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104_baseline_tables.do
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104_baseline_tables.do
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/*==============================================================================
DO FILE NAME: 104_baseline_tables.do
DATE: 24/05/2023
AUTHOR: R Costello
DESCRIPTION OF FILE: Produce baseline table for cohort
==============================================================================*/
adopath + ./analysis/ado
cap mkdir ./output/tables
* Open a log file
cap log using ./logs/baseline_tables.log, replace
import delimited using ./output/input_pbc.csv, clear
* Prepare variables for table
replace ethnicity=6 if ethnicity==0
label define eth5 1 "White" ///
2 "Mixed" ///
3 "Asian" ///
4 "Black" ///
5 "Other" ///
6 "Unknown"
label values ethnicity eth5
safetab ethnicity, m
*(2) IMD
replace imd=6 if imd==0
* Create age categories
egen age_cat = cut(age), at(18, 40, 60, 80, 120) icodes
label define age 0 "18 - 40 years" 1 "41 - 60 years" 2 "61 - 80 years" 3 ">80 years"
label values age_cat age
bys age_cat: sum age
* BMI categories
* assume BMI's less than 10 are incorrect and set to missing
sum bmi, d
replace bmi = . if bmi<10
egen bmi_cat = cut(bmi), at(0, 1, 18.5, 24.9, 29.9, 39.9, 100) icodes
bys bmi_cat: sum bmi
* add missing . to zero category
replace bmi_cat = 0 if bmi_cat==.
label define bmi 0 "Missing" 1 "Underweight" 2 "Healthy range" 3 "Overweight" 4 "Obese" 5 "Morbidly obese"
label values bmi_cat bmi
* Smoking status
gen smoking = 0 if smoking_status=="N"
replace smoking = 1 if smoking_status=="S"
replace smoking = 2 if smoking_status=="E"
replace smoking = 3 if smoking==.
label define smok 1 "Current smoker" 2 "Ex-smoker" 0 "Never smoked" 3 "Unknown"
label values smoking smok
replace oral_steroid_drugs_nhsd=. if oral_steroid_drug_nhsd_3m_count < 2 & oral_steroid_drug_nhsd_12m_count < 4
gen imid_nhsd=max(oral_steroid_drugs_nhsd, immunosuppresant_drugs_nhsd)
gen rare_neuro_nhsd = max(multiple_sclerosis_nhsd, motor_neurone_disease_nhsd, myasthenia_gravis_nhsd, huntingtons_disease_nhsd)
gen solid_organ_transplant_bin = solid_organ_transplant_nhsd_new!=""
gen any_high_risk_condition = max(learning_disability_nhsd_snomed, cancer_opensafely_snomed_new, haematological_disease_nhsd, ///
ckd_stage_5_nhsd, imid_nhsd, immunosupression_nhsd_new, hiv_aids_nhsd, solid_organ_transplant_bin, rare_neuro_nhsd)
foreach var in udca gc budesonide fenofibrate {
sum `var'_count_bl, d
sum `var'_count_fu, d
gen `var'_any = (`var'_count_fu>=1 & `var'_count_fu!=.)
gen `var'_bl = (`var'_count_bl>=1 & `var'_count_bl!=.)
tab `var'_any, m
tab `var'_bl, m
}
* Generate variable with number of vaccinations
foreach var in covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth {
gen `var'= (`var'_date!="")
}
egen vacc_any = rowmax(covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth)
egen total_vaccs = rowtotal(covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth)
tab total_vaccs
gen severe_disease_fu = severe_disease_fu_date!=""
* Time from most recent vaccination on 1st March 2021
gen date_most_recent_cov_vacA = date(date_most_recent_cov_vac, "YMD")
gen time_vacc = date("01Mar2021", "DMY") - date_most_recent_cov_vacA
sum time_vacc, d
xtile time_vacc_cat = time_vacc, nq(4)
bys time_vacc_cat: sum time_vacc
* Create tables
* Characteristics whole cohort
preserve
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ ethnicity cat \ severe_disease_bl cat \ smoking cat \ bmi_cat cat \ has_pbc bin \ ///
any_high_risk_condition cat) clear
export delimited using ./output/tables/baseline_table.csv, replace
* Rounding numbers in table to nearest 5
destring _columna_1, gen(n) ignore(",") force
destring _columnb_1, gen(percent) ignore("-" "%" "(" ")") force
gen rounded_n = round(n, 5)
tostring rounded_n, gen(n_rounded)
replace n_rounded = "redacted" if (rounded_n<=5)
keep factor level n_rounded percent
export delimited using ./output/tables/baseline_table_rounded.csv
restore
* High risk conditions
preserve
table1_mc, vars(learning_disability_nhsd_snomed cat \ cancer_opensafely_snomed_new cat \ haematological_disease_nhsd cat \ ///
ckd_stage_5_nhsd cat \ imid_nhsd cat \ immunosupression_nhsd_new cat \ hiv_aids_nhsd cat \ solid_organ_transplant_bin cat \ ///
rare_neuro_nhsd cat) clear
export delimited using ./output/tables/high_risk.csv, replace
* Rounding numbers in table to nearest 5
destring _columna_1, gen(n) ignore(",") force
destring _columnb_1, gen(percent) ignore("-" "%" "(" ")") force
gen rounded_n = round(n, 5)
tostring rounded_n, gen(n_rounded)
replace n_rounded = "redacted" if (rounded_n<=5)
keep factor level n_rounded percent
export delimited using ./output/tables/high_risk_rounded.csv
restore
* Characteristics by exposure status at baseline
preserve
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ ethnicity cat \ severe_disease_bl cat \ smoking cat \ bmi_cat cat \ has_pbc bin \ any_high_risk_condition cat) by(udca_bl) clear
export delimited using ./output/tables/baseline_table_udca.csv, replace
* Rounding numbers in table to nearest 5
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring percent`i', gen(percent_`i')
tostring rounded_n`i', gen(n`i'_rounded)
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor level n0_rounded percent_0 n1_rounded percent_1
export delimited using ./output/tables/baseline_table_udca_rounded.csv
restore
* Additional medications by any exposure
preserve
table1_mc, vars(budesonide_bl bin \ fenofibrate_bl bin \ gc_bl bin \ oca_bl bin \ rituximab_bl bin \ severe_disease_fu bin \ vacc_any bin \ total_vaccs cat \ time_vacc_cat cat) by(udca_bl) clear
export delimited using ./output/tables/additional_meds_udca.csv, replace
* Rounding numbers in table to nearest 5
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring rounded_n`i', gen(n`i'_rounded)
tostring percent`i', gen(percent_`i')
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor n0_rounded percent_0 n1_rounded percent_1
export delimited using ./output/tables/additional_meds_udca_rounded.csv
restore
* Checking outcomes
foreach var in died_date_ons hosp_covid_primary hosp_covid_any dereg_date liver_transplant_fu_date {
gen `var'A = date(`var', "YMD")
format `var'A %dD/N/CY
drop `var'
gen yr_`var' = year(`var'A)
tab yr_`var'
replace `var'A=. if yr_`var'==2023
}
gen end_study = date("31/12/2022", "DMY")
egen end_date = rowmin(dereg_dateA end_study died_date_onsA)
replace died_date_onsA=. if end_date<died_date_onsA
replace died_ons_covid_flag_any=0 if end_date<died_date_onsA & died_ons_covid_flag_any==1
replace hosp_covid_anyA=. if end_date<hosp_covid_anyA
* Flag if died
gen died_flag = died_date_onsA!=.
* Generate date if died of covid
gen died_date_ons_covid = died_date_onsA if died_ons_covid_flag_any == 1
* Flag hospitalised with covid
gen hosp_any_flag = hosp_covid_anyA!=.
* Composite outcome
gen hosp_died = (died_ons_covid_flag_any==1 | hosp_any_flag==1)
tab hosp_died
egen hosp_died_dateA = rowmin(died_date_onsA hosp_covid_anyA)
replace hosp_died_dateA=. if hosp_died==0
sum hosp_died_dateA
* Generate flag of how met composite outcome
di "Number where died before hospitalisation date - should be zero"
count if died_date_onsA<hosp_covid_anyA & hosp_covid_anyA!=.
di "Number where have both hospitalisation and death date"
count if died_date_onsA!=. & hosp_covid_anyA!=.
gen composite_make_up = hosp_died
replace composite_make_up = 2 if died_ons_covid_flag_any==1 & hosp_any_flag==0
label define comp 0 "No outcome" 1 "Hospitalised" 2 "Died"
label values composite_make_up comp
* Check
count if composite_make_up==1 & hosp_covid_anyA==.
count if composite_make_up==1 & hosp_died_dateA!=hosp_covid_anyA
count if composite_make_up==2 & died_date_onsA==.
count if composite_make_up==2 & hosp_died_dateA!=died_date_onsA
tab composite_make_up died_ons_covid_flag_any
tab composite_make_up hosp_any_flag
tab udca_bl died_ons_covid_flag_any
tab udca_bl hosp_any_flag
* check number of outcomes per ethnicity group
tab ethnicity died_ons_covid_flag_any
tab ethnicity hosp_any_flag
* Number of outcomes within each time period: pre-vaccination of most population - prior to 1st March 2021 and after
gen composite_pre = hosp_died_dateA < date("01Mar2021", "DMY")
replace composite_pre = 0 if hosp_died_dateA==.
tab hosp_died composite_pre
gen died_pre = died_date_onsA < date("01Mar2021", "DMY")
replace died_pre = . if died_date_onsA==. | died_ons_covid_flag_any==0
tab died_ons_covid_flag_any died_pre
gen hosp_pre = hosp_covid_anyA < date("01Mar2021", "DMY")
replace hosp_pre = . if hosp_covid_anyA==.
tab hosp_any_flag hosp_pre
gen composite_post = hosp_died_dateA >= date("01Mar2021", "DMY") & hosp_died_dateA!=.
replace composite_post = 0 if hosp_died_dateA==.
tab hosp_died composite_post
gen died_post = died_date_onsA >= date("01Mar2021", "DMY") & died_date_onsA!=.
replace died_post = . if died_date_onsA==. | died_ons_covid_flag_any==0
tab died_ons_covid_flag_any died_post
gen hosp_post = hosp_covid_anyA >= date("01Mar2021", "DMY") & hosp_covid_anyA!=.
replace hosp_post = . if hosp_covid_anyA==.
tab hosp_any_flag hosp_post
* Exploring death by liver disease
gen died_liver_any = died_ons_liver_flag_any==1 & died_ons_covid_flag_any!=1
gen died_liver_underlying = died_ons_liver_flag_underlying==1 & died_ons_covid_flag_any!=1
* Exploring liver transplants during follow-update
gen liver_transplant_fu = liver_transplant_fu_dateA!=.
* Check
count if died_liver_any==1 & died_ons_covid_flag_any==1
count if died_liver_underlying==1 & died_ons_covid_flag_any==1
* Outcomes by baseline exposure stataus
preserve
table1_mc, by(udca_bl) vars(died_ons_covid_flag_any bin \ hosp_any_flag bin \ hosp_died bin \ composite_make_up cat \ composite_pre bin \ ///
died_pre bin \ hosp_pre bin \ composite_post bin \ died_post bin \ hosp_post bin \ died_liver_any bin \ died_liver_underlying bin \ liver_transplant_fu bin) clear
export delimited using ./output/tables/baseline_outcomes.csv
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring rounded_n`i', gen(n`i'_rounded)
tostring percent`i', gen(percent_`i')
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor rounded_n0 percent0 rounded_n1 percent1
export delimited using ./output/tables/baseline_outcomes_rounded.csv
restore
preserve
table1_mc, by(died_ons_covid_flag_any) vars(died_ons_liver_flag_any bin \ died_ons_liver_flag_underlying bin) clear
export delimited using ./output/tables/baseline_liver_covid_any.csv
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring rounded_n`i', gen(n`i'_rounded)
tostring percent`i', gen(percent_`i')
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor rounded_n0 percent0 rounded_n1 percent1
export delimited using ./output/tables/baseline_liver_covid_any_rounded.csv
restore
preserve
table1_mc, by(died_ons_covid_flag_underlying) vars(died_ons_liver_flag_any bin \ died_ons_liver_flag_underlying bin) clear
export delimited using ./output/tables/baseline_liver_covid_underlying.csv
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring rounded_n`i', gen(n`i'_rounded)
tostring percent`i', gen(percent_`i')
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor rounded_n0 percent0 rounded_n1 percent1
export delimited using ./output/tables/baseline_liver_covid_underlying_rounded.csv
restore
* Sensitivity analysis March 2021 cohort - vaccinations
import delimited using ./output/input_vacc.csv, clear
* Prepare variables for table
replace ethnicity=6 if ethnicity==0
label define eth5 1 "White" ///
2 "Mixed" ///
3 "Asian" ///
4 "Black" ///
5 "Other" ///
6 "Unknown"
label values ethnicity eth5
safetab ethnicity, m
*(2) IMD
replace imd=6 if imd==0
* Create age categories
egen age_cat = cut(age), at(18, 40, 60, 80, 120) icodes
label define age 0 "18 - 40 years" 1 "41 - 60 years" 2 "61 - 80 years" 3 ">80 years"
label values age_cat age
bys age_cat: sum age
* BMI categories
* assume BMI's less than 10 are incorrect and set to missing
sum bmi, d
replace bmi = . if bmi<10
egen bmi_cat = cut(bmi), at(0, 1, 18.5, 24.9, 29.9, 39.9, 100) icodes
bys bmi_cat: sum bmi
* add missing . to zero category
replace bmi_cat = 0 if bmi_cat==.
label define bmi 0 "Missing" 1 "Underweight" 2 "Healthy range" 3 "Overweight" 4 "Obese" 5 "Morbidly obese"
label values bmi_cat bmi
* Smoking status
gen smoking = 0 if smoking_status=="N"
replace smoking = 1 if smoking_status=="S"
replace smoking = 2 if smoking_status=="E"
replace smoking = 3 if smoking==.
label define smok 1 "Current smoker" 2 "Ex-smoker" 0 "Never smoked" 3 "Unknown"
label values smoking smok
replace oral_steroid_drugs_nhsd=. if oral_steroid_drug_nhsd_3m_count < 2 & oral_steroid_drug_nhsd_12m_count < 4
gen imid_nhsd=max(oral_steroid_drugs_nhsd, immunosuppresant_drugs_nhsd)
gen rare_neuro_nhsd = max(multiple_sclerosis_nhsd, motor_neurone_disease_nhsd, myasthenia_gravis_nhsd, huntingtons_disease_nhsd)
gen solid_organ_transplant_bin = solid_organ_transplant_nhsd_new!=""
gen any_high_risk_condition = max(learning_disability_nhsd_snomed, cancer_opensafely_snomed_new, haematological_disease_nhsd, ///
ckd_stage_5_nhsd, imid_nhsd, immunosupression_nhsd_new, hiv_aids_nhsd, solid_organ_transplant_bin, rare_neuro_nhsd)
foreach var in udca gc budesonide fenofibrate {
sum `var'_count_bl, d
sum `var'_count_fu, d
gen `var'_any = (`var'_count_fu>=1 & `var'_count_fu!=.)
gen `var'_bl = (`var'_count_bl>=1 & `var'_count_bl!=.)
tab `var'_any, m
tab `var'_bl, m
}
* Generate variable with number of vaccinations
foreach var in covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth {
gen `var'= (`var'_date!="")
}
egen vacc_any = rowmax(covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth)
egen total_vaccs = rowtotal(covid_vacc_first covid_vacc_second covid_vacc_third covid_vacc_fourth covid_vacc_fifth)
tab total_vaccs
gen severe_disease_fu = severe_disease_fu_date!=""
* Time from most recent vaccination on 1st March 2021
gen date_most_recent_cov_vacA = date(date_most_recent_cov_vac, "YMD")
gen time_vacc = date("01Mar2021", "DMY") - date_most_recent_cov_vacA
sum time_vacc, d
xtile time_vacc_cat = time_vacc, nq(4)
bys time_vacc_cat: sum time_vacc
* Create tables
* Characteristics whole cohort
preserve
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ ethnicity cat \ severe_disease_bl cat \ smoking cat \ bmi_cat cat \ has_pbc bin \ ///
any_high_risk_condition cat) clear
export delimited using ./output/tables/baseline_table_vacc.csv, replace
* Rounding numbers in table to nearest 5
destring _columna_1, gen(n) ignore(",") force
destring _columnb_1, gen(percent) ignore("-" "%" "(" ")") force
gen rounded_n = round(n, 5)
tostring rounded_n, gen(n_rounded)
replace n_rounded = "redacted" if (rounded_n<=5)
keep factor level n_rounded percent
export delimited using ./output/tables/baseline_table_vacc_rounded.csv
restore
* Characteristics by exposure status at baseline
preserve
table1_mc, vars(age_cat cat \ sex cat \ imd cat \ ethnicity cat \ severe_disease_bl cat \ smoking cat \ bmi_cat cat \ has_pbc bin \ any_high_risk_condition cat) by(udca_bl) clear
export delimited using ./output/tables/baseline_table_udca_vacc.csv, replace
* Rounding numbers in table to nearest 5
forvalues i=0/1 {
destring _columna_`i', gen(n`i') ignore(",") force
destring _columnb_`i', gen(percent`i') ignore("-" "%" "(" ")") force
gen rounded_n`i' = round(n`i', 5)
tostring percent`i', gen(percent_`i')
tostring rounded_n`i', gen(n`i'_rounded)
replace n`i'_rounded = "redacted" if (rounded_n`i'<=5)
replace percent_`i' = "redacted" if (rounded_n`i'<=5)
}
keep factor level n0_rounded percent_0 n1_rounded percent_1
export delimited using ./output/tables/baseline_table_udca_vacc_rounded.csv
restore