df_sbp <-
df_pings2_bp |> # labelled::look_for("acei")
select(sbp, dbp, pid, eventname) |>
mutate(
mth = parse_number(eventname),
mth = case_when(
eventname == "Baseline" ~ 0,
TRUE ~ mth)) |>
pivot_wider(
id_cols = pid,
values_from = sbp,
names_from = mth,
names_prefix = "sbp_m_") |>
drop_na()
df_dbp <-
df_pings2_bp |> # labelled::look_for("acei")
select(sbp, dbp, pid, eventname) |>
mutate(
mth = parse_number(eventname),
mth = case_when(
eventname == "Baseline" ~ 0,
TRUE ~ mth)) |>
pivot_wider(
id_cols = pid,
values_from = dbp,
names_from = mth,
names_prefix = "dbp_m_") |>
drop_na()
df_drugs <-
df_drugs_temp %>%
left_join(df_max_drug_doses_temp, by = "drug") %>%
mutate(
dose_index = freq_number*dose_number/maxi,
antihpt = case_when(is.na(antihpt) ~ 0, TRUE ~ antihpt)) %>%
filter(antihpt == 1) %>%
group_by(pid, eventname, arm) %>%
summarize(
n_hpts = sum(antihpt),
dose_index = sum(dose_index)) %>%
ungroup() |>
filter(eventname == "Baseline") |>
select(pid, dose_index)
df_paper_20 <-
df_pings2_bp |> # labelled::look_for("acei")
select(sbp, dbp, pid, eventname) |>
mutate(
sbp_high = sbp >= 140,
dbp_high = dbp >= 90,
hpt = sbp_high | dbp_high,
mth = parse_number(eventname),
mth = case_when(
eventname == "Baseline" ~ 0,
TRUE ~ mth)) |>
select(pid, mth, hpt) |>
arrange(pid, mth) |>
pivot_wider(
id_cols = pid,
values_from = hpt,
names_from = mth,
names_prefix = "m_") |>
drop_na() |>
mutate(
m_0 = case_when(m_0 == FALSE ~ TRUE, TRUE ~ m_0),
total_1 = rowSums(across(c(m_0:m_12))),
hpt_class = case_when(
total_1 == 1 ~ "Early",
total_1 == 6 ~ "Persistent",
total_1 == 5 & m_12 == FALSE ~ "Late",
total_1 == 5 ~ "Fluctuating",
total_1 == 4 & m_9 == FALSE & m_12 == FALSE~ "Late",
m_0 & m_1 & m_3 == FALSE & m_6 == FALSE & m_9 == FALSE &
m_12 == FALSE~ "Early",
m_0 & m_1 & m_3 & m_6 == FALSE & m_9 == FALSE & m_12 == FALSE~ "Early",
m_0 & m_1 & m_3 & m_6 & m_9 == FALSE & m_12 == FALSE~ "Late",
m_0 & m_1 & m_3 & m_6 & m_9 & m_12 == FALSE~ "Late",
m_0 & m_1 & m_3 & m_6 == FALSE & m_9 & m_12 == FALSE~ "Fluctuating",
m_0 & m_1 & m_3 & m_6 == FALSE & m_9 == FALSE & m_12 ~ "Fluctuating",
m_0 & m_1 & m_3 == FALSE & m_6 & m_9 & m_12 == FALSE~ "Fluctuating",
total_1 == 4 & m_9 == FALSE & m_12 ~ "Fluctuating",
total_1 == 4 & m_9 & m_12 == FALSE~ "Fluctuating",
total_1 == 4 ~ "Fluctuating",
total_1 == 3 ~ "Fluctuating",
total_1 == 2 ~ "Fluctuating")) |>
left_join(df_baseline) |>
left_join(df_antihypt) |>
left_join(df_sbp) |>
left_join(df_dbp) |>
left_join(df_drugs) |>
left_join(df_for_outc) |>
mutate(
no_of_hpts =
(h_acei == "Checked") + (h_arb == "Checked") +
(h_bb == "Checked") + (h_ccb == "Checked") +
(h_diuretics == "Checked") + (h_alpha_md == "Checked") +
(h_alpha_ab == "Checked"),
arm = case_when(
arm == "Arm 1- Intervention Arm" ~ "Intervention",
arm == "Arm 2- Routine Care" ~ "Routine") |>
factor(levels = c("Routine", "Intervention")),
alcohol_curr = case_when(
g_alcohol == "Currently uses alcohol" ~ "Yes",
g_alcohol %in% c(
"Never used alcohol",
"Past 12 months",
"Past 30 days",
"Formerly used alcohol",
"Stopped after the stroke occured") ~ "No"))
df_ready <-
df_paper_20 |>
mutate(
bmi_cat = case_when(
bmi < 25 ~ "Normal",
bmi < 30 ~ "Overweight",
bmi >= 30 ~ "Obese")|>
factor(levels = c("Normal","Overweight", "Obese")),
hba1c_high = case_when(
ff_hba1c_0 >= 6.5 ~ "Yes",
ff_hba1c_0 < 6.5 ~ "No"),
across(
c(h_acei, h_arb, h_bb, h_ccb, h_diuretics, h_alpha_md, h_alpha_ab),
.fns = ~case_when(
.x == "Checked" ~ "Yes",
.x == "Unchecked" ~ "No")),
across(
c(a_agebase, waist_hip_ratio, ff_hba1c_0),
.fns = as.numeric),
across(
c(a_gender, educ, a_domicile, a_religion, d_stroke_ct, d_st_type, dm,
tobacco_use, hyperlipidemia, alcohol_curr, hba1c_high, h_acei:h_statins,
hosp_cat, outc_140_18, outc_130_14, h_ccb, income),
.fns = as.factor)) |>
select(
a_agebase, a_gender, educ, maristat, a_domicile, income,
a_religion, d_stroke_ct, d_st_type, nihss_scale, ranking,
dm, tobacco_use, bmi_cat, waist_hip_ratio, arm,hyperlipidemia,
alcohol_curr, ff_hba1c_0, hba1c_high, h_acei, h_arb, h_bb, h_ccb,
h_diuretics, h_alpha_md, h_alpha_ab,h_antiplt, h_statins, hillbone,
aa_hkq, eq_5d, hosp_cat, dose_index, outc_140_18, outc_130_14)