixed_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)"))
) %>%
purrr::map(~ glmmixed(.x, dependent, explanatory, random_effect = "redcap_data_access_group")) %>%
as.mira() %>%
pool()
fit_multi_imputed = fit_multi_imputed_pool%>%
fit2df(estimate_name = "OR (multivariable imputation)", exp = TRUE)
fit_mixed_imputed = fit_mixed_imputed_pool%>%
fit2df(estimate_name = "OR (multilevel imputation)", exp = TRUE)
fit_cc %>%
# ff_merge(fit_uni_imputed) %>%
ff_merge(fit_multi_imputed) %>%
ff_merge(fit_mixed_imputed, last_merge = TRUE) %>%
# dplyr::relocate(1, 2, 3, 4, 5, 7, 6, 8) %>%
mytable()
```
## Figure: Multivariable logistic regression (multiple imputation) (with BMI 2 level WIHHOUT discharge_to_review)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
## New figure: Multivariable logistic regression (multiple imputation) (with BMI 2 level WIHHOUT discharge_to_review)
```{r fig.height=7, fig.width=12}
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss")
fit = phosp_hosp %>%
or_plot(dependent, explanatory, column_space = c(-0.5, 0.15, 0.5), table_text_size = 4,
glmfit = fit_mixed_imputed_pool,
dependent_label = "", suffix = "",
breaks = c(0.2, 0.5, 1, 2, 5, 10, 30), )
# PDF 10 x 9
```
## Table 6D: Multivariable logistic regression (with BMI 2 levels AND discharge_to_review, outcome not imputed)
### REVISED
```{r}
## Decide about metrcs later.
dependent = "psq_recovered"
explanatory = c("age_admission_factor", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels", "crf3a_bmi_2levels", "crf1a_resp_support_4levels", "crf1a_treat_ss", "crf1a_treat_at", "crf1a_treat_tdac", "discharge_to_3m_review")
explanatory_multi = c("age_admission", "crf1a_sex", "crf1b_eth_5levels", "imd_quintile", "no_comorbid_3levels")
fit_cc = phosp_hosp %>%
finalfit(dependent, explanatory, keep_fit_id = TRUE)
fit_multi_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",
age_admission < 80 ~ "70-79",
is.na(age_admission) ~ NA_character_,
TRUE ~ "80+"
) %>%
fct_relevel("50-59"),
crf3a_bmi_2levels = case_when(
crf3a_bmi < 30 ~ "BMI < 30 kg/m^2",
is.na(crf3a_bmi) ~ NA_character_,
TRUE ~ "BMI 30+ kg/m^2"
) %>%
factor() %>%
fct_relevel("BMI < 30 kg/m^2", "BMI 30+ kg/m^2") %>%
ff_label("BMI (2 levels)")
)
) %>%
purrr::map(~ glmmulti(.x, dependent, explanatory)) %>%
as.mira() %>%
pool()
# Multilevel
library(broom.mixed)
fit_mixed_imputed_pool = phosp_hosp_imputed %>%
mice::complete("all") %>%
# Probably temp. Add age_admission_factor to imputed datasets
purrr::map(~ mutate(.,
age_admission_factor = case_when(
age_admission < 30 ~ "<30",
age_admission < 40 ~ "30-39",
age_admission < 50 ~ "40-49",
age_admission < 60 ~ "50-59",
age_admission < 70 ~ "60-69",