Papre 21: 12 Month BP Control

Author

Dr Solomon Gyabaah

Published

October 4, 2026

Note
  • Everyone was hypertensive at baseline so that is not included in the tables
  • Only 39 participants are current alcohol users. Should we still use that criteria?

Importing Data

Show code 
df_pings2_bp <- 
    dget("Data/pings_2_data_with_bps_ready")

df_baseline <- 
    dget("Data/df_for_papers") %>% 
    labelled::unlabelled() %>% 
    mutate(
        d_st_type = as.character(d_st_type),
        d_st_type = case_when(
            d_st_type == "Ischemic With Hemorrhagic Transformation" ~ 
            "Ischemic Stroke", 
            is.na(d_st_type) ~ "Untyped Stroke (no CT scan available)",
            TRUE ~ d_st_type)) |> 
    labelled::set_variable_labels(
            d_st_type = "Stroke Type")

df_antihypt <- 
    df_pings2_bp |> 
    filter(eventname == "Baseline") |> 
    select(
        pid, h_antihpt, h_ccb, h_acei, h_arb, h_bb, 
        h_alpha_md, h_diuretics, h_alpha_ab, h_antiplt, 
        h_statins)

df_drugs_temp <- 
  readxl::read_xlsx(
      "Data/drugs_data_clean_10122025.xlsx", sheet = 1) 

df_max_drug_doses_temp <- 
  readxl::read_xlsx(
      "Data/PINGS_Antihypertensive Maximum doses.xlsx", sheet = 2)

Data manipulation

Show code 
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) |> 
    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 |> 
    select(
        hpt_class, a_agebase, a_gender, educ, maristat, arm, income,    
        a_religion, d_stroke_ct, d_st_type, nihss_scale, ranking, 
        dm, hyperlipidemia, tobacco_use, bmi, waist_hip_ratio, 
        alcohol_curr, ff_hba1c_0, ff_creat_0, ee_sbp_0, ee_dbp_0, h_acei, 
        h_arb, h_bb, h_ccb, h_diuretics, h_alpha_md, h_alpha_ab, 
        no_of_hpts, h_antiplt, h_statins, hillbone, aa_hkq, eq_5d, 
        hosp_cat, dose_index)

Labelling data

Show code 
df_ready <- 
    df_ready |> 
    labelled::set_variable_labels(
            h_arb = "Angiotensin Receptor Blocker",
            h_acei = "ACE-Inhibitors",
            h_alpha_md = "Alpha Methyl Dopa",
            h_bb = "Beta blockers",
            h_ccb = "Calcium Channel Blocker",
            h_diuretics = "Diuretics",
            h_alpha_ab = "Alpha Adrenergic Blockers", 
            no_of_hpts = "No. of Antihypertensives", 
            h_antiplt = "Antiplatelet", 
            h_statins = "Statins",
            dose_index = "Antihypertensive treatment intensity",
            eq_5d = "Health Related QoL", 
            arm = "Study Arm", 
            hpt_class = "Hypertension response classification")

Table 0

Show code 
gtsummary::theme_gtsummary_compact()
Setting theme "Compact"
Show code 
gtsummary::theme_gtsummary_eda()
Setting theme "Exploratory Data Analysis"
Show code 
df_ready |> # labelled::look_for("alc")
    gtsummary::tbl_summary(missing_text = "Missing") |> 
    gtsummary::bold_labels()
Characteristic N = 3651
Hypertension response classification
    Early 64 (17.5%)
    Fluctuating 171 (46.8%)
    Late 43 (11.8%)
    Persistent 87 (23.8%)
Age in years
    Median (Q1, Q3) 58 (50, 67)
    Mean (SD) 58 (11)
    Min, Max 29, 86
Gender
    Male 207 (56.7%)
    Female 158 (43.3%)
Educational Status
    None 31 (8.49%)
    Primary 152 (41.6%)
    Secondary 117 (32.1%)
    Tertiary 65 (17.8%)
Marital Status
    Currently Married 244 (66.8%)
    Previously Married 101 (27.7%)
    Never Married 20 (5.48%)
Study Arm
    Routine 184 (50.4%)
    Intervention 181 (49.6%)
Income in GHC
    0-100 130 (35.8%)
    101-250 113 (31.1%)
    251-500 81 (22.3%)
    >500 39 (10.7%)
    Missing 2
Religion
    Christianity 332 (91.0%)
    Islam 31 (8.49%)
    Other 2 (0.55%)
Stroke Subtype ( with results of Brain CT scan)
    Ischaemic 237 (73.8%)
    Haemorrhage infarct 10 (3.12%)
    Haemorrhagic 66 (20.6%)
    Ischaemic and Haemorrhagic 8 (2.49%)
    Missing 44
Stroke Type
    Intracerebral Hemorrhagic Stroke 76 (20.8%)
    Ischemic Stroke 260 (71.2%)
    Untyped Stroke (no CT scan available) 29 (7.95%)
NIH Stroke Scale
    Median (Q1, Q3) 2.0 (0.0, 7.0)
    Mean (SD) 4.3 (4.9)
    Min, Max 0.0, 23.0
    Missing 4
Modified Ranking Score
    0 26 (7.12%)
    1 84 (23.0%)
    2 117 (32.1%)
    3 72 (19.7%)
    4 65 (17.8%)
    5 1 (0.27%)
Diabetes Mellitus 123 (33.7%)
Hyperlipidemia 120 (32.9%)
History of tobacco use 25 (7.00%)
    Missing 8
Body Mass Index
    Median (Q1, Q3) 26.0 (22.7, 30.0)
    Mean (SD) 26.6 (5.5)
    Min, Max 11.4, 47.9
    Missing 8
Waist-Hip Ratio
    Median (Q1, Q3) 0.93 (0.89, 0.96)
    Mean (SD) 1.17 (4.63)
    Min, Max 0.09, 88.30
    Missing 8
alcohol_curr 39 (10.7%)
HBA1C (Baseline)
    Median (Q1, Q3) 5.80 (5.40, 6.70)
    Mean (SD) 6.44 (2.03)
    Min, Max 3.00, 16.10
    Missing 69
Serum Creatinine (mmol/L)
    Median (Q1, Q3) 87 (71, 104)
    Mean (SD) 94 (37)
    Min, Max 11, 266
    Missing 43
Systolic blood pressure (mm Hg)-Baseline
    Median (Q1, Q3) 155 (148, 170)
    Mean (SD) 161 (19)
    Min, Max 114, 263
Diastolic Blood Pressure
    Median (Q1, Q3) 95 (88, 105)
    Mean (SD) 97 (14)
    Min, Max 64, 150
ACE-Inhibitors
    Unchecked 336 (92.1%)
    Checked 29 (7.95%)
Angiotensin Receptor Blocker
    Unchecked 332 (91.0%)
    Checked 33 (9.04%)
Beta blockers
    Unchecked 351 (96.2%)
    Checked 14 (3.84%)
Calcium Channel Blocker
    Unchecked 203 (55.6%)
    Checked 162 (44.4%)
Diuretics
    Unchecked 350 (95.9%)
    Checked 15 (4.11%)
Alpha Methyl Dopa
    Unchecked 348 (95.3%)
    Checked 17 (4.66%)
Alpha Adrenergic Blockers
    Unchecked 360 (98.6%)
    Checked 5 (1.37%)
No. of Antihypertensives
    0 175 (47.9%)
    1 121 (33.2%)
    2 56 (15.3%)
    3 12 (3.29%)
    6 1 (0.27%)
Antiplatelet 48 (14.0%)
    Missing 21
Statins 35 (10.4%)
    Missing 29
Total Hillbone Score
    Median (Q1, Q3) 53.0 (50.0, 53.0)
    Mean (SD) 51.4 (3.7)
    Min, Max 30.0, 56.0
    Missing 6
Total HKQ Score
    Median (Q1, Q3) 9 (6, 10)
    Mean (SD) 8 (3)
    Min, Max 0, 13
    Missing 15
Health Related QoL
    Median (Q1, Q3) 10.00 (8.00, 11.00)
    Mean (SD) 9.71 (1.94)
    Min, Max 6.00, 15.00
    Missing 144
Health institution category
    Primary 112 (30.7%)
    Secondary 81 (22.2%)
    Tertiary 172 (47.1%)
Antihypertensive treatment intensity
    Median (Q1, Q3) 1.44 (0.83, 1.92)
    Mean (SD) 1.48 (0.79)
    Min, Max 0.06, 4.50
    Missing 43
1 n (%)

Table 1

Show code 
gtsummary::reset_gtsummary_theme()
gtsummary::theme_gtsummary_compact()
Setting theme "Compact"
Show code 
df_ready |> 
    gtsummary::tbl_summary(
        by = hpt_class, 
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing = "ifany", 
        type = list(ranking ~ "continuous", no_of_hpts ~ "continuous")) |> 
    gtsummary::add_overall(last = TRUE) |> 
    gtsummary::bold_labels() |> 
    gtsummary::modify_spanning_header(
    gtsummary::all_stat_cols() ~ "**Hypertension Trajectories**") |>
    gtsummary::modify_caption("Table 1") %>% 
    gtsummary::add_p(
        pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3), 
        test = list(
            gtsummary::all_categorical() ~ "fisher.test"), 
        test.args = list(
            gtsummary::all_categorical() ~ list(
                simulate.p.value = TRUE, B = 10000)))
Table 1
Characteristic
Hypertension Trajectories
p-value2
Early
N = 641		 Fluctuating
N = 1711		 Late
N = 431		 Persistent
N = 871		 Overall
N = 3651
Age in years 59 (52, 70) 58 (51, 67) 59 (55, 65) 56 (48, 65) 58 (50, 67) 0.410
Gender




0.751
    Male 38 (59.4) 100 (58.5) 22 (51.2) 47 (54.0) 207 (56.7)
    Female 26 (40.6) 71 (41.5) 21 (48.8) 40 (46.0) 158 (43.3)
Educational Status




0.414
    None 4 (6.3) 18 (10.5) 4 (9.3) 5 (5.7) 31 (8.5)
    Primary 28 (43.8) 66 (38.6) 18 (41.9) 40 (46.0) 152 (41.6)
    Secondary 19 (29.7) 57 (33.3) 18 (41.9) 23 (26.4) 117 (32.1)
    Tertiary 13 (20.3) 30 (17.5) 3 (7.0) 19 (21.8) 65 (17.8)
Marital Status




0.374
    Currently Married 49 (76.6) 113 (66.1) 24 (55.8) 58 (66.7) 244 (66.8)
    Previously Married 13 (20.3) 50 (29.2) 15 (34.9) 23 (26.4) 101 (27.7)
    Never Married 2 (3.1) 8 (4.7) 4 (9.3) 6 (6.9) 20 (5.5)
Study Arm




<0.001
    Routine 22 (34.4) 97 (56.7) 14 (32.6) 51 (58.6) 184 (50.4)
    Intervention 42 (65.6) 74 (43.3) 29 (67.4) 36 (41.4) 181 (49.6)
Income in GHC




0.387
    0-100 28 (44.4) 52 (30.6) 14 (32.6) 36 (41.4) 130 (35.8)
    101-250 14 (22.2) 56 (32.9) 18 (41.9) 25 (28.7) 113 (31.1)
    251-500 14 (22.2) 40 (23.5) 9 (20.9) 18 (20.7) 81 (22.3)
    >500 7 (11.1) 22 (12.9) 2 (4.7) 8 (9.2) 39 (10.7)
    Unknown 1 1 0 0 2
Religion




0.425
    Christianity 58 (90.6) 157 (91.8) 36 (83.7) 81 (93.1) 332 (91.0)
    Islam 5 (7.8) 13 (7.6) 7 (16.3) 6 (6.9) 31 (8.5)
    Other 1 (1.6) 1 (0.6) 0 (0.0) 0 (0.0) 2 (0.5)
Stroke Subtype ( with results of Brain CT scan)




0.745
    Ischaemic 38 (67.9) 108 (73.5) 32 (82.1) 59 (74.7) 237 (73.8)
    Haemorrhage infarct 4 (7.1) 5 (3.4) 0 (0.0) 1 (1.3) 10 (3.1)
    Haemorrhagic 12 (21.4) 30 (20.4) 7 (17.9) 17 (21.5) 66 (20.6)
    Ischaemic and Haemorrhagic 2 (3.6) 4 (2.7) 0 (0.0) 2 (2.5) 8 (2.5)
    Unknown 8 24 4 8 44
Stroke Type




0.863
    Intracerebral Hemorrhagic Stroke 17 (26.6) 35 (20.5) 8 (18.6) 16 (18.4) 76 (20.8)
    Ischemic Stroke 44 (68.8) 121 (70.8) 32 (74.4) 63 (72.4) 260 (71.2)
    Untyped Stroke (no CT scan available) 3 (4.7) 15 (8.8) 3 (7.0) 8 (9.2) 29 (7.9)
NIH Stroke Scale 4.5 (1.0, 8.0) 1.0 (0.0, 5.0) 4.0 (0.0, 9.0) 4.0 (0.0, 8.0) 2.0 (0.0, 7.0) 0.002
    Unknown 0 2 0 2 4
Modified Ranking Score 2.00 (1.00, 4.00) 2.00 (1.00, 3.00) 2.00 (1.00, 3.00) 2.00 (2.00, 3.00) 2.00 (1.00, 3.00) 0.149
Diabetes Mellitus 19 (29.7) 56 (32.7) 22 (51.2) 26 (29.9) 123 (33.7) 0.081
Hyperlipidemia 22 (34.4) 58 (33.9) 12 (27.9) 28 (32.2) 120 (32.9) 0.894
History of tobacco use 8 (12.5) 11 (6.7) 2 (4.7) 4 (4.7) 25 (7.0) 0.310
    Unknown 0 6 0 2 8
Body Mass Index 26.0 (22.9, 30.3) 25.8 (22.5, 29.1) 26.0 (24.1, 30.6) 26.2 (22.5, 30.3) 26.0 (22.7, 30.0) 0.825
    Unknown 2 4 2 0 8
Waist-Hip Ratio 0.93 (0.89, 0.95) 0.93 (0.89, 0.97) 0.93 (0.89, 0.98) 0.93 (0.87, 0.97) 0.93 (0.89, 0.96) 0.826
    Unknown 1 5 1 1 8
alcohol_curr 7 (10.9) 23 (13.5) 2 (4.7) 7 (8.0) 39 (10.7) 0.337
HBA1C (Baseline) 5.70 (5.40, 6.10) 5.90 (5.30, 6.80) 6.00 (5.40, 8.70) 5.75 (5.35, 6.30) 5.80 (5.40, 6.70) 0.164
    Unknown 7 41 6 15 69
Serum Creatinine (mmol/L) 88 (69, 106) 89 (73, 103) 90 (75, 97) 85 (68, 102) 87 (71, 104) 0.589
    Unknown 1 26 5 11 43
Systolic blood pressure (mm Hg)-Baseline 152 (145, 160) 154 (146, 168) 159 (152, 175) 164 (152, 180) 155 (148, 170) <0.001
Diastolic Blood Pressure 93 (86, 99) 95 (86, 102) 100 (89, 110) 98 (91, 108) 95 (88, 105) 0.006
ACE-Inhibitors




0.853
    Unchecked 59 (92.2) 156 (91.2) 39 (90.7) 82 (94.3) 336 (92.1)
    Checked 5 (7.8) 15 (8.8) 4 (9.3) 5 (5.7) 29 (7.9)
Angiotensin Receptor Blocker




0.765
    Unchecked 59 (92.2) 154 (90.1) 38 (88.4) 81 (93.1) 332 (91.0)
    Checked 5 (7.8) 17 (9.9) 5 (11.6) 6 (6.9) 33 (9.0)
Beta blockers




0.809
    Unchecked 62 (96.9) 165 (96.5) 42 (97.7) 82 (94.3) 351 (96.2)
    Checked 2 (3.1) 6 (3.5) 1 (2.3) 5 (5.7) 14 (3.8)
Calcium Channel Blocker




0.748
    Unchecked 33 (51.6) 93 (54.4) 26 (60.5) 51 (58.6) 203 (55.6)
    Checked 31 (48.4) 78 (45.6) 17 (39.5) 36 (41.4) 162 (44.4)
Diuretics




0.624
    Unchecked 63 (98.4) 163 (95.3) 42 (97.7) 82 (94.3) 350 (95.9)
    Checked 1 (1.6) 8 (4.7) 1 (2.3) 5 (5.7) 15 (4.1)
Alpha Methyl Dopa




0.131
    Unchecked 63 (98.4) 158 (92.4) 42 (97.7) 85 (97.7) 348 (95.3)
    Checked 1 (1.6) 13 (7.6) 1 (2.3) 2 (2.3) 17 (4.7)
Alpha Adrenergic Blockers




0.469
    Unchecked 63 (98.4) 167 (97.7) 43 (100.0) 87 (100.0) 360 (98.6)
    Checked 1 (1.6) 4 (2.3) 0 (0.0) 0 (0.0) 5 (1.4)
No. of Antihypertensives 1 (0, 1) 1 (0, 1) 0 (0, 1) 0 (0, 1) 1 (0, 1) 0.619
Antiplatelet 10 (16.1) 24 (14.6) 3 (8.1) 11 (13.6) 48 (14.0) 0.750
    Unknown 2 7 6 6 21
Statins 5 (8.1) 21 (13.4) 5 (13.9) 4 (4.9) 35 (10.4) 0.170
    Unknown 2 14 7 6 29
Total Hillbone Score 52.0 (50.0, 53.0) 53.0 (51.0, 53.0) 52.0 (48.0, 53.0) 53.0 (51.0, 53.0) 53.0 (50.0, 53.0) 0.483
    Unknown 1 3 0 2 6
Total HKQ Score 9 (7, 11) 9 (6, 10) 8 (7, 10) 8 (6, 10) 9 (6, 10) 0.495
    Unknown 3 7 2 3 15
Health Related QoL 10.00 (9.00, 12.00) 9.00 (8.00, 10.50) 10.00 (8.00, 10.00) 10.00 (9.00, 10.00) 10.00 (8.00, 11.00) 0.021
    Unknown 18 75 17 34 144
Health institution category




0.002
    Primary 20 (31.3) 36 (21.1) 22 (51.2) 34 (39.1) 112 (30.7)
    Secondary 19 (29.7) 41 (24.0) 5 (11.6) 16 (18.4) 81 (22.2)
    Tertiary 25 (39.1) 94 (55.0) 16 (37.2) 37 (42.5) 172 (47.1)
Antihypertensive treatment intensity 1.33 (0.83, 1.83) 1.50 (1.00, 2.10) 1.17 (0.83, 1.71) 1.33 (0.83, 1.75) 1.44 (0.83, 1.92) 0.128
    Unknown 10 22 3 8 43
1 Median (Q1, Q3); n (%)
2 Kruskal-Wallis rank sum test; Fisher’s Exact Test for Count Data with simulated p-value (based on 10000 replicates)

Figure 1

Show code 
df_paper_20 |> 
    select(pid, hpt_class, eventname, starts_with("sbp_m_")) |> 
    pivot_longer(cols = sbp_m_0:sbp_m_12) |> 
    group_by(hpt_class, name) %>% 
    reframe(across(value, ~epiDisplay::ci.numeric(.x))) %>% 
    unnest(value) |> 
    mutate(
        hpt_class = factor(
            hpt_class, 
            levels = c("Early", "Late", "Fluctuating", "Persistent")),
        Month = parse_number(name)) %>% 
    ggplot(
        aes(
            x = Month, 
            y = mean, 
            ymin = mean - se, 
            ymax = mean+ se,
            color = hpt_class)) +
    geom_errorbar(
        position = position_dodge2(width = 0.4),
        width = 0.2) +    
    geom_point(
        position = position_dodge2(width = 0.2),
        size = 3)+
    geom_line(aes(group = hpt_class)) +
    labs(
        x = "Month", 
        y = "Systolic Blood Pressure (mmHg)",
        color = NULL)+
    theme_bw()+
    scale_x_continuous(
        breaks = c(0, 1, 3, 6, 9, 12),
        labels = c(0, 1, 3, 6, 9, 12))

Figure 2 - Comparative graph

Show code 
df_paper_20 |> 
    select(pid, hpt_class, eventname, arm, starts_with("sbp_m_")) |> 
    pivot_longer(cols = sbp_m_0:sbp_m_12) |> 
    group_by(hpt_class, name, arm) %>% 
    reframe(across(value, ~epiDisplay::ci.numeric(.x))) %>% 
    unnest(value) |> 
    mutate(
        hpt_class = factor(
            hpt_class, 
            levels = c("Early", "Late", "Fluctuating", "Persistent")),
        Month = parse_number(name)) %>% 
    ggplot(
        aes(
            x = Month, 
            y = mean, 
            ymin = mean - se, 
            ymax = mean+ se,
            color = hpt_class)) +
    geom_errorbar(
        position = position_dodge2(width = 0.4),
        width = 0.2) +    
    geom_point(
        position = position_dodge2(width = 0.2),
        size = 3)+
    geom_line(aes(group = hpt_class)) +
    facet_wrap(vars(arm), nrow= 1) + 
    labs(
        x = "Month", 
        y = "Systolic Blood Pressure (mmHg)",
        color = NULL)+
    theme_bw()+
    scale_x_continuous(
        breaks = c(0, 1, 3, 6, 9, 12),
        labels = c(0, 1, 3, 6, 9, 12))+
    theme(legend.position = "bottom")

Poisson Regression version

Table 2 - Early vrs Late

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Late")) %>%
    mutate(y_binary = ifelse(hpt_class == "Late", 1, 0)) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = poisson),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N IRR 95% CI p-value
Study Arm 107


    Routine
— —
    Intervention
1.05 0.57, 2.05 0.880
Gender 107


    Male
— —
    Female
1.22 0.67, 2.22 0.517
Systolic blood pressure (mm Hg)-Baseline 107 1.01 1.00, 1.03 0.084
Diastolic Blood Pressure 107 1.02 1.00, 1.04 0.042
Stroke Type 107


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
1.32 0.64, 3.07 0.487
    Untyped Stroke (no CT scan available)
1.56 0.34, 5.40 0.510
NIH Stroke Scale 107 1.00 0.94, 1.06 0.994
Diabetes Mellitus 107


    No
— —
    Yes
1.69 0.92, 3.08 0.087
Body Mass Index 103 1.01 0.95, 1.07 0.835
Hyperlipidemia 107


    No
— —
    Yes
0.83 0.41, 1.58 0.586
History of tobacco use 107


    No
— —
    Yes
0.47 0.08, 1.54 0.301
Antihypertensive treatment intensity 94 0.97 0.61, 1.48 0.906
No. of Antihypertensives 107 0.96 0.65, 1.37 0.833
Total Hillbone Score 106 0.97 0.90, 1.06 0.436
Total HKQ Score 102 0.97 0.88, 1.08 0.591
Health Related QoL 72 0.89 0.72, 1.08 0.262
Health institution category 107


    Primary
— —
    Secondary
0.40 0.13, 0.97 0.063
    Tertiary
0.75 0.38, 1.41 0.370
Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio

Table 2 - Early vrs Fluctuating

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Fluctuating")) %>%
    mutate(y_binary = ifelse(hpt_class == "Fluctuating", 1, 0)) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = poisson),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N IRR 95% CI p-value
Study Arm 235


    Routine
— —
    Intervention
0.78 0.58, 1.06 0.112
Gender 235


    Male
— —
    Female
1.01 0.74, 1.37 0.948
Systolic blood pressure (mm Hg)-Baseline 235 1.00 0.99, 1.01 0.519
Diastolic Blood Pressure 235 1.00 0.99, 1.01 0.473
Stroke Type 235


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
1.09 0.76, 1.61 0.655
    Untyped Stroke (no CT scan available)
1.24 0.66, 2.22 0.489
NIH Stroke Scale 233 0.97 0.94, 1.01 0.127
Diabetes Mellitus 235


    No
— —
    Yes
1.04 0.75, 1.42 0.815
Body Mass Index 229 1.00 0.97, 1.03 0.970
Hyperlipidemia 235


    No
— —
    Yes
0.99 0.72, 1.36 0.973
History of tobacco use 229


    No
— —
    Yes
0.79 0.40, 1.39 0.449
Antihypertensive treatment intensity 203 1.10 0.90, 1.33 0.345
No. of Antihypertensives 235 1.03 0.88, 1.21 0.678
Total Hillbone Score 231 1.00 0.96, 1.05 0.851
Total HKQ Score 225 0.99 0.94, 1.04 0.558
Health Related QoL 142 0.91 0.82, 1.01 0.068
Health institution category 235


    Primary
— —
    Secondary
1.06 0.68, 1.67 0.789
    Tertiary
1.23 0.85, 1.83 0.293
Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio

Table 2 - Early vrs Persistent

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Persistent")) %>%
    mutate(y_binary = ifelse(hpt_class == "Persistent", 1, 0)) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = poisson),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N IRR 95% CI p-value
Study Arm 151


    Routine
— —
    Intervention
0.66 0.43, 1.01 0.057
Gender 151


    Male
— —
    Female
1.10 0.72, 1.67 0.670
Systolic blood pressure (mm Hg)-Baseline 151 1.01 1.00, 1.02 0.028
Diastolic Blood Pressure 151 1.01 1.00, 1.03 0.036
Stroke Type 151


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
1.21 0.72, 2.17 0.488
    Untyped Stroke (no CT scan available)
1.50 0.61, 3.41 0.349
NIH Stroke Scale 149 0.99 0.95, 1.04 0.766
Diabetes Mellitus 151


    No
— —
    Yes
1.00 0.62, 1.57 0.986
Body Mass Index 149 1.01 0.97, 1.05 0.642
Hyperlipidemia 151


    No
— —
    Yes
0.96 0.60, 1.49 0.854
History of tobacco use 149


    No
— —
    Yes
0.56 0.17, 1.35 0.263
Antihypertensive treatment intensity 133 1.03 0.75, 1.40 0.860
No. of Antihypertensives 151 0.97 0.74, 1.25 0.844
Total Hillbone Score 148 1.00 0.95, 1.07 0.980
Total HKQ Score 145 0.97 0.90, 1.04 0.384
Health Related QoL 99 0.92 0.80, 1.06 0.258
Health institution category 151


    Primary
— —
    Secondary
0.73 0.39, 1.29 0.291
    Tertiary
0.95 0.59, 1.52 0.822
Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio

Figure 3 - Sensitivity Analysis with Diastolic BP

Show code 
df_paper_20 |> 
    select(pid, hpt_class, eventname, starts_with("dbp_m_")) |> 
    pivot_longer(cols = dbp_m_0:dbp_m_12) |> 
    group_by(hpt_class, name) %>% 
    reframe(across(value, ~epiDisplay::ci.numeric(.x))) %>% 
    unnest(value) |> 
    mutate(
        hpt_class = factor(
            hpt_class, 
            levels = c("Early", "Late", "Fluctuating", "Persistent")),
        Month = parse_number(name)) %>% 
    ggplot(
        aes(
            x = Month, 
            y = mean, 
            ymin = mean - se, 
            ymax = mean+ se,
            color = hpt_class)) +
    geom_errorbar(
        position = position_dodge2(width = 0.4),
        width = 0.2) +    
    geom_point(
        position = position_dodge2(width = 0.2),
        size = 3)+
    geom_line(aes(group = hpt_class)) +
    labs(
        x = "Month", 
        y = "Diastolic Blood Pressure (mmHg)",
        color = NULL)+
    theme_bw()+
    scale_x_continuous(
        breaks = c(0, 1, 3, 6, 9, 12),
        labels = c(0, 1, 3, 6, 9, 12))

Table 3 - Early vrs Rest

Show code 
df_ready |> 
#    filter(hpt_class %in% c("Early", "Fluctuating")) %>%
    mutate(y_binary = ifelse(
      hpt_class %in% c("Late", "Fluctuating", "Persistent"), 0, 1)) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = poisson),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N IRR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
1.94 1.17, 3.31 0.012
Gender 365


    Male
— —
    Female
0.90 0.54, 1.47 0.667
Systolic blood pressure (mm Hg)-Baseline 365 0.98 0.97, 1.00 0.032
Diastolic Blood Pressure 365 0.98 0.96, 1.00 0.031
Stroke Type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.76 0.44, 1.36 0.329
    Untyped Stroke (no CT scan available)
0.46 0.11, 1.38 0.218
NIH Stroke Scale 361 1.04 0.99, 1.08 0.094
Diabetes Mellitus 365


    No
— —
    Yes
0.83 0.47, 1.40 0.498
Body Mass Index 357 0.99 0.95, 1.04 0.749
Hyperlipidemia 365


    No
— —
    Yes
1.07 0.63, 1.77 0.799
History of tobacco use 357


    No
— —
    Yes
1.90 0.84, 3.75 0.090
Antihypertensive treatment intensity 322 0.82 0.57, 1.17 0.292
No. of Antihypertensives 365 0.95 0.71, 1.25 0.752
Total Hillbone Score 359 1.00 0.94, 1.07 0.986
Total HKQ Score 350 1.06 0.97, 1.16 0.241
Health Related QoL 221 1.20 1.04, 1.37 0.009
Health institution category 365


    Primary
— —
    Secondary
1.31 0.70, 2.47 0.395
    Tertiary
0.81 0.45, 1.48 0.493
Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio

Table 4 - Early/Late vrs Rest

Show code 
df_ready |> 
#    filter(hpt_class %in% c("Early", "Fluctuating")) %>%
    mutate(y_binary = ifelse(
      hpt_class %in% c("Fluctuating", "Persistent"), 0, 1)) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = poisson),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N IRR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
2.00 1.35, 3.02 <0.001
Gender 365


    Male
— —
    Female
1.03 0.70, 1.50 0.894
Systolic blood pressure (mm Hg)-Baseline 365 1.0 0.98, 1.00 0.331
Diastolic Blood Pressure 365 1.0 0.98, 1.01 0.461
Stroke Type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.89 0.57, 1.42 0.609
    Untyped Stroke (no CT scan available)
0.63 0.23, 1.44 0.308
NIH Stroke Scale 361 1.04 1.00, 1.07 0.031
Diabetes Mellitus 365


    No
— —
    Yes
1.22 0.82, 1.80 0.313
Body Mass Index 357 1.00 0.96, 1.03 0.834
Hyperlipidemia 365


    No
— —
    Yes
0.95 0.63, 1.42 0.808
History of tobacco use 357


    No
— —
    Yes
1.37 0.67, 2.49 0.344
Antihypertensive treatment intensity 322 0.81 0.61, 1.06 0.131
No. of Antihypertensives 365 0.93 0.74, 1.16 0.537
Total Hillbone Score 359 0.98 0.94, 1.03 0.427
Total HKQ Score 350 1.03 0.97, 1.11 0.328
Health Related QoL 221 1.13 1.01, 1.26 0.030
Health institution category 365


    Primary
— —
    Secondary
0.79 0.47, 1.29 0.357
    Tertiary
0.64 0.41, 0.98 0.039
Abbreviations: CI = Confidence Interval, IRR = Incidence Rate Ratio

Logistic Regression version

Table 2 New - Early vrs Late

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Late")) %>%
    mutate(
      y_binary = ifelse(hpt_class == "Late", "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 107


    Routine
— —
    Intervention
0.92 0.40, 2.08 0.845
Gender 107


    Male
— —
    Female
0.72 0.33, 1.56 0.402
Systolic blood pressure (mm Hg)-Baseline 107 0.98 0.95, 1.00 0.030
Diastolic Blood Pressure 107 0.96 0.93, 0.99 0.011
Stroke Type 107


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.65 0.24, 1.65 0.372
    Untyped Stroke (no CT scan available)
0.47 0.07, 3.03 0.414
NIH Stroke Scale 107 1.00 0.93, 1.08 0.992
Diabetes Mellitus 107


    No
— —
    Yes
0.40 0.18, 0.89 0.027
Body Mass Index 103 0.99 0.91, 1.07 0.788
Hyperlipidemia 107


    No
— —
    Yes
1.35 0.59, 3.21 0.482
History of tobacco use 107


    No
— —
    Yes
2.93 0.69, 20.1 0.188
Antihypertensive treatment intensity 94 1.05 0.58, 1.90 0.877
No. of Antihypertensives 107 1.07 0.67, 1.73 0.785
Total Hillbone Score 106 1.06 0.95, 1.19 0.316
Total HKQ Score 102 1.05 0.91, 1.21 0.489
Health Related QoL 72 1.20 0.94, 1.58 0.166
Health institution category 107


    Primary
— —
    Secondary
4.18 1.39, 14.5 0.015
    Tertiary
1.72 0.72, 4.16 0.223
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 2 New - Early vrs Fluctuating

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Fluctuating")) %>%
    mutate(
      y_binary = ifelse(hpt_class == "Fluctuating", "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 235


    Routine
— —
    Intervention
2.50 1.39, 4.61 0.003
Gender 235


    Male
— —
    Female
0.96 0.53, 1.72 0.901
Systolic blood pressure (mm Hg)-Baseline 235 0.99 0.97, 1.01 0.219
Diastolic Blood Pressure 235 0.98 0.96, 1.01 0.172
Stroke Type 235


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.75 0.38, 1.49 0.400
    Untyped Stroke (no CT scan available)
0.41 0.09, 1.46 0.204
NIH Stroke Scale 233 1.09 1.03, 1.16 0.005
Diabetes Mellitus 235


    No
— —
    Yes
0.87 0.46, 1.60 0.654
Body Mass Index 229 1.00 0.95, 1.05 0.942
Hyperlipidemia 235


    No
— —
    Yes
1.02 0.55, 1.86 0.948
History of tobacco use 229


    No
— —
    Yes
2.00 0.74, 5.20 0.157
Antihypertensive treatment intensity 203 0.67 0.43, 1.02 0.069
No. of Antihypertensives 235 0.88 0.62, 1.20 0.428
Total Hillbone Score 231 0.98 0.91, 1.07 0.718
Total HKQ Score 225 1.06 0.96, 1.18 0.263
Health Related QoL 142 1.35 1.12, 1.64 0.002
Health institution category 235


    Primary
— —
    Secondary
0.83 0.38, 1.81 0.645
    Tertiary
0.48 0.24, 0.97 0.040
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 2 New - Early vrs Persistent

Show code 
df_ready |> 
    filter(hpt_class %in% c("Early", "Persistent")) %>%
    mutate(
      y_binary = ifelse(hpt_class == "Persistent", "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 151


    Routine
— —
    Intervention
2.70 1.40, 5.35 0.004
Gender 151


    Male
— —
    Female
0.80 0.42, 1.54 0.513
Systolic blood pressure (mm Hg)-Baseline 151 0.97 0.94, 0.99 0.001
Diastolic Blood Pressure 151 0.96 0.93, 0.98 0.002
Stroke Type 151


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.66 0.30, 1.44 0.294
    Untyped Stroke (no CT scan available)
0.35 0.07, 1.46 0.171
NIH Stroke Scale 149 1.02 0.95, 1.08 0.650
Diabetes Mellitus 151


    No
— —
    Yes
0.99 0.49, 2.00 0.979
Body Mass Index 149 0.98 0.92, 1.04 0.472
Hyperlipidemia 151


    No
— —
    Yes
1.10 0.55, 2.19 0.777
History of tobacco use 149


    No
— —
    Yes
2.89 0.87, 11.3 0.095
Antihypertensive treatment intensity 133 0.93 0.56, 1.53 0.782
No. of Antihypertensives 151 1.06 0.71, 1.58 0.762
Total Hillbone Score 148 1.00 0.91, 1.10 0.969
Total HKQ Score 145 1.08 0.97, 1.22 0.182
Health Related QoL 99 1.18 0.97, 1.46 0.102
Health institution category 151


    Primary
— —
    Secondary
2.02 0.86, 4.85 0.111
    Tertiary
1.15 0.54, 2.45 0.717
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 3 - Early vrs Rest

Show code 
df_ready |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Late", "Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
2.22 1.28, 3.96 0.005
Gender 365


    Male
— —
    Female
0.88 0.50, 1.51 0.636
Systolic blood pressure (mm Hg)-Baseline 365 0.98 0.96, 1.00 0.020
Diastolic Blood Pressure 365 0.98 0.95, 1.00 0.018
Stroke Type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.71 0.38, 1.35 0.280
    Untyped Stroke (no CT scan available)
0.40 0.09, 1.32 0.171
NIH Stroke Scale 361 1.05 1.00, 1.10 0.066
Diabetes Mellitus 365


    No
— —
    Yes
0.80 0.44, 1.42 0.455
Body Mass Index 357 0.99 0.94, 1.04 0.725
Hyperlipidemia 365


    No
— —
    Yes
1.09 0.61, 1.90 0.779
History of tobacco use 357


    No
— —
    Yes
2.32 0.91, 5.49 0.063
Antihypertensive treatment intensity 322 0.79 0.53, 1.16 0.249
No. of Antihypertensives 365 0.95 0.68, 1.28 0.728
Total Hillbone Score 359 1.00 0.93, 1.08 0.985
Total HKQ Score 350 1.07 0.97, 1.18 0.197
Health Related QoL 221 1.28 1.08, 1.51 0.004
Health institution category 365


    Primary
— —
    Secondary
1.41 0.69, 2.86 0.340
    Tertiary
0.78 0.41, 1.50 0.454
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 4 - Early/Late vrs Rest

Show code 
df_ready |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
2.65 1.67, 4.28 <0.001
Gender 365


    Male
— —
    Female
1.04 0.66, 1.63 0.874
Systolic blood pressure (mm Hg)-Baseline 365 0.99 0.98, 1.00 0.250
Diastolic Blood Pressure 365 0.99 0.98, 1.01 0.381
Stroke Type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.84 0.49, 1.47 0.540
    Untyped Stroke (no CT scan available)
0.53 0.18, 1.41 0.225
NIH Stroke Scale 361 1.06 1.01, 1.11 0.011
Diabetes Mellitus 365


    No
— —
    Yes
1.33 0.83, 2.13 0.230
Body Mass Index 357 1.0 0.95, 1.04 0.804
Hyperlipidemia 365


    No
— —
    Yes
0.93 0.57, 1.50 0.773
History of tobacco use 357


    No
— —
    Yes
1.62 0.68, 3.68 0.260
Antihypertensive treatment intensity 322 0.75 0.53, 1.02 0.074
No. of Antihypertensives 365 0.91 0.69, 1.17 0.463
Total Hillbone Score 359 0.97 0.92, 1.03 0.346
Total HKQ Score 350 1.05 0.97, 1.14 0.245
Health Related QoL 221 1.22 1.05, 1.41 0.009
Health institution category 365


    Primary
— —
    Secondary
0.70 0.38, 1.29 0.256
    Tertiary
0.52 0.31, 0.88 0.014
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Multivariable logistic regression

Note
  • Cutoff of 0.2 used for both multivariable regression
  • Systolic BP dropped in Early vs Rest because of colinearity
Show code 
df_mv1 <- 
  df_ready |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Late", "Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, ee_sbp_0, nihss_scale, ee_dbp_0, 
        tobacco_use, eq_5d, aa_hkq)

mv1 <-
  glm(
      y_binary ~ arm +  ee_dbp_0 + nihss_scale + tobacco_use + eq_5d + aa_hkq, 
                   data = df_mv1, 
                   family = binomial(link = "logit")) |> 
    gtsummary::tbl_regression(
    exponentiate = TRUE) |> 
    gtsummary::bold_p(t = 0.05) 
  
    

df_mv2 <- 
  df_ready |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, nihss_scale, dose_index, eq_5d, hosp_cat)

mv2 <- 
  glm(
      y_binary ~ arm + nihss_scale + dose_index + eq_5d + hosp_cat, 
                   data = df_mv2, 
                   family = binomial(link = "logit")) |> 
    gtsummary::tbl_regression(
    exponentiate = TRUE) |> 
    gtsummary::bold_p(t = 0.05) 

final_table <- 
  gtsummary::tbl_merge(
    tbls = list(mv1, mv2), 
    tab_spanner = c("**Early Vs Rest**", "**Early/Late Vs Rest**")) |> 
  gtsummary::bold_labels()

final_table
Characteristic
Early Vs Rest
Early/Late Vs Rest
OR 95% CI p-value OR 95% CI p-value
Study Arm





    Routine — —
— —
    Intervention 2.68 1.30, 5.74 0.009 4.25 2.20, 8.54 <0.001
Diastolic Blood Pressure 0.97 0.94, 1.00 0.047


NIH Stroke Scale 0.97 0.89, 1.07 0.6 0.98 0.90, 1.06 0.6
History of tobacco use





    No — —



    Yes 2.23 0.68, 6.75 0.2


Health Related QoL 1.41 1.11, 1.81 0.006 1.25 1.01, 1.56 0.047
Total HKQ Score 1.05 0.93, 1.19 0.4


Antihypertensive treatment intensity


0.59 0.36, 0.94 0.034
Health institution category





    Primary


— —
    Secondary


1.29 0.56, 3.00 0.6
    Tertiary


0.86 0.38, 1.96 0.7
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Mice Logistic Regression

Create MICE Data

Show code 
df_ready_mice <- 
  df_ready |> 
    select(
        hpt_class, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    mutate(hpt_class = factor(hpt_class)) %>% 
    drop_na(hpt_class) %>% 
    mutate(across(where(is.character), as.factor)) %>% 
    mice::mice(m = 5, method = 'pmm', seed = 510) |> 
    mice::complete(2)

 iter imp variable
  1   1  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  1   2  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  1   3  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  1   4  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  1   5  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  2   1  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  2   2  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  2   3  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  2   4  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  2   5  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  3   1  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  3   2  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  3   3  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  3   4  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  3   5  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  4   1  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  4   2  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  4   3  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  4   4  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  4   5  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  5   1  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  5   2  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  5   3  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  5   4  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d
  5   5  nihss_scale  bmi  tobacco_use  dose_index  hillbone  aa_hkq  eq_5d

Table 3 - Early vrs Rest

Show code 
df_ready_mice |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Late", "Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
2.22 1.28, 3.96 0.005
Gender 365


    Male
— —
    Female
0.88 0.50, 1.51 0.636
Systolic blood pressure (mm Hg)-Baseline 365 0.98 0.96, 1.00 0.020
Diastolic Blood Pressure 365 0.98 0.95, 1.00 0.018
d_st_type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.71 0.38, 1.35 0.280
    Untyped Stroke (no CT scan available)
0.40 0.09, 1.32 0.171
NIH Stroke Scale 365 1.05 0.99, 1.10 0.086
dm 365


    No
— —
    Yes
0.80 0.44, 1.42 0.455
Body Mass Index 365 0.99 0.94, 1.04 0.729
hyperlipidemia 365


    No
— —
    Yes
1.09 0.61, 1.90 0.779
tobacco_use 365


    No
— —
    Yes
2.25 0.88, 5.27 0.072
Antihypertensive treatment intensity 365 0.81 0.57, 1.13 0.230
No. of Antihypertensives 365 0.95 0.68, 1.28 0.728
Total Hillbone Score 365 1.00 0.93, 1.08 0.977
Total HKQ Score 365 1.07 0.97, 1.18 0.181
Health Related QoL 365 1.31 1.14, 1.52 <0.001
hosp_cat 365


    Primary
— —
    Secondary
1.41 0.69, 2.86 0.340
    Tertiary
0.78 0.41, 1.50 0.454
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Table 4 - Early/Late vrs Rest

Show code 
df_ready_mice |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, a_gender, ee_sbp_0, ee_dbp_0, 
        d_st_type, nihss_scale, dm, bmi, hyperlipidemia,
        tobacco_use, dose_index, no_of_hpts, hillbone, aa_hkq, eq_5d, 
        hosp_cat) |> 
    gtsummary::tbl_uvregression(
        method = glm,
        y = y_binary,
        method.args = list(family = binomial),
        exponentiate = TRUE, 
        pvalue_fun = function(x) style_pvalue(x, digits = 3)) |>
    gtsummary::bold_labels() |> 
    gtsummary::bold_p()
Characteristic N OR 95% CI p-value
Study Arm 365


    Routine
— —
    Intervention
2.65 1.67, 4.28 <0.001
Gender 365


    Male
— —
    Female
1.04 0.66, 1.63 0.874
Systolic blood pressure (mm Hg)-Baseline 365 0.99 0.98, 1.00 0.250
Diastolic Blood Pressure 365 0.99 0.98, 1.01 0.381
d_st_type 365


    Intracerebral Hemorrhagic Stroke
— —
    Ischemic Stroke
0.84 0.49, 1.47 0.540
    Untyped Stroke (no CT scan available)
0.53 0.18, 1.41 0.225
NIH Stroke Scale 365 1.05 1.01, 1.10 0.017
dm 365


    No
— —
    Yes
1.33 0.83, 2.13 0.230
Body Mass Index 365 1.00 0.96, 1.04 0.881
hyperlipidemia 365


    No
— —
    Yes
0.93 0.57, 1.50 0.773
tobacco_use 365


    No
— —
    Yes
1.56 0.66, 3.51 0.291
Antihypertensive treatment intensity 365 0.73 0.54, 0.98 0.038
No. of Antihypertensives 365 0.91 0.69, 1.17 0.463
Total Hillbone Score 365 0.97 0.92, 1.03 0.325
Total HKQ Score 365 1.04 0.96, 1.12 0.357
Health Related QoL 365 1.26 1.11, 1.43 <0.001
hosp_cat 365


    Primary
— —
    Secondary
0.70 0.38, 1.29 0.256
    Tertiary
0.52 0.31, 0.88 0.014
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Multivariable logistic regression

Note
  • Cutoff of 0.2 used for both multivariable regression
  • Systolic BP dropped in Early vs Rest because of colinearity
Show code 
df_mv1 <- 
  df_ready_mice |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Late", "Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, ee_sbp_0, nihss_scale, ee_dbp_0, 
        tobacco_use, eq_5d, aa_hkq)

mv1 <-
  glm(
      y_binary ~ arm +  ee_dbp_0 + nihss_scale + tobacco_use + eq_5d + + aa_hkq, 
                   data = df_mv1, 
                   family = binomial(link = "logit")) |> 
    gtsummary::tbl_regression(
    exponentiate = TRUE) |> 
    gtsummary::bold_p(t = 0.05) 
    

df_mv2 <- 
  df_ready |> 
    mutate(
      y_binary = ifelse(
        hpt_class %in% c("Fluctuating", "Persistent"), "No", "Yes") |> 
        factor()) |> 
    select(
        y_binary, arm, nihss_scale, dose_index, eq_5d, hosp_cat)

mv2 <- 
  glm(
      y_binary ~ arm + nihss_scale + dose_index + eq_5d + hosp_cat, 
                   data = df_mv2, 
                   family = binomial(link = "logit")) |> 
    gtsummary::tbl_regression(
    exponentiate = TRUE) |> 
    gtsummary::bold_p(t = 0.05) 

final_table <- 
  gtsummary::tbl_merge(
    tbls = list(mv1, mv2), 
    tab_spanner = c("**Early Vs Rest**", "**Early/Late Vs Rest**")) |> 
  gtsummary::bold_labels()

final_table
Characteristic
Early Vs Rest
Early/Late Vs Rest
OR 95% CI p-value OR 95% CI p-value
Study Arm





    Routine — —
— —
    Intervention 2.25 1.26, 4.13 0.007 4.25 2.20, 8.54 <0.001
Diastolic Blood Pressure 0.97 0.95, 1.0 0.018


NIH Stroke Scale 0.96 0.89, 1.04 0.3 0.98 0.90, 1.06 0.6
tobacco_use





    No — —



    Yes 2.17 0.82, 5.35 0.10


Health Related QoL 1.48 1.21, 1.82 <0.001 1.25 1.01, 1.56 0.047
Total HKQ Score 1.08 0.98, 1.20 0.13


Antihypertensive treatment intensity


0.59 0.36, 0.94 0.034
Health institution category





    Primary


— —
    Secondary


1.29 0.56, 3.00 0.6
    Tertiary


0.86 0.38, 1.96 0.7
Abbreviations: CI = Confidence Interval, OR = Odds Ratio