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Comparative_vaccine_effectiveness.R
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Comparative_vaccine_effectiveness.R
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# Load R packages ------------------------------------------------------------
library(tidyverse)
library(lubridate)
library(data.table) # for fread function
# Vaccination cohorts from GP EMIS prescription datasource (England) ----------------------------------
# England test results
covid19_result_england <-
fread( input = "D:/DPhil/UK_Biobank_opioid_application/COVID_test_results/Version_20211205/covid19_result_england.txt") %>%
mutate( specdate = lubridate::dmy( specdate))
#===========================the EMIS source===========================#
covid19_emis_gp_clinical <-
fread( input = "D:/DPhil/UK_Biobank_opioid_application/GP_covid_only/Version_20211209/covid19_emis_gp_clinical.txt") %>%
mutate( event_dt = lubridate::dmy(event_dt),
code = as.character(code))
print(object.size(covid19_emis_gp_clinical), units = "Gb")
covid19_emis_gp_scripts <-
fread( input = "D:/DPhil/UK_Biobank_opioid_application/GP_covid_only/Version_20211209/covid19_emis_gp_scripts.txt") %>%
mutate( issue_date = lubridate::dmy(issue_date),
code = as.character(code))
print(object.size(covid19_emis_gp_scripts), units = "Gb")
# UKBB England participants
Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation.rds")
England_participants_ID <- filter( Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation, !is.na(f.26410.0.0))
# vaccine_codes
vaccine_dm_codes <- c( "Janssen-Cilag Ltd" = "39230211000001104",
"Moderna, Inc" = "39326911000001101",
"Medicago Inc" = "39826711000001101",
"Baxter Oncology GmbH" = "39473011000001103",
"AstraZeneca UK Ltd" = "39114911000001105",
"Valneva UK Ltd" = "39373511000001104",
"Pfizer Ltd" = "39115611000001103")
vaccine_cohort_extract_func <- function( vaccine_code_list){
vaccine_cohort_emis_scripts <-
covid19_emis_gp_scripts %>%
filter( code %in% vaccine_code_list) %>%
rename( dmd_code = code) %>%
select( -code_type) %>% #attention: different coding systems
mutate( dmd_code = factor( dmd_code,
levels = c("39230211000001104", "39326911000001101", "39826711000001101", "39473011000001103", "39114911000001105", "39373511000001104", "39115611000001103"),
labels = c("Janssen", "Moderna", "Medicago", "Baxter", "AstraZeneca", "Valneva","Pfizer")))
return( vaccine_cohort_emis_scripts)
}
vaccine_cohort_raw <- vaccine_cohort_extract_func( vaccine_code_list = vaccine_dm_codes)
vaccine_cohort_raw %>% summarise( across( .cols = everything(), ~ sum(is.na(.)))) #missing value check
vaccine_cohort <-
vaccine_cohort_raw %>%
arrange( issue_date) %>%
group_by( eid) %>%
mutate( dose_seq = row_number()) %>%
ungroup() %>%
select( eid, dmd_code, issue_date, dose_seq) %>%
pivot_wider( id_cols = eid,
names_from = dose_seq,
values_from = c( issue_date, dmd_code))
table(vaccine_cohort_raw$dmd_code)
write_rds( vaccine_cohort_raw, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/NC_second_round/vaccine_cohort_raw.rds")
write_rds( vaccine_cohort, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/NC_second_round/vaccine_cohort.rds")
# Demographic information from recruitment survey ----------------------------------------------
Population_characteristics___Baseline_characteristics <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/Population_characteristics___Baseline_characteristics.rds")
Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation.rds")
UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Education <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Education.rds")
UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Ethnicity <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Ethnicity.rds")
UK_Biobank_Assessment_Centre___Physical_measures___Anthropometry___Body_size_measures <- readRDS("D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/R_datasets/Main_dataset/UK_Biobank_Assessment_Centre___Physical_measures___Anthropometry___Body_size_measures.rds")
UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Ethnicity %>%
summarise( across( .cols = everything(), ~ mean(is.na(.))*100))
# extract sex and age
baseline_sex_birth <-
Population_characteristics___Baseline_characteristics %>%
select( f.eid, f.31.0.0, f.34.0.0, f.189.0.0) %>%
rename( sex = f.31.0.0, birth_year = f.34.0.0, downsend_deprivation_index = f.189.0.0)
# extract arthropometry such as BMI
baseline_BMI <-
UK_Biobank_Assessment_Centre___Physical_measures___Anthropometry___Body_size_measures %>%
select( f.eid, f.21001.0.0) %>%
rename( BMI = f.21001.0.0)
# extract Multiple Deprivation
baseline_Index_of_Multiple_Deprivation <-
Population_characteristics___Baseline_characteristics___Indices_of_Multiple_Deprivation %>%
select( f.eid, f.26410.0.0:f.26416.0.0) %>%
rename( IMD = f.26410.0.0,
IMD_income_score = f.26411.0.0,
IMD_employment_score = f.26412.0.0,
IMD_health_score = f.26413.0.0,
IMD_education_score = f.26414.0.0,
IMD_housing_score = f.26415.0.0,
IMD_crime_score = f.26416.0.0)
# extract Education
baseline_education <-
UK_Biobank_Assessment_Centre___Touchscreen___Sociodemographics___Education %>%
select( f.eid, f.6138.0.0) %>%
rename( Education = f.6138.0.0) %>%
mutate( Education = factor(Education, ordered = FALSE))
# extract Ethnicity
coding <- reader::reader("D:\\DPhil\\UK_Biobank_opioid_application\\Raw_main_dataset\\coding1001.tsv")
vbs_ext_1 <- colnames(fread("D:\\DPhil\\UK_Biobank_opioid_application\\Raw_main_dataset\\ukb45017.tab", header=TRUE, sep="\t", nrows=1 ))
selected_vars <-c("f.eid",
grep("f.21000.",vbs_ext_1, value = TRUE),
grep("f.26410.",vbs_ext_1, value = TRUE),
grep("f.31.",vbs_ext_1, value = TRUE),
grep("f.34.",vbs_ext_1, value = TRUE),
grep("f.52.",vbs_ext_1, value = TRUE))
extract1 <- fread("D:\\DPhil\\UK_Biobank_opioid_application\\Raw_main_dataset\\ukb45017.tab",
header=TRUE, sep="\t", select = selected_vars)
baseline_ethnicity <-
extract1 %>%
filter(!is.na(f.26410.0.0)) %>%
select(c(f.eid,f.21000.0.0,f.21000.1.0,f.21000.2.0)) %>%
mutate(f.21000.0.0=coalesce(f.21000.2.0,f.21000.1.0,f.21000.0.0)) %>%
left_join(coding, by = c("f.21000.0.0" = "coding")) %>%
left_join(coding, by = c("parent_id" = "coding")) %>%
mutate(ethnicity=coalesce(meaning.y,meaning.x)) %>%
select( f.eid, ethnicity)
write_rds( baseline_ethnicity, "D:/DPhil/UK_Biobank_opioid_application/Albert_deriviation/baseline_ethnicity.rds")
#combined baseline charateristics
recruitment_charateristics <-
reduce( list( baseline_sex_birth, baseline_BMI, baseline_Index_of_Multiple_Deprivation, baseline_education, baseline_ethnicity),
left_join, by = "f.eid")%>%
labelled::remove_var_label()
#write_rds( recruitment_charateristics, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/recruitment_charateristics.rds")
glimpse(Index_of_Multiple_Deprivation)
labelled::var_label( Index_of_Multiple_Deprivation)
# Hosptial admission number from HES --------------------------------------
hes_admission <-
hesin %>%
select( eid, admidate) %>%
arrange( eid, admidate) %>%
group_by( eid) %>%
distinct( admidate) %>%
ungroup()
write_rds( hes_admission, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/hes_admission.rds")
# COVID-19 test results ---------------------------------------------------
covid19_result_england %>% summarise( across( .cols = everything(), ~ sum(is.na(.))))#missing value check
#valid cohorts
first_dose_vaccine_cohort <- vaccine_cohort %>% filter( !is.na( dmd_code_1))
second_dose_vaccine_cohort <- vaccine_cohort %>% filter( !is.na( dmd_code_2))
#postive covid-19 testing results
All_covid_positive_result <- covid19_result_england %>% filter( result == 1)
# generate outcome index and follow-up days
first_dose_outcome_extract_func <- function( cohort , prop, lastest_update_date){
start_time <- Sys.time()
sample <- sample_frac( cohort, prop)
print(names( sample))
# link to covid test results and define follow-up period for the first dose cohort
sample <-
sample %>%
#attention: the same date of first and second dose
#attention: na.rm = true
mutate( max_follow_up_date = pmin( issue_date_1 + 7*14, issue_date_2, lastest_update_date, na.rm = TRUE) + 1)
# infection outcome
infection_outcome_cases <-
sample %>%
left_join( select( All_covid_positive_result, eid, specdate, result), by = "eid") %>%
filter( specdate > issue_date_1, specdate <= max_follow_up_date) %>%
arrange( specdate) %>%
group_by( eid) %>%
filter( row_number() == 1) %>%
select( eid, specdate, result)%>%
ungroup()
infection_cohort <-
sample %>%
left_join( infection_outcome_cases, by = "eid") %>%
group_by( eid) %>%
mutate( result = case_when( is.na( result) ~ as.integer(0), TRUE ~ result),
specdate = case_when( is.na( specdate) ~ max_follow_up_date, TRUE ~ specdate)) %>%
mutate( follow_up_days = (specdate - issue_date_1) + 1) %>%
ungroup()
#hospitalization outcome
hospitalization_outcome_cases <-
sample %>%
left_join( select( All_covid_positive_result, eid, specdate, result, origin), by = "eid") %>%
filter( specdate > issue_date_1, specdate <= max_follow_up_date, origin == 1) %>%
arrange( specdate) %>%
group_by( eid) %>%
filter( row_number() == 1) %>%
select( eid, specdate, result) %>%
ungroup()
hospitalization_cohort <-
sample %>%
left_join( hospitalization_outcome_cases, by = "eid") %>%
group_by( eid) %>%
mutate( result = case_when( is.na( result) ~ as.integer(0), TRUE ~ result),
specdate = case_when( is.na( specdate) ~ max_follow_up_date, TRUE ~ specdate)) %>%
mutate( follow_up_days = (specdate - issue_date_1) + 1) %>%
ungroup()
combined <- list( infection_event = infection_cohort,
hospitalization_event = hospitalization_cohort)
end_time <- Sys.time()
print(end_time - start_time)
return(combined)
}
second_dose_outcome_extract_func <- function( cohort , prop, lastest_update_date){
start_time <- Sys.time()
sample <- sample_frac( cohort, prop)
print(names( sample))
# link to covid test results and define follow-up period for the first dose cohort
sample <-
sample %>%
#attention: the same date of first and second dose
#attention: na.rm = true
#attention: no 14 weeks follow-up restriction for second dose
mutate( max_follow_up_date = pmin( issue_date_3, lastest_update_date, na.rm = TRUE) + 1)
# infection outcome
infection_outcome_cases <-
sample %>%
left_join( select( All_covid_positive_result, eid, specdate, result), by = "eid") %>%
filter( specdate > issue_date_2, specdate <= max_follow_up_date) %>%
arrange( specdate) %>%
group_by( eid) %>%
filter( row_number() == 1) %>%
select( eid, specdate, result)%>%
ungroup()
infection_cohort <-
sample %>%
left_join( infection_outcome_cases, by = "eid") %>%
group_by( eid) %>%
mutate( result = case_when( is.na( result) ~ as.integer(0), TRUE ~ result),
specdate = case_when( is.na( specdate) ~ max_follow_up_date, TRUE ~ specdate)) %>%
mutate( follow_up_days = (specdate - issue_date_2) + 1) %>%
ungroup()
#hospitalization outcome
hospitalization_outcome_cases <-
sample %>%
left_join( select( All_covid_positive_result, eid, specdate, result, origin), by = "eid") %>%
filter( specdate > issue_date_2, specdate <= max_follow_up_date, origin == 1) %>%
arrange( specdate) %>%
group_by( eid) %>%
filter( row_number() == 1) %>%
select( eid, specdate, result) %>%
ungroup()
hospitalization_cohort <-
sample %>%
left_join( hospitalization_outcome_cases, by = "eid") %>%
group_by( eid) %>%
mutate( result = case_when( is.na( result) ~ as.integer(0), TRUE ~ result),
specdate = case_when( is.na( specdate) ~ max_follow_up_date, TRUE ~ specdate)) %>%
mutate( follow_up_days = (specdate - issue_date_2) + 1) %>%
ungroup()
combined <- list( infection_event = infection_cohort,
hospitalization_event = hospitalization_cohort)
end_time <- Sys.time()
print(end_time - start_time)
return(combined)
}
first_dose_link_outcome_cohort <- first_dose_outcome_extract_func( cohort = first_dose_vaccine_cohort,
prop = 1,
lastest_update_date = as.Date("2021/10/18"))
second_dose_link_outcome_cohort <- second_dose_outcome_extract_func( cohort = second_dose_vaccine_cohort,
prop = 1,
lastest_update_date = as.Date("2021/10/18"))
write_rds( first_dose_link_outcome_cohort, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/NC_second_round/first_dose_link_outcome_cohort.rds")
write_rds( second_dose_link_outcome_cohort, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/NC_second_round/second_dose_link_outcome_cohort.rds")
# curate counting cox regression format
#library(magrittr)
library(survival)
first_dose_construct_data_func <- function( cohort, vaccine, outcome, follow_up){
sample <- cohort[[outcome]]
dose_base <-
sample %>%
select( eid, all_of(vaccine), result, specdate, .data[[follow_up]]) %>%
mutate( interval_total_number = case_when((.data[[follow_up]]/14) < 1 ~ 1,
(.data[[follow_up]]/14) >= 1 & (.data[[follow_up]]/14) < 3 ~ 2,
(.data[[follow_up]]/14) >= 3 & (.data[[follow_up]]/14) < 5 ~ 3,
#(.data[[follow_up]]/14) >= 5 & (.data[[follow_up]]/14) < 7 ~ 4,
# (.data[[follow_up]]/14) >= 4 & (.data[[follow_up]]/14) < 5 ~ 5,
# (.data[[follow_up]]/14) >= 5 & (.data[[follow_up]]/14) < 6 ~ 6,
(.data[[follow_up]]/14) >= 5 ~ 4))
# Create an index of the rows you want with duplications
temp_idx <- rep(1:nrow( dose_base), dose_base$interval_total_number)
# Use that index to genderate your new data frame
dose_base_dup <-
dose_base[temp_idx,] %>%
group_by( eid) %>%
mutate( interval_index = row_number(),
interval_days = case_when( interval_index == 1 ~ 0,
interval_index == 2 ~ 14,
interval_index == 3 ~ 42,
interval_index == 4 ~ 70))
dose_base_range <- tmerge( dose_base, dose_base, id = eid, outcome_status = event( dose_base[[follow_up]], result)) #set range
counting_process <- tmerge(data1 = dose_base_range,
data2 = dose_base_dup,
id = eid,
follow_cat = tdc( interval_days, interval_index)) %>%
mutate( follow_cat = as.character(follow_cat))
counting_process_ref <-
counting_process %>%
mutate( follow_cat_ref = case_when( .data[[vaccine]] == "AstraZeneca" ~ "0", TRUE ~ follow_cat))
return( counting_process_ref)
}
second_dose_construct_data_func <- function( cohort, vaccine, outcome, follow_up){
sample <- cohort[[outcome]]
dose_base <-
sample %>%
select( eid, all_of(vaccine), result, specdate, .data[[follow_up]]) %>%
mutate( interval_total_number = case_when((.data[[follow_up]]/28) < 1 ~ 1,
(.data[[follow_up]]/28) >= 1 & (.data[[follow_up]]/28) < 2 ~ 2,
(.data[[follow_up]]/28) >= 2 & (.data[[follow_up]]/28) < 3 ~ 3,
(.data[[follow_up]]/28) >= 3 & (.data[[follow_up]]/28) < 4 ~ 4,
(.data[[follow_up]]/28) >= 4 & (.data[[follow_up]]/28) < 5 ~ 5,
(.data[[follow_up]]/28) >= 5 & (.data[[follow_up]]/28) < 6 ~ 6,
(.data[[follow_up]]/28) >= 6 ~ 7))
# Create an index of the rows you want with duplications
temp_idx <- rep(1:nrow( dose_base), dose_base$interval_total_number)
# Use that index to genderate your new data frame
dose_base_dup <-
dose_base[temp_idx,] %>%
group_by( eid) %>%
mutate( interval_index = row_number(),
interval_days = (interval_index - 1) * 28 )
dose_base_range <- tmerge( dose_base, dose_base, id = eid, outcome_status = event( dose_base[[follow_up]], result)) #set range
counting_process <- tmerge(data1 = dose_base_range,
data2 = dose_base_dup,
id = eid,
follow_cat = tdc( interval_days, interval_index)) %>%
mutate( follow_cat = as.character(follow_cat))
counting_process_ref <-
counting_process %>%
mutate( follow_cat_ref = case_when( .data[[vaccine]] == "AstraZeneca" ~ "0", TRUE ~ follow_cat))
return( counting_process_ref)
}
first_dose_counting_infection_cohort <- first_dose_construct_data_func( cohort = first_dose_link_outcome_cohort,
outcome = "infection_event",
follow_up = "follow_up_days",
vaccine = "dmd_code_1")
first_dose_counting_hospitalization_cohort <- first_dose_construct_data_func( cohort = first_dose_link_outcome_cohort,
outcome = "hospitalization_event",
follow_up = "follow_up_days",
vaccine = "dmd_code_1")
second_dose_counting_infection_cohort <- second_dose_construct_data_func( cohort = second_dose_link_outcome_cohort,
outcome = "infection_event",
follow_up = "follow_up_days",
vaccine = "dmd_code_2")
second_dose_counting_hospitalization_cohort <- second_dose_construct_data_func( cohort = second_dose_link_outcome_cohort,
outcome = "hospitalization_event",
follow_up = "follow_up_days",
vaccine = "dmd_code_2")
# Curate baseline covariates ----------------------------------------------
link_baseline_func <- function( cohort, index_date){
sample <- cohort
# previous infection status
infection_cases <-
sample %>%
left_join( select( All_covid_positive_result, eid, specdate, result), by = "eid") %>%
filter( specdate <= .data[[index_date]]) %>%
arrange( specdate) %>%
group_by( eid) %>%
filter( row_number() == 1) %>%
mutate( previous_infection_status = "1") %>%
select( eid, previous_infection_status)%>%
ungroup()
# link to GP medication
GP_medication_long <-
sample %>%
select( eid, all_of(index_date)) %>%
left_join( emis_gp_medication, by = "eid") %>%
filter( issue_date <= .data[[index_date]] , issue_date > .data[[index_date]] - 365*3) %>% # medication history within 3 years before index date
left_join( all_medication_code, by = "dmd_code") %>%
left_join( candidate_medication_list_dpa, by = c("top_level" = "Top_level_medication_classes"))
GP_medication_long_wide <<- # to environment
GP_medication_long %>%
group_by( eid, Manual_category) %>%
filter( row_number() == 1) %>%
ungroup() %>%
mutate( medication = 1) %>%
pivot_wider( id_cols = eid,names_from = Manual_category, values_from = medication) %>%
janitor::clean_names()
drug_names <- setdiff( names(GP_medication_long_wide), "eid")
#link to GP diagnosis
GP_diagnosis_long <-
sample %>%
select( eid, all_of( index_date)) %>%
left_join( emis_gp_clinical, by = "eid") %>%
filter( event_dt <= .data[[index_date]] ) %>%
left_join( all_clinical_emis_code, by = c("code" = "SnomedCTConceptId"))
GP_diagnosis_long %>% summarise( across( .cols = everything(), ~ mean(is.na(.))*100))
GP_diagnosis_long_wide <<-
GP_diagnosis_long %>%
group_by( eid, second_level ) %>%
filter( row_number() == 1) %>%
ungroup() %>%
mutate( diagnosis = 1) %>%
pivot_wider( id_cols = eid,names_from = second_level, values_from = diagnosis) %>%
janitor::clean_names()
#link to HES admission
HES_admission <-
sample %>%
select( eid, all_of(index_date)) %>%
left_join( hes_admission, by = "eid") %>%
filter( admidate <= .data[[index_date]] , admidate > .data[[index_date]] - 365*3) %>%
group_by( eid) %>%
summarise( admission_num = n()) %>%
ungroup()
temp <-
sample %>%
select( eid, all_of(index_date)) %>%
left_join( infection_cases, by = "eid") %>%
left_join( GP_medication_long_wide, by = "eid") %>%
left_join( GP_diagnosis_long_wide, by = "eid") %>%
left_join( HES_admission, by = "eid") %>%
left_join( recruitment_charateristics, by = c( "eid" = "f.eid")) %>%
mutate( previous_infection_status = case_when( is.na(previous_infection_status) ~ "0", TRUE ~ previous_infection_status)) %>%
mutate( across( .cols = all_of( setdiff(names(GP_medication_long_wide), "eid")), ~ case_when( is.na(.) ~ 0, TRUE ~ .)),
across( .cols = all_of( setdiff(names(GP_medication_long_wide), "eid")), ~ as.factor(.))) %>%
mutate( across( .cols = all_of( setdiff(names(GP_diagnosis_long_wide), "eid")), ~ case_when( is.na(.) ~ 0, TRUE ~ .)),
across( .cols = all_of( setdiff(names(GP_diagnosis_long_wide), "eid")), ~ as.factor(.))) %>%
mutate( admission_num = case_when( is.na(admission_num) ~ as.integer(0), TRUE ~ admission_num)) %>%
mutate( age = year( .data[[index_date]]) - birth_year) %>%
mutate( across( starts_with( "BMI"), ~ case_when( is.na(.) ~ mean(., na.rm = TRUE), TRUE ~ .)), # missing data imputation
across( starts_with( "IMD"), ~ case_when( is.na(.) ~ mean(., na.rm = TRUE), TRUE ~ .)),
across( Education, ~ case_when( !is.na(.) ~ ., TRUE ~ as.factor("Prefer not to answer"))),
across( ethnicity, ~ case_when( !is.na(.) ~ ., TRUE ~ "Prefer not to answer"))) %>%
mutate( calendar_scale_week = as.factor(isoweek( .data[[index_date]])), # epi calendar week, starting from monday rather than sunday
calendar_scale_num = isoweek( .data[[index_date]])) %>%
select( -all_of(index_date))
print(summarise( temp, across( everything(), ~ sum( is.na(.)))))
return( temp)
}
first_dose_baseline_covariates <- link_baseline_func( cohort = first_dose_vaccine_cohort, index_date = "issue_date_1")
second_dose_baseline_covariates <- link_baseline_func( cohort = second_dose_vaccine_cohort, index_date = "issue_date_2")
#write_rds( first_dose_baseline_covariates, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/first_dose_baseline_covariates.rds")
#write_rds( second_dose_baseline_covariates, "D:/DPhil/UK_Biobank_opioid_application/UKB_based_projects/Sub_project/Vac_effect/Derived_datasets/second_dose_baseline_covariates.rds")
########################Association analysis#############
# First dose cohot Baseline assessment and PS weighting -----------------------------
first_dose_eligible <-
first_dose_vaccine_cohort %>%
filter( issue_date_1 >= as.Date("2021/1/11"),
issue_date_1 <= as.Date("2021/2/28"),
dmd_code_1 %in% c("AstraZeneca", "Pfizer"))
# align baseline charateristics
first_dose_align_baseline_cohort <-
first_dose_eligible %>%
select( eid, issue_date_1, dmd_code_1) %>%
left_join( first_dose_baseline_covariates, by = "eid") %>%
mutate( dmd_code_1 = fct_drop( dmd_code_1),
calendar_scale_week = fct_drop( calendar_scale_week)) #important step for counting process
first_dose_align_baseline_cohort_dummy <- # dummy baseline variables
fastDummies::dummy_cols( first_dose_align_baseline_cohort, select_columns = c( "calendar_scale_week", "Education","ethnicity")) %>%
mutate( across(starts_with("calendar_scale_week_"), ~ as.factor(.x))) %>%
mutate( across(starts_with("Education_"), ~ as.factor(.x))) %>%
mutate(across(starts_with("ethnicity_"), ~ as.factor(.x))) %>%
janitor::clean_names()
first_dose_covariates_list <- c(str_subset( names(first_dose_align_baseline_cohort_dummy), "calendar_scale_week_"), "previous_infection_status", "age", "sex", "bmi",
"imd", "imd_income_score", "imd_employment_score","imd_health_score", "imd_education_score", "imd_housing_score","imd_crime_score",
str_subset( names(first_dose_align_baseline_cohort_dummy), "ethnicity_"),
str_subset( names(first_dose_align_baseline_cohort_dummy), "education_"),
setdiff( names( GP_medication_long_wide), "eid"),
"admission_num",
setdiff( names( GP_diagnosis_long_wide), "eid")) %>% unique()
print( first_dose_covariates_list)
# PS weighting
library( survey)
library( tableone)
first_dose_ps_func <- function( input_data, proportion, model_covariate, vaccine){
set.seed(1)
sample <- sample_frac( input_data, proportion)
print(summarise( sample, across( all_of( model_covariate), ~ sum( is.na(.)))))
sample <-
sample%>%
mutate( exposure = case_when( .data[[vaccine]] %in% "AstraZeneca" ~ 0, .data[[vaccine]] %in% "Pfizer" ~ 1)) %>%
filter( !is.na(exposure))
print( table(sample$exposure))
PS_model <- glm( reformulate( termlabels = model_covariate , response = "exposure"), family = binomial(link = "logit"), data = sample)
# data with probability
PS_probability <- mutate( sample,
P_treatment = PS_model$fitted.values,
P_control = 1 - PS_model$fitted.values,
PS_weights = case_when( exposure == 1 ~ 1/P_treatment, TRUE ~ 1/P_control))
PS_probability_trim <-
PS_probability %>%
filter( P_treatment <= quantile( P_treatment, 0.99), P_treatment >= quantile( P_treatment, 0.01))
## Weighted data format
PS_IPTW <- survey::svydesign( ids = ~ 1, data = PS_probability, weights = ~ PS_weights)
PS_IPTW_trim <- survey::svydesign( ids = ~ 1, data = PS_probability_trim, weights = ~ PS_weights)
return( list( PS_probability = PS_probability,
PS_probability_trim = PS_probability_trim,
PS_IPTW = PS_IPTW,
PS_IPTW_trim = PS_IPTW_trim))
}
first_dose_cohort_weighted <- first_dose_ps_func( input_data = first_dose_align_baseline_cohort_dummy,
proportion = 1,
model_covariate = first_dose_covariates_list,
vaccine = "dmd_code_1")
table( first_dose_cohort_weighted$PS_probability_trim$dmd_code_1)
# second dose cohot Baseline assessment and PS weighting -----------------------------
second_dose_eligible <-
second_dose_vaccine_cohort %>%
filter( issue_date_2 >= as.Date("2021/3/22"),
issue_date_2 <= as.Date("2021/5/09"),
dmd_code_2 %in% c("AstraZeneca", "Pfizer"))
# align baseline charateristics
second_dose_align_baseline_cohort <-
second_dose_eligible %>%
select( eid, issue_date_2, dmd_code_2) %>%
left_join( second_dose_baseline_covariates, by = "eid") %>%
mutate( dmd_code_2 = fct_drop( dmd_code_2),
calendar_scale_week = fct_drop( calendar_scale_week)) #important step for counting process
second_dose_align_baseline_cohort_dummy <- # dummy baseline variables
fastDummies::dummy_cols( second_dose_align_baseline_cohort, select_columns = c("calendar_scale_week", "Education","ethnicity")) %>%
mutate( across(starts_with("calendar_scale_week_"), ~ as.factor(.x))) %>%
mutate( across(starts_with("Education_"), ~ as.factor(.x))) %>%
mutate(across(starts_with("ethnicity_"), ~ as.factor(.x))) %>%
janitor::clean_names()
second_dose_covariates_list <- c(str_subset( names(second_dose_align_baseline_cohort_dummy), "calendar_scale_week_"), "previous_infection_status", "age", "sex", "bmi",
"imd", "imd_income_score", "imd_employment_score","imd_health_score", "imd_education_score", "imd_housing_score","imd_crime_score",
str_subset( names(second_dose_align_baseline_cohort_dummy), "ethnicity_"),
str_subset( names(second_dose_align_baseline_cohort_dummy), "education_"),
setdiff(names(GP_medication_long_wide), "eid"),
"admission_num",
setdiff(names(GP_diagnosis_long_wide), "eid")) %>% unique()
print( second_dose_covariates_list)
# PS weighting
library( survey)
library( tableone)
second_ps_func <- function( input_data, proportion, model_covariate, vaccine){
set.seed(1)
sample <- sample_frac( input_data, proportion)
print(summarise( sample, across( all_of( model_covariate), ~ sum( is.na(.)))))
sample <-
sample%>%
mutate( exposure = case_when( .data[[vaccine]] %in% "AstraZeneca" ~ 0, .data[[vaccine]] %in% "Pfizer" ~ 1)) %>%
filter( !is.na(exposure))
print( table(sample$exposure))
PS_model <- glm( reformulate( termlabels = model_covariate , response = "exposure"), family = binomial(link = "logit"), data = sample)
# data with probability
PS_probability <- mutate( sample,
P_treatment = PS_model$fitted.values,
P_control = 1 - PS_model$fitted.values,
PS_weights = case_when( exposure == 1 ~ 1/P_treatment, TRUE ~ 1/P_control))
PS_probability_trim <-
PS_probability %>%
filter( P_treatment <= quantile( P_treatment, 0.99), P_treatment >= quantile( P_treatment, 0.01))
## Weighted data format
PS_IPTW <- survey::svydesign( ids = ~ 1, data = PS_probability, weights = ~ PS_weights)
PS_IPTW_trim <- survey::svydesign( ids = ~ 1, data = PS_probability_trim, weights = ~ PS_weights)
return( list( PS_probability = PS_probability,
PS_probability_trim = PS_probability_trim,
PS_IPTW = PS_IPTW,
PS_IPTW_trim = PS_IPTW_trim))
}
second_dose_cohort_weighted <- second_ps_func(input_data = second_dose_align_baseline_cohort_dummy,
proportion = 1,
model_covariate = second_dose_covariates_list,
vaccine = "dmd_code_2")
summary_after_IPTW <-
tableone::svyCreateTableOne( data = second_dose_cohort_weighted$PS_IPTW,
vars = second_dose_covariates_list,
includeNA = FALSE,
strata = "exposure",
test = FALSE)
# Construct a baseline table 1 --------------------------------------------
first_dose_table <- function( df, sub_df, sub_df_weight){
summary_before_IPTW <-
tableone::CreateTableOne( data = df[[sub_df]],
vars = first_dose_covariates_list,
includeNA = FALSE,
strata = "exposure",
test = FALSE)
summary_after_IPTW <-
tableone::svyCreateTableOne( data = df[[sub_df_weight]],
vars = first_dose_covariates_list,
includeNA = FALSE,
strata = "exposure",
test = FALSE)
first_dose_smd <-
data.frame(variable = rownames( tableone::ExtractSmd( summary_before_IPTW)),
before = as.numeric(tableone::ExtractSmd( summary_before_IPTW)),
after = as.numeric(tableone::ExtractSmd( summary_after_IPTW)))
first_dose_baseline <<-
print( summary_before_IPTW) %>%
as_tibble( rownames = "rowname") %>%
bind_cols( as_tibble(print( summary_after_IPTW)))
return( first_dose_smd)
}
second_dose_table <- function( df, sub_df, sub_df_weight){
summary_before_IPTW <-
tableone::CreateTableOne( data = df[[sub_df]],
vars = second_dose_covariates_list,
includeNA = FALSE,
strata = "exposure",
test = FALSE)
summary_after_IPTW <-
tableone::svyCreateTableOne( data = df[[sub_df_weight]],
vars = second_dose_covariates_list,
includeNA = FALSE,
strata = "exposure",
test = FALSE)
second_dose_smd <-
data.frame(variable = rownames( tableone::ExtractSmd( summary_before_IPTW)),
before = as.numeric(tableone::ExtractSmd( summary_before_IPTW)),
after = as.numeric(tableone::ExtractSmd( summary_after_IPTW)))
second_dose_baseline <<-
print( summary_before_IPTW) %>%
as_tibble( rownames = "rowname") %>%
bind_cols( as_tibble(print( summary_after_IPTW)))
return( second_dose_smd)
}
first_dose_smd <- first_dose_table( df = first_dose_cohort_weighted, sub_df = "PS_probability_trim", sub_df_weight = "PS_IPTW_trim")
second_dose_smd <- second_dose_table( df = second_dose_cohort_weighted, sub_df = "PS_probability_trim", sub_df_weight = "PS_IPTW_trim")
first_dose_name_list <- function(){
#paste( first_dose_smd$variable, sep = "", collapse = '","')
first_raw_names <-
c("calendar_scale_week_2",
"calendar_scale_week_3",
"calendar_scale_week_4",
"calendar_scale_week_5",
"calendar_scale_week_6",
"calendar_scale_week_7",
"calendar_scale_week_8",
"previous_infection_status" ,
"age","sex","bmi",
"imd","imd_income_score",
"imd_employment_score",
"imd_health_score",
"imd_education_score",
"imd_housing_score",
"imd_crime_score",
"ethnicity_asian_or_asian_british",
"ethnicity_black_or_black_british",
"ethnicity_chinese",
"ethnicity_do_not_know",
"ethnicity_mixed",
"ethnicity_other_ethnic_group",
"ethnicity_prefer_not_to_answer",
"ethnicity_white",
"education_none_of_the_above",
"education_prefer_not_to_answer",
"education_college_or_university_degree",
"education_a_levels_as_levels_or_equivalent",
"education_o_levels_gcs_es_or_equivalent",
"education_cs_es_or_equivalent",
"education_nvq_or_hnd_or_hnc_or_equivalent",
"education_other_professional_qualifications_eg_nursing_teaching",
"ra_sinh","proton_pump_inhibitors",
"systemic_glucocorticoids",
"antithrombotics",
"lipid_lowering_drugs",
"antidepressants",
"anticoagulants",
"antihypertensives",
"antidiabetes",
"immunosuppressants_excl_corticosteroids",
"antineoplastic_agents",
"admission_num",
"ch_pulmonary",
"ch_cancer",
"ch_cerebrovasc",
"ch_renal",
"ch_rheumatol",
"ch_diabetes",
"ch_dementia",
"ch_diabetes_comp",
"ch_mi",
"ch_peripheralvasc",
"ch_pepticulcer",
"ch_liver_mod",
"ch_heart_cong",
"ch_cancer_meta",
"ch_liver_mild",
"ch_hemiplegia",
"ch_aids")
first_Var_labels_manual <-
c("Epi week_2",
"Epi week_3",
"Epi week_4",
"Epi week_5",
"Epi week_6",
"Epi week_7",
"Epi week_8",
"Previous Covid-19 infection",
"Age","Sex","BMI",
"IMD",
"Income",
"Employment",
"Health",
"Education",
"Housing",
"Crime",
"Asian or asian british",
"Black or black british",
"Chinese",
"Do not know",
"Mixed",
"Other ethnic group",
"Prefer not to answer (Race)",
"White",
"None of the_above",
"Prefer not to answer",
"College or university degree",
"A levels as levels or equivalent",
"O levels gcs es or equivalent",
"Cs es or equivalent",
"Nvq or hnd or hnc or equivalent",
"Other professional qualifications",
"RAS inhibitors",
"Proton pump inhibitors",
"Systemic glucocorticoids",
"Antithrombotic",
"Lipid lowering drugs",
"Antidepressants",
"Anticoagulants",
"Other anti-hypertensives",
"Diabetes medicines",
"Immunosuppressants excl corticosteroids",
"Antineoplastic agents",
"Admission frequency",
"COPD",
"Cancer",
"Cerebrovascular disease",
"Chronic kidney disease",
"Rheumatoid arthritis",
"Diabetes (uncomplicated)",
"Dementia",
"Diabetes (end-organ damage)",
"Myocardial infarction",
"Peripheral vascular disease",
"Peptic ulcer",
"Liver disease (moderate to severe)",
"Congestive heart failure",
"Malignant cancer",
"Liver disease (mild)",
"Hemiplegia",
"AIDS")
}
second_dose_name_list <- function(){
second_raw_names <-
c("calendar_scale_week_12",
"calendar_scale_week_13",
"calendar_scale_week_14",
"calendar_scale_week_15",
"calendar_scale_week_16",
"calendar_scale_week_17",
"calendar_scale_week_18",
"previous_infection_status" ,
"age","sex","bmi",
"imd","imd_income_score",
"imd_employment_score",
"imd_health_score",
"imd_education_score",
"imd_housing_score",
"imd_crime_score",
"ethnicity_asian_or_asian_british",
"ethnicity_black_or_black_british",
"ethnicity_chinese",
"ethnicity_do_not_know",
"ethnicity_mixed",
"ethnicity_other_ethnic_group",
"ethnicity_prefer_not_to_answer",
"ethnicity_white",
"education_none_of_the_above",
"education_prefer_not_to_answer",
"education_college_or_university_degree",
"education_a_levels_as_levels_or_equivalent",
"education_o_levels_gcs_es_or_equivalent",
"education_cs_es_or_equivalent",
"education_nvq_or_hnd_or_hnc_or_equivalent",
"education_other_professional_qualifications_eg_nursing_teaching",
"ra_sinh","proton_pump_inhibitors",
"systemic_glucocorticoids",
"antithrombotics",
"lipid_lowering_drugs",
"antidepressants",
"anticoagulants",
"antihypertensives",
"antidiabetes",
"immunosuppressants_excl_corticosteroids",
"antineoplastic_agents",
"admission_num",
"ch_pulmonary",
"ch_cancer",
"ch_cerebrovasc",
"ch_renal",
"ch_rheumatol",
"ch_diabetes",
"ch_dementia",
"ch_diabetes_comp",
"ch_mi",
"ch_peripheralvasc",
"ch_pepticulcer",
"ch_liver_mod",
"ch_heart_cong",
"ch_cancer_meta",
"ch_liver_mild",
"ch_hemiplegia",
"ch_aids")
second_Var_labels_manual <-
c("Epi week_12",
"Epi week_13",
"Epi week_14",
"Epi week_15",
"Epi week_16",
"Epi week_17",
"Epi week_18",
"Previous Covid-19 infection",
"Age","Sex","BMI",
"IMD",
"Income",
"Employment",
"Health",
"Education",
"Housing",
"Crime",
"Asian or asian british",
"Black or black british",
"Chinese",
"Do not know",
"Mixed",
"Other ethnic group",
"Prefer not to answer (Race)",
"White",
"None of the_above",
"Prefer not to answer",
"College or university degree",
"A levels as levels or equivalent",