DSMB Analysis

Data preparation

Table 1 - Preparation

df_table_1 <- 
    dget("pings_2_data_ready") %>% #labelled::look_for("adverse")
    filter(eventname == "Baseline") %>% 
    mutate(
        hosp_cat = case_when(
            datagrp %in% c(
                "CCTH SITE", "KATH SITE", "KORLE-BU SITE") ~ "Tertiary",
            datagrp %in% c(
                "KUMASI SOUTH HOSPITAL", "AGOGO SITE", "KNUST SITE") ~ "Secondary",
            datagrp %in% c(
                "ANKAASE SITE", "MANHYIA GOVT HOSPITAL", "KWADASO SITE", "TAFO SITE") ~ 
                "Primary"),
        maristat_2 = case_when(
            a_maristat == "Never Married"  ~ "Never Married",
            a_maristat %in% c("Currently Married", "Cohabitating") ~ 
                "Currently Married",
            a_maristat %in% c("Separated","Widow/Widower", "Divorced") ~ 
                "Previously married"),
        mod_rank_score = parse_number(as.character(x_ranking))) %>% 
    select(
        a_gender, a_maristat, a_formeduc, hosp_cat, a_religion, 
        maristat_2, a_domicile, a_income, ee_bmi_0, d_st_type, 
        d_st_type, mod_rank_score, arm) %>% 
    mutate(
        arm  = case_when(
        arm == "Arm 1- Intervention Arm" ~ "Intervention",
        arm == "Arm 2- Routine Care" ~ "Routine Care"))

Table 2 preparation

df_temp <- 
    dget("pings_2_data_ready") %>% 
    filter(eventname == "Baseline") %>% 
    select(pid, arm)
    
df_table_2 <- 
    dget("df_pings2_bp_data") %>% 
    rename(pid = `RedCap ID`) %>% 
    full_join(df_temp) %>% 
    select(
        arm, ee_sbp_0 = sbp_m0,  ee_dbp_0 = dbp_m0, sbp_m1, 
        dbp_m1, ee_sbp_3 = sbp_m3, ee_dbp_3 = dbp_m3, sbp_m6, 
        dbp_m6, sbp_m9, dbp_m9, ee_sbp_12 = sbp_m12, 
        ee_dbp_12 = dbp_m12) %>% 
    mutate(
        arm  = case_when(
        arm == "Arm 1- Intervention Arm" ~ "Intervention",
        arm == "Arm 2- Routine Care" ~ "Routine Care"))

Joining with `by = join_by(pid)`

Table 3 preparation

df_table_3_qol <- 
    dget("pings_2_data_ready") %>% #labelled::look_for("health")
    filter(eventname %in% c("Baseline", "Month12")) %>% 
    mutate(
        mobility = case_when(
            str_detect(
                p_mobility , "I have no problems in walking about") ~ "1",
            str_detect(
                p_mobility , "I have some problems in walking about") ~ "2",
            str_detect(
                p_mobility , "I am confined to bed") ~ "3")%>% 
            as.numeric(),
        selfcare = case_when(
            str_detect(
                p_selfcare , "I have no problems with self-care") ~ "1",
            str_detect(
                p_selfcare , "I have some problems with washing or") ~ "2",
            str_detect(
                p_selfcare , "I am unable to wash or dress myself") ~ "3")%>%
            as.numeric(),
        usual_act = case_when(
            str_detect(
                p_usual_act , "I have no problems with performing my") ~ "1",
            str_detect(
                p_usual_act , "I have some problems with performing") ~ "2",
            str_detect(
                p_usual_act , "I am unable to perform my usual activitie") ~
                "3")%>% 
            as.numeric(),
        pain_disc = case_when(
            str_detect(p_pain_disc , "I have no pain or discomfort") ~ "1",
            str_detect(
                p_pain_disc , "I have moderate pain or discomfort") ~ "2",
            str_detect(
                p_pain_disc , "I have extreme pain or discomfort") ~ "3") %>%
            as.numeric(),
        anxiety = case_when(
            str_detect(p_anxiety, "I am not anxious or depressed") ~ "1",
            str_detect(
                p_anxiety, "I am moderately anxious or depressed") ~ "2",
            str_detect(
                p_anxiety, "I am extremely anxious or depressed") ~ "3")%>% 
            as.numeric(),
        eq_5d = mobility + selfcare + usual_act + pain_disc + anxiety) %>% 
    select(pid, eq_5d, arm, eventname) %>% 
    pivot_wider(
        id_cols = pid, names_from = eventname, values_from = eq_5d) %>% 
    full_join(df_temp)

Joining with `by = join_by(pid)`

df_table_3_hillbone <- 
    dget("pings_2_data_ready") %>%
    filter(eventname %in% c("Baseline", "Month12")) %>% 
    mutate(
        across(
            c(l_forget, l_decide, l_salty, l_shake, l_fasfood, l_appoint, 
            l_missched, l_prescrip, l_runout, l_skipmed, l_feelbet,
            l_feelsick, l_someone, l_careless), 
            .fns = ~factor(.x),
            .names = "{.col}_unclassed"),
        across(
            c(l_forget_unclassed, l_decide_unclassed, l_salty_unclassed, 
              l_shake_unclassed, l_fasfood_unclassed, l_appoint_unclassed, 
            l_missched_unclassed, l_prescrip_unclassed, l_runout_unclassed, 
            l_skipmed_unclassed, l_feelbet_unclassed, l_feelsick_unclassed, 
            l_someone_unclassed, l_careless_unclassed), 
            .fns = ~unclass(.x)),
        hillbone = (
            l_forget_unclassed + l_decide_unclassed + 
            l_salty_unclassed + l_shake_unclassed + 
            l_fasfood_unclassed + l_appoint_unclassed + 
            l_missched_unclassed + l_prescrip_unclassed + 
            l_runout_unclassed + l_skipmed_unclassed + 
            l_feelbet_unclassed + l_feelsick_unclassed + 
            l_someone_unclassed + l_careless_unclassed) %>%  
            as.numeric(),
        ) %>% 
    select(pid, hillbone, arm, eventname) %>% 
    pivot_wider(
        id_cols = pid, names_from = eventname, values_from = hillbone) %>% 
    full_join(df_temp)

Joining with `by = join_by(pid)`

a <- function(x){
    as.character(x) %>% 
    str_to_sentence() %>% 
    ifelse(str_detect(x, "Nil"), "No", x)
    }

df_temp_2 <- 
    dget("pings_2_data_ready") %>% 
    filter(eventname == "Month12") %>% 
    select(kk_status, pid) %>% 
    mutate(status = kk_status == "Dead") %>% 
    select(pid, status)

df_adverse_event <-
    dget("pings_2_data_ready") %>% 
    select(
        starts_with("gg_events"), starts_with("gg_stroke"),
        starts_with("gg_heart"),  starts_with("gg_stemi"), 
        starts_with("gg_cdeath"), starts_with("gg_renalf"), 
        starts_with("gg_emerg"), eventname, arm, pid) %>% 
    filter(eventname == "Baseline") %>% 
    mutate(
        across(starts_with("gg_"), ~as.character(.x)),
        across(starts_with("gg_"), ~str_to_sentence(.x)),
        across(starts_with("gg_"), ~ifelse(.x == "", "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "Nil"), "0", .x)), 
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "No"), "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "Nnil"), "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "N/a"), "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "Nik"), "0", .x)),
        across(starts_with("gg_"), ~ifelse(.x == "Ni", "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "X"), "0", .x)),
        across(starts_with("gg_"), ~ifelse(str_detect(.x, "Yes"), "1", .x)),
        across(starts_with("gg_"), ~ifelse(.x != "0", "1", .x)),
        across(starts_with("gg_"), ~as.numeric(.x))) %>% 
    rowwise() %>% 
    mutate(
        gg_stroke = gg_stroke_1 + gg_stroke_3 + 
            gg_stroke_6 + gg_stroke_9 + gg_stroke_12,
        gg_stemi = gg_stemi_0 + gg_stemi_1 + gg_stemi_3 + 
            gg_stemi_6 + gg_stemi_9 + gg_stemi_12,
        gg_cdeath = gg_cdeath_0 + gg_cdeath_1 + gg_cdeath_3 + 
            gg_cdeath_6 + gg_cdeath_9 + gg_cdeath_12) %>% 
    select(pid, arm, gg_stroke, gg_cdeath, gg_stemi) %>% 
    mutate(gg_stroke = ifelse(gg_stroke > 0, 1, gg_stroke)) %>% 
    full_join(df_temp_2) %>% 
    select(-pid)

Joining with `by = join_by(pid)`

df_hpt_stroke_know <- 
    dget("pings_2_data_ready") %>%
    mutate(
        aa_bp_115_75_corrrect = ifelse(
            aa_bp_115_75 == "Normal", "Yes", "No"),
        aa_bp160_100_correct = ifelse(
            aa_bp160_100 == "High", "Yes", "No"),
        aa_hptlasts_correct = ifelse(
            aa_hptlasts == "The Rest of their Life", "Yes", "No"),
        aa_hptmeds_correct = ifelse(aa_hptmeds == "Everyday", "Yes", "No"),
        aa_losewght_correct = ifelse(aa_losewght == "Go Down", "Yes", "No"),
        aa_eatsalt_correct = ifelse(aa_eatsalt == "Go Down", "Yes", "No"),
        aa_hrtattack_correct = ifelse(aa_hrtattack == "Yes", "Yes", "No"),
        aa_cancer_correct = ifelse(aa_cancer == "No", "Yes", "No"),
        aa_stroke_correct = ifelse(aa_stroke == "Yes", "Yes", "No"),
        aa_kidneyp_correct = ifelse(aa_kidneyp == "Yes", "Yes", "No"),
        aa_highrisk_correct = ifelse(aa_highrisk == "Yes", "Yes", "No"),
        aa_headache_correct = ifelse(aa_headache == "No", "Yes", "No"),
        aa_feelgood_correct = ifelse(aa_feelgood == "Never", "Yes", "No"),
        aa_strokeris_correct = ifelse(
            aa_strokeris == "The Rest of Their Life", "Yes", "No"),
        aa_hkq = (aa_bp_115_75_corrrect == "Yes") + 
            (aa_bp160_100_correct == "Yes") +
            (aa_hptlasts_correct =="Yes") + 
            (aa_hptmeds_correct == "Yes") + 
            (aa_losewght_correct == "Yes") + 
            (aa_eatsalt_correct == "Yes") + 
            (aa_hrtattack_correct == "Yes") + 
            (aa_cancer_correct == "Yes") + 
            (aa_stroke_correct == "Yes") + 
            (aa_kidneyp_correct == "Yes") + 
            (aa_highrisk_correct == "Yes") + 
            (aa_headache_correct =="Yes") + 
            (aa_feelgood_correct =="Yes") + 
            (aa_strokeris_correct == "Yes")) %>% 
    select(arm, aa_hkq, eventname, pid) %>% 
    filter(eventname %in% c("Baseline","Month12")) %>% 
    pivot_wider(
        id_cols = pid, 
        values_from = aa_hkq, 
        names_from = eventname) %>% 
    full_join(df_temp) %>% 
    select(-pid)

Joining with `by = join_by(pid)`

df_ranking <- 
    dget("pings_2_data_ready") %>%
    filter(eventname %in% c("Baseline","Month12")) %>%
    mutate(mod_rank_score = parse_number(as.character(x_ranking))) %>% 
    select(mod_rank_score, arm, eventname, pid) %>% 
    pivot_wider(
        id_cols = pid, 
        values_from = mod_rank_score, 
        names_from = eventname) %>% 
    full_join(df_temp) %>% 
    select(-pid)

Joining with `by = join_by(pid)`

df_ranking

# A tibble: 500 × 3
   Baseline Month12 arm                    
      <dbl>   <dbl> <fct>                  
 1        4       2 Arm 2- Routine Care    
 2        4       3 Arm 1- Intervention Arm
 3        2       0 Arm 1- Intervention Arm
 4        2       1 Arm 1- Intervention Arm
 5        4       3 Arm 2- Routine Care    
 6        2       1 Arm 2- Routine Care    
 7        1       0 Arm 2- Routine Care    
 8        1       2 Arm 1- Intervention Arm
 9        2       1 Arm 2- Routine Care    
10        4       2 Arm 1- Intervention Arm
# ℹ 490 more rows

Table 1

gtsummary::theme_gtsummary_compact()

Setting theme "Compact"

df_table_1 %>% 
    gtsummary::tbl_summary(
        digits = gtsummary::all_categorical()~ c(0,1),
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            hosp_cat = "Health Institution category",
            maristat_2 = "Marital Status",
            mod_rank_score = "Modified Ranking Score"),
        type = list(mod_rank_score ~ "continuous")) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::modify_caption(
        "**Table 1**: Demographic Characteristics of Study Participants") 

Table 1: Demographic Characteristics of Study Participants

Characteristic	Overall N = 500 1	Intervention N = 244 1	Routine Care N = 256 1	p-value 2
Gender				0.5
Male	281 (56.2)	141 (57.8)	140 (54.7)
Female	219 (43.8)	103 (42.2)	116 (45.3)
Marital Status				0.5
Never Married	23 (4.6)	11 (4.5)	12 (4.7)
Currently Married	325 (65.0)	164 (67.2)	161 (62.9)
Separated	38 (7.6)	22 (9.0)	16 (6.3)
Widow/Widower	84 (16.8)	35 (14.3)	49 (19.1)
Cohabitating	8 (1.6)	3 (1.2)	5 (2.0)
Divorced	22 (4.4)	9 (3.7)	13 (5.1)
Level of Formal education				0.2
None	49 (9.8)	17 (7.0)	32 (12.5)
Primary	204 (40.8)	96 (39.3)	108 (42.2)
Secondary	164 (32.8)	88 (36.1)	76 (29.7)
Tertiary	72 (14.4)	37 (15.2)	35 (13.7)
Postgraduate	11 (2.2)	6 (2.5)	5 (2.0)
Health Institution category				0.8
Primary	148 (29.6)	73 (29.9)	75 (29.3)
Secondary	119 (23.8)	55 (22.5)	64 (25.0)
Tertiary	233 (46.6)	116 (47.5)	117 (45.7)
Religion				0.7
Christianity	448 (89.6)	221 (90.6)	227 (88.7)
Islam	49 (9.8)	22 (9.0)	27 (10.5)
Other	3 (0.6)	1 (0.4)	2 (0.8)
Marital Status				0.7
Currently Married	333 (66.6)	167 (68.4)	166 (64.8)
Never Married	23 (4.6)	11 (4.5)	12 (4.7)
Previously married	144 (28.8)	66 (27.0)	78 (30.5)
Domicile				0.8
Rural	33 (6.6)	18 (7.4)	15 (5.9)
Semi-Urban	166 (33.2)	80 (32.8)	86 (33.6)
Urban	301 (60.2)	146 (59.8)	155 (60.5)
Income Bracket				>0.9
0-100	174 (35.1)	81 (33.6)	93 (36.5)
101-250	149 (30.0)	74 (30.7)	75 (29.4)
251-500	109 (22.0)	54 (22.4)	55 (21.6)
501-1500	43 (8.7)	22 (9.1)	21 (8.2)
1501-3000	15 (3.0)	7 (2.9)	8 (3.1)
> 3000	6 (1.2)	3 (1.2)	3 (1.2)
Missing	4	3	1
BMI (kg/m2)- Baseline	26.4 (22.7, 30.5)	26.6 (23.1, 30.9)	26.3 (22.5, 29.7)	0.5
Missing	71	34	37
Stroke Type (Choose One)				0.013
Ischemic Stroke	319 (73.5)	163 (75.8)	156 (71.2)
Intracerebral Hemorrhagic Stroke	100 (23.0)	44 (20.5)	56 (25.6)
Ischemic With Hemorrhagic Transformation	10 (2.3)	8 (3.7)	2 (0.9)
Untyped Stroke (no CT scan available)	5 (1.2)	0 (0.0)	5 (2.3)
Missing	66	29	37
Modified Ranking Score	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	2.00 (1.00, 3.00)	0.9
Missing	3	1	2
1 n (%); Median (Q1, Q3)
2 Pearson’s Chi-squared test; Fisher’s exact test; Wilcoxon rank sum test

Table 2

tbl1 <- 
    df_table_2 %>% 
    select(ee_sbp_0, ee_sbp_3, ee_sbp_12, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            ee_sbp_0 ~ "Month 0",
            ee_sbp_3 ~ "Month 3",
            ee_sbp_12 ~ "Month 12"
            )
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#---------------------

tbl2 <- 
    df_table_2 %>% 
    mutate(
        sbp_0_cat = case_when(
            ee_sbp_0 < 140 ~ "Yes", ee_sbp_0 >= 140 ~ "No"),
        sbp_3_cat = case_when(
            ee_sbp_3 < 140 ~ "Yes", ee_sbp_3 >= 140 ~ "No"), 
        sbp_12_cat = case_when(
            ee_sbp_12 < 140 ~ "Yes", ee_sbp_12 >= 140 ~ "No")) %>% 
    select(sbp_0_cat, sbp_3_cat, sbp_12_cat, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            sbp_0_cat ~ "Month 0",
            sbp_3_cat ~ "Month 3",
            sbp_12_cat ~ "Month 12"
            )
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#--------------

tbl3 <- 
    df_table_2 %>% 
    mutate(
        sbp_0_cat = case_when(
            (ee_sbp_0 < 140) & (ee_dbp_0 < 90) ~ "Yes", 
            (ee_sbp_0 >= 140) | (ee_dbp_0 >= 90) ~ "No"),
        sbp_3_cat = case_when(
            (ee_sbp_3 < 140) & (ee_dbp_3 < 90) ~ "Yes", 
            (ee_sbp_3 >= 140) | (ee_dbp_3 >= 90) ~ "No"),
        sbp_12_cat = case_when(
            (ee_sbp_12 < 140) & (ee_dbp_12 < 90) ~ "Yes", 
            (ee_sbp_12 >= 140) | (ee_dbp_12 >= 90) ~ "No")
        ) %>% 
    select(sbp_0_cat, sbp_3_cat, sbp_12_cat, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_categorical() ~ "{n} ({p})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            sbp_0_cat ~ "Month 0",
            sbp_3_cat ~ "Month 3",
            sbp_12_cat ~ "Month 12"
            )
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#---------

gtsummary::tbl_stack(
    group_header = c(
        "Systolic BP: Mean (SD)", 
        "Systolic BP <140: n (%)", 
        "Blood Pressure <140/90: n (%)"),
    tbls = list(tbl1, tbl2, tbl3),
    ) %>% 
    gtsummary::modify_caption("Table 2: Study Outcomes") 

Table 2: Study Outcomes

Characteristic	Overall N = 500 1	Intervention N = 244 1	Routine Care N = 256 1	p-value 2
Systolic BP: Mean (SD)
Month 0	158 (19)	157 (18)	158 (20)	0.7
Month 3	143 (19)	142 (20)	144 (19)	0.3
Missing	67	25	42
Month 12	141 (21)	138 (20)	143 (22)	0.003
Missing	90	44	46
Systolic BP <140: n (%)
Month 0	41 (8.2)	22 (9.0)	19 (7.4)	0.5
Month 3	209 (48)	107 (49)	102 (48)	0.8
Missing	67	25	42
Month 12	235 (57)	141 (71)	94 (45)	<0.001
Missing	90	44	46
Blood Pressure <140/90: n (%)
Month 0	31 (6.2)	17 (7.0)	14 (5.5)	0.5
Month 3	172 (40)	84 (39)	88 (41)	0.6
Missing	68	26	42
Month 12	205 (50)	119 (60)	86 (41)	<0.001
Missing	90	44	46
1 Mean (SD)
2 Wilcoxon rank sum test

Table 3

tbl2 <- 
    df_table_3_hillbone %>% 
    select(Baseline, Month12, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            Baseline ~ "Baseline",
            Month12 ~ "Month 12"
            )
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#---------------------

tbl3 <- 
    df_adverse_event %>% 
    select(gg_stroke, gg_stemi, gg_cdeath, status, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            gg_stroke ~ "Recurrent stroke",
            gg_stemi ~ "Myocardial infarction",
            gg_cdeath ~ "Vascular deaths",
            status ~ "All-cause mortality")
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#---------------------

tbl4 <- 
    df_hpt_stroke_know %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            Baseline ~ "Baseline",
            Month12 ~ "Month 12")
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#---------------
tbl5 <- 
    df_table_3_qol %>% 
    select(Baseline, Month12, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            Baseline ~ "Baseline",
            Month12 ~ "Month 12"
            )
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()


#---------------
tbl6 <- 
    df_ranking %>% 
    select(Baseline, Month12, arm) %>% 
    gtsummary::tbl_summary(
        statistic = gtsummary::all_continuous() ~ "{mean} ({sd})",
        missing_text = "Missing", 
        by = arm,
        label = list(
            Baseline ~ "Baseline",
            Month12 ~ "Month 12"
            ),
        type = list(
            Baseline ~ "continuous",
            Month12 ~ "continuous")
        ) %>% 
    gtsummary::add_overall() %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_p() %>% 
    gtsummary::bold_p()

#-----------

gtsummary::tbl_stack(
    group_header = c(
        "Medication Adherence 14-item Hill Bone Compliance", 
        "Major Adverse Cardiovascular Events over 12 months", 
        "Health Literacy in HPT/stroke",
        "Health-related quality of life",
        "Functional Status (Modified Rankin Score)"),
    tbls = list(tbl2, tbl3, tbl4, tbl5, tbl6),
    ) %>% 
    gtsummary::modify_caption("Table 3") 

Table 3

Characteristic	Overall N = 500 1	Arm 1- Intervention Arm N = 244 1	Arm 2- Routine Care N = 256 1	p-value 2
Medication Adherence 14-item Hill Bone Compliance
Baseline	50.9 (4.1)	50.8 (3.8)	50.9 (4.3)	0.3
Missing	9	3	6
Month 12	51.36 (3.30)	51.50 (2.89)	51.23 (3.66)	0.8
Missing	82	39	43
Major Adverse Cardiovascular Events over 12 months
Recurrent stroke	7 (1.4%)	6 (2.5%)	1 (0.4%)	0.063
Myocardial infarction	1 (0.2%)	1 (0.4%)	0 (0%)	0.5
Vascular deaths	4 (0.8%)	3 (1.2%)	1 (0.4%)	0.4
All-cause mortality	13 (3.4%)	6 (3.3%)	7 (3.4%)	>0.9
Missing	113	60	53
Health Literacy in HPT/stroke
Baseline	8 (3)	8 (3)	7 (3)	0.038
Missing	31	13	18
Month 12	10 (2)	10 (2)	9 (2)	0.087
Missing	94	43	51
Health-related quality of life
Baseline	8.38 (2.59)	8.36 (2.57)	8.40 (2.61)	0.9
Missing	5	0	5
Month 12	6.87 (2.22)	6.84 (2.17)	6.89 (2.27)	>0.9
Missing	79	38	41
Functional Status (Modified Rankin Score)
Baseline	2.21 (1.26)	2.20 (1.25)	2.22 (1.28)	0.9
Missing	3	1	2
Month 12	1.54 (1.34)	1.52 (1.35)	1.56 (1.33)	0.6
Missing	66	31	35
1 Mean (SD)
2 Wilcoxon rank sum test