AVERT Paper 1

Author

Samuel Blay Nguah

MRS figure

Code 
library(gtsummary)
library(tidyverse)
gtsummary::theme_gtsummary_compact()
df_avert <- dget("df_avert")
Code 
df_avert %>% 
  group_by(tmt_statin, tmt_aspirin) %>% 
  summarise(across(mrs_diff_0_12, epiDisplay::ci.numeric)) %>% 
  unnest(mrs_diff_0_12) %>%
  mutate(
    tmt_aspirin = factor(tmt_aspirin, levels = c(0, 75), labels = c("0mg", "75mg")), 
    tmt_statin = factor(tmt_statin, levels = c(0, 20, 80), labels = c("0mg", "20mg", "80mg"))) %>% 
  ggplot(aes(x = tmt_statin, y = mean, group = tmt_aspirin)) +
  geom_point() +
  geom_line(aes(x = tmt_statin, y = mean, color = tmt_aspirin)) +
  labs(x = "Statin Dose", y = "MRS", color = "Aspirin", 
       title = "Change in mean Modified Ranking Score from recruitemt to month 12 \n for the dose combination of Aspirin and Statin ")+
  theme_light() +
  scale_x_discrete(expand = c(0.05,0.05))+
  scale_y_continuous(expand = c(0.02,0.02))+
  theme(legend.position = "top")
`summarise()` has grouped output by 'tmt_statin'. You can override using the
`.groups` argument.

MOCA figure

Code 
library(gtsummary)
library(tidyverse)
gtsummary::theme_gtsummary_compact()

df_avert %>% 
  group_by(tmt_statin, tmt_aspirin) %>% 
  summarise(across(moca_diff_0_12, epiDisplay::ci.numeric)) %>% 
  unnest(moca_diff_0_12) %>%
  mutate(
    tmt_aspirin = factor(tmt_aspirin, levels = c(0, 75), labels = c("0mg", "75mg")), 
    tmt_statin = factor(tmt_statin, levels = c(0, 20, 80), labels = c("0mg", "20mg", "80mg"))) %>% 
  ggplot(aes(x = tmt_statin, y = mean, group = tmt_aspirin)) +
  geom_point() +
  geom_line(aes(x = tmt_statin, y = mean, color = tmt_aspirin)) +
  labs(x = "Statin Dose", y = "MOCA", color = "Aspirin", 
       title = "Change in mean MOCA from recruitemt to month 12 \n for the dose combination of Aspirin and Statin ")+
  theme_light() +
  scale_x_discrete(expand = c(0.05,0.05))+
  scale_y_continuous(expand = c(0.02,0.02))+
  theme(legend.position = "top")

Table 3

Code 
tab1 <- 
  df_avert %>% 
  select(mrs_diff_0_12, treatment) %>% 
  gtsummary::tbl_uvregression(
    y = mrs_diff_0_12, 
    method = lm, 
    pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
  modify_header(
    update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) 

tab2 <- 
  df_avert %>% 
  select(mrs_diff_0_12, treatment, mrs_m0) %>% 
  lm(mrs_diff_0_12 ~ treatment + mrs_m0, data=.) %>% 
  tbl_regression(
    include = treatment,
    pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
  modify_header(
    update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) 

gtsummary::tbl_merge(
  tbls = list(tab1, tab2), 
  tab_spanner = c("Unadjusted estimates", "Adjusted for baseline MRS"))%>% 
  modify_caption(
    caption = "**TABLE 3: Effect size estimates of atherosclerotic interventions/regimens change in Modified Rankin Score  (MRS) at Month 12 compared with baseline**")
TABLE 3: Effect size estimates of atherosclerotic interventions/regimens change in Modified Rankin Score (MRS) at Month 12 compared with baseline
Variable Unadjusted estimates Adjusted for baseline MRS
N Estimate 95% CI1 p-value Estimate 95% CI1 p-value
Treatment 60





    Statin 0mg + Aspirin 0mg
— —
— —
    Statin 0mg + Aspirin 75mg
-0.40 -1.2, 0.43 0.337 -0.40 -1.2, 0.44 0.343
    Statin 20mg + Aspirin 0mg
-0.04 -0.90, 0.81 0.917 -0.04 -0.90, 0.81 0.917
    Statin 20mg + Aspirin 75mg
-0.24 -1.0, 0.57 0.561 -0.24 -1.1, 0.58 0.564
    Statin 80mg + Aspirin 0mg
-0.20 -1.0, 0.63 0.630 -0.20 -1.0, 0.64 0.632
    Statin 80mg + Aspirin 75mg
-0.50 -1.3, 0.33 0.231 -0.50 -1.3, 0.34 0.236
1 CI = Confidence Interval

Table 4

Code 
tab1 <- 
  df_avert %>% 
  mutate(moca_diff_0_12 = moca_m12 - moca_m0) %>% 
  select(moca_diff_0_12, treatment) %>% 
  gtsummary::tbl_uvregression(
    y = moca_diff_0_12, 
    method = lm, 
    pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
  modify_header(
    update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) 

tab2 <- 
  df_avert %>% 
  select(moca_diff_0_12, treatment, moca_m0) %>% 
  lm(moca_diff_0_12 ~ treatment + moca_m0, data=.) %>% 
  tbl_regression(
    include = treatment,
    pvalue_fun = function(x) style_pvalue(x, digits = 3)) %>% 
  modify_header(
    update = list(estimate ~ "**Estimate**", label ~ "**Variable**")) 

gtsummary::tbl_merge(
  tbls = list(tab1, tab2), 
  tab_spanner = c("Unadjusted estimates", "Adjusted for baseline MOCA"))%>% 
  modify_caption(
    caption = "**TABLE 4: Effect size estimates of atherosclerotic interventions/regimens- change in MOCA at Month 12 compared with Month 3**")
TABLE 4: Effect size estimates of atherosclerotic interventions/regimens- change in MOCA at Month 12 compared with Month 3
Variable Unadjusted estimates Adjusted for baseline MOCA
N Estimate 95% CI1 p-value Estimate 95% CI1 p-value
Treatment 57





    Statin 0mg + Aspirin 0mg
— —
— —
    Statin 0mg + Aspirin 75mg
4.3 -0.48, 9.0 0.077 1.0 -2.1, 4.1 0.513
    Statin 20mg + Aspirin 0mg
1.5 -3.5, 6.6 0.545 -0.61 -3.8, 2.6 0.700
    Statin 20mg + Aspirin 75mg
-0.86 -5.5, 3.8 0.713 -2.6 -5.5, 0.36 0.084
    Statin 80mg + Aspirin 0mg
1.0 -3.9, 5.9 0.683 -1.1 -4.2, 2.0 0.483
    Statin 80mg + Aspirin 75mg
2.7 -2.1, 7.4 0.264 -0.14 -3.2, 2.9 0.925
1 CI = Confidence Interval

Table 5A

Code 
gtsummary::tbl_uvregression(
    df_avert %>% mutate(mrs_m12 = factor(mrs_m12, ordered = T)),
    include = c(treatment, mrs_m12),
    method = ordinal::clm,
    y = mrs_m12,
    exponentiate = TRUE,
    pvalue_fun = ~ gtsummary::style_pvalue(.x, digits = 3),
    add_estimate_to_reference_rows = TRUE,
    hide_n = TRUE,
    tidy_fun = function(x, ...) broom::tidy(x, ..., p.values = TRUE),
    ) %>%
    gtsummary::bold_labels() %>% 
    gtsummary::bold_p() %>% 
    gtsummary::add_n() %>% 
    modify_caption(caption = "**TABLE 5A: ORDINAL LOGISTIC REGRESSION OF CHANGES IN MODIFIED RANKIN SCORE**")
TABLE 5A: ORDINAL LOGISTIC REGRESSION OF CHANGES IN MODIFIED RANKIN SCORE
Characteristic N OR1 95% CI1 p-value
Treatment 60


    Statin 0mg + Aspirin 0mg
1.00 —
    Statin 0mg + Aspirin 75mg
0.80 0.16, 3.97 0.784
    Statin 20mg + Aspirin 0mg
1.16 0.22, 6.06 0.860
    Statin 20mg + Aspirin 75mg
1.19 0.26, 5.47 0.826
    Statin 80mg + Aspirin 0mg
0.87 0.15, 4.86 0.878
    Statin 80mg + Aspirin 75mg
0.86 0.17, 4.19 0.855
1 OR = Odds Ratio, CI = Confidence Interval

Table 5B

Code 
df_avert %>% 
  select(mrs_m12_cat, treatment) %>% 
  drop_na(mrs_m12_cat) %>% 
  tbl_summary(
    by = mrs_m12_cat
    ) %>% 
  gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
  gtsummary::bold_labels() %>% 
  modify_caption(
      caption = "**TABLE 5B: Chisquare analysis of MRS at month 12 categorised**")
TABLE 5B: Chisquare analysis of MRS at month 12 categorised
Characteristic Favourable, N = 471 Unfavourable, N = 131 p-value2
Treatment

0.912
    Statin 0mg + Aspirin 0mg 7 (15%) 3 (23%)
    Statin 0mg + Aspirin 75mg 8 (17%) 2 (15%)
    Statin 20mg + Aspirin 0mg 7 (15%) 2 (15%)
    Statin 20mg + Aspirin 75mg 9 (19%) 2 (15%)
    Statin 80mg + Aspirin 0mg 7 (15%) 3 (23%)
    Statin 80mg + Aspirin 75mg 9 (19%) 1 (7.7%)
1 n (%)
2 Fisher’s exact test

Table I

Code 
df_avert %>% 
    select(
        treatment, ageyrs, male, formal_education, income_in_usd, 
        type_of_domicile, cigarette, alcohol, anti_hpt_b4s, antiplatelet_b4s, 
        anti_coagulants_b4s, anti_diabetics_b4s, statins_b4s, anti_hpt_as, 
        anti_diabetics_as, ccb_as, arb_acei_as, diuretic_as, betablocker_as, 
        vaso_hydralazine_as, mra_as, methyldopa_as, bmi_m0, whr_m0, sbp_m0, 
        dbp_m0, no_of_antihyp, whr_m0, rbs_m0, moca_m0, mrs_m0, nihss_m0) %>% 
    gtsummary::tbl_summary(
        type = list(mrs_m0 = "continuous", no_of_antihyp = "continuous"),
        by = treatment,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1),
            gtsummary::all_continuous() ~ c(0,1),
            no_of_antihyp ~ c(0,0)),
        statistic = list(
            gtsummary::all_categorical() ~ "{n} ({p})",
            gtsummary::all_continuous() ~ "{mean} ({sd})",
            no_of_antihyp ~ "{median} ({min},{max})"),
        missing_text = "Missing"
        ) %>% 
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
    gtsummary::bold_labels() %>% 
    gtsummary::add_stat_label(location = "column") %>% 
    modify_caption(
      caption = "**Table 1: Comparison of baseline characteristics By Treatment Allocation**")
Table 1: Comparison of baseline characteristics By Treatment Allocation
Characteristic Statistic Statin 0mg + Aspirin 0mg, N = 10 Statin 0mg + Aspirin 75mg, N = 10 Statin 20mg + Aspirin 0mg, N = 10 Statin 20mg + Aspirin 75mg, N = 11 Statin 80mg + Aspirin 0mg, N = 10 Statin 80mg + Aspirin 75mg, N = 11 p-value1
Age (years) Mean (SD) 50 (15.1) 50 (7.2) 50 (12.9) 56 (13.0) 60 (7.5) 52 (10.7) 0.242
Sex, male (%) n (%) 3 (30.0) 4 (40.0) 5 (50.0) 5 (45.5) 5 (50.0) 8 (72.7) 0.538
Educational level






0.642
    0 n (%) 2 (20.0) 1 (11.1) 1 (10.0) 0 (0.0) 2 (20.0) 1 (9.1)
    1 n (%) 4 (40.0) 3 (33.3) 7 (70.0) 6 (54.5) 6 (60.0) 7 (63.6)
    2 n (%) 4 (40.0) 3 (33.3) 2 (20.0) 5 (45.5) 2 (20.0) 2 (18.2)
    3 n (%) 0 (0.0) 2 (22.2) 0 (0.0) 0 (0.0) 0 (0.0) 1 (9.1)
    Missing n 0 1 0 0 0 0
Income (USD)






0.317
    0 n (%) 1 (11.1) 1 (10.0) 2 (28.6) 0 (0.0) 1 (10.0) 2 (20.0)
    1 n (%) 4 (44.4) 4 (40.0) 3 (42.9) 9 (90.0) 6 (60.0) 7 (70.0)
    2 n (%) 4 (44.4) 5 (50.0) 2 (28.6) 1 (10.0) 3 (30.0) 1 (10.0)
    Missing n 1 0 3 1 0 1
Type of domicile






0.774
    1 n (%) 1 (10.0) 1 (10.0) 2 (20.0) 2 (18.2) 1 (10.0) 0 (0.0)
    2 n (%) 9 (90.0) 9 (90.0) 8 (80.0) 9 (81.8) 9 (90.0) 11 (100.0)
Cigarette smoking n (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 3 (30.0) 2 (18.2) 0.030
Alcohol use n (%) 3 (30.0) 2 (20.0) 1 (10.0) 1 (9.1) 4 (40.0) 3 (27.3) 0.550
Antihypertensive before ICH n (%) 4 (40.0) 7 (70.0) 5 (50.0) 7 (63.6) 5 (50.0) 7 (63.6) 0.777
Anti-platelet before ICH n (%) 0 (0.0) 1 (10.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0.645
Anti-coagulant before ICH n (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) >0.999
Anti-glycemics before ICH n (%) 1 (10.0) 0 (0.0) 0 (0.0) 0 (0.0) 1 (10.0) 0 (0.0) 0.471
Statins before ICH n (%) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) >0.999
Antihypertensive after ICH n (%) 10 (100.0) 10 (100.0) 8 (80.0) 11 (100.0) 9 (90.0) 11 (100.0) 0.164
Anti-diabetics after ICH n (%) 1 (10.0) 1 (10.0) 0 (0.0) 3 (27.3) 2 (20.0) 2 (18.2) 0.688
CCB n (%) 8 (80.0) 8 (80.0) 7 (70.0) 7 (63.6) 8 (80.0) 9 (81.8) 0.938
ARB/ACE-I n (%) 10 (100.0) 7 (70.0) 7 (70.0) 7 (63.6) 7 (70.0) 11 (100.0) 0.073
DIURETICS n (%) 6 (60.0) 8 (80.0) 5 (50.0) 1 (9.1) 4 (40.0) 4 (36.4) 0.029
BETABLOCKER n (%) 5 (50.0) 5 (50.0) 3 (30.0) 3 (27.3) 4 (40.0) 5 (45.5) 0.866
VASODILATOR n (%) 4 (40.0) 1 (10.0) 3 (30.0) 3 (27.3) 1 (10.0) 4 (36.4) 0.495
MRA n (%) 1 (10.0) 1 (10.0) 0 (0.0) 2 (18.2) 1 (10.0) 0 (0.0) 0.779
CENTRALLY ACTING n (%) 1 (10.0) 1 (10.0) 1 (10.0) 2 (18.2) 1 (10.0) 1 (9.1) >0.999
BMI (Month 0) Mean (SD) 26 (5.8) 25 (3.4) 24 (4.3) 24 (5.7) 25 (7.5) 31 (24.9) 0.899
WAIST-TO-HIP RATIO (Month 0) Mean (SD) 1 (0.1) 1 (0.1) 1 (0.1) 1 (0.1) 1 (0.1) 1 (0.1) 0.886
Baseline systolic BP (Month 0) Mean (SD) 151 (16.0) 154 (22.7) 154 (32.6) 157 (22.0) 165 (32.2) 163 (18.5) 0.727
Baseline diastolic BP (Month 0) Mean (SD) 98 (12.2) 97 (15.5) 98 (19.0) 95 (18.4) 103 (27.9) 96 (11.6) 0.994
Antihypertensive meds) Median (Minimum,Maximum) 4 (2,5) 3 (2,5) 3 (0,5) 2 (1,4) 3 (0,5) 3 (2,4) 0.278
Random blood glucose (Month 0) Mean (SD) 6 (2.2) 7 (2.7) 6 (1.1) 7 (2.4) 6 (1.6) 7 (1.7) 0.842
    Missing n 1 0 0 0 0 0
MOCA (Month 0) Mean (SD) 18 (9.3) 15 (6.3) 15 (7.5) 17 (6.0) 16 (6.8) 16 (5.2) 0.886
Modified Rankin Score (Month 0) Mean (SD) 2 (1.4) 2 (1.1) 2 (1.2) 2 (0.9) 3 (1.2) 2 (0.9) 0.997
NIHSS (Month 0) Mean (SD) 2 (3.0) 3 (5.2) 4 (9.4) 2 (3.3) 3 (3.1) 2 (3.4) 0.893
1 Kruskal-Wallis rank sum test; Fisher’s exact test

Table IIA

Code 
df_avert %>% 
    select(
        treatment, died, recurrent_stroke, major_adverse_events, 
        m12_follow_up_done, morisky_m12, tsq_m12, mrs_diff_0_12, moca_diff_3_12, 
        sbp_diff_0_12, hamd_diff_0_12) %>% 
    gtsummary::tbl_summary(
        type = list(c(mrs_diff_0_12, tsq_m12, morisky_m12) ~ "continuous"),
        by = treatment,
        digits = list(
            gtsummary::all_categorical() ~ c(0,1),
            gtsummary::all_continuous() ~ c(0,1)),
        statistic = list(
            gtsummary::all_categorical() ~ "{n}",
            gtsummary::all_continuous() ~ "{mean} ({sd})")
        ) %>% 
    gtsummary::add_p(
        pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)) %>% 
    gtsummary::bold_labels() %>% 
    modify_caption(
      caption = "**Table 2: Comparison of Study Outcomes By Treatment Allocation**")
Table 2: Comparison of Study Outcomes By Treatment Allocation
Characteristic Statin 0mg + Aspirin 0mg, N = 101 Statin 0mg + Aspirin 75mg, N = 101 Statin 20mg + Aspirin 0mg, N = 101 Statin 20mg + Aspirin 75mg, N = 111 Statin 80mg + Aspirin 0mg, N = 101 Statin 80mg + Aspirin 75mg, N = 111 p-value2
All-cause deaths 1 0 1 0 1 0 0.523
Recurrent strokes 0 0 0 1 0 1 >0.999
SAE 1 0 1 1 1 1 >0.999
Completion of 12 months follow-up 9 10 8 11 9 10 0.610
Adherence scores at month 12 0 (0.7) 0 (0.7) 0 (0.4) 0 (0.8) 0 (1.0) 0 (0.3) 0.956
    Unknown 1 0 2 0 1 1
Treatment satisfaction score at month 12 53 (0.0) 53 (2.0) 53 (1.1) 53 (0.0) 53 (1.0) 53 (0.0) 0.777
    Unknown 1 0 2 0 1 1
Modified Rankin Score change(0 to 12 months) 0 (1.1) -1 (0.8) 0 (0.9) -1 (0.7) -1 (1.2) -1 (0.9) 0.877
    Unknown 0 0 1 0 0 1
MOCA (3 to 12 months) 3 (2.7) 4 (3.6) 3 (2.8) 4 (4.9) 4 (4.1) 4 (2.3) 0.970
    Unknown 1 2 4 0 1 3
Sytstolic Blood Pressure (3 to 12 months) -12 (18.3) -13 (23.3) -20 (30.4) -18 (19.7) -12 (16.1) -9 (16.5) 0.894
    Unknown 1 0 2 0 1 1
Hamd change (0 to 12 months) -5 (2.6) -3 (2.6) -2 (1.8) -3 (2.7) -4 (2.5) -4 (2.9) 0.322
    Unknown 1 0 2 0 1 1
1 n; Mean (SD)
2 Fisher’s exact test; Kruskal-Wallis rank sum test

Table IIB

Code 
tbl_mrs <- 
    df_avert %>% 
    select(treatment, mrs_m0, mrs_m12) %>% 
    pivot_longer(
        cols = c(mrs_m0, mrs_m12), names_to = "mth", values_to = "mrs") %>% 
    mutate(mth = str_extract(mth, "m\\d+$")) %>% 
    tbl_strata(
    strata = treatment,
    .tbl_fun =
      ~ .x |>
        tbl_summary(
            by = mth, 
            missing = "no",
            type = mrs ~ "continuous",
            statistic = mrs ~ "{mean}({sd})",
            digits = mrs ~ c(1,1)) |>
        add_p(
            mrs ~ "t.test", 
            pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)),
    bold_p(),
    .header = "**{strata}**, N = {n}"
    )

tbl_moca <- 
    df_avert %>% 
    select(treatment, moca_m0, moca_m12) %>% 
    pivot_longer(
        cols = c(moca_m0, moca_m12), names_to = "mth", values_to = "moca") %>% 
    mutate(mth = str_extract(mth, "m\\d+$")) %>%
    tbl_strata(
    strata = treatment,
    .tbl_fun =
      ~ .x |>
        tbl_summary(
            by = mth, 
            missing = "no",
            type = moca ~ "continuous",
            statistic = moca ~ "{mean}({sd})",
            digits = moca ~ c(1,1)) |>
        add_p(
            moca ~ "t.test", 
            pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)),
    bold_p(),
    .header = "**{strata}**, N = {n}"
    )

tbl_sbp <- 
    df_avert %>% 
    select(treatment, sbp_m0, sbp_m12) %>% 
    pivot_longer(
        cols = c(sbp_m0, sbp_m12), names_to = "mth", values_to = "sbp") %>% 
    mutate(mth = str_extract(mth, "m\\d+$")) %>%
    tbl_strata(
    strata = treatment,
    .tbl_fun =
      ~ .x |>
        tbl_summary(
            by = mth, 
            missing = "no",
            type = sbp ~ "continuous",
            statistic = sbp ~ "{mean}({sd})",
            digits = sbp ~ c(1,1)) |>
        add_p(
            sbp ~ "t.test", 
            pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)),
    bold_p(),
    .header = "**{strata}**, N = {n}"
    )

tbl_dbp <- 
    df_avert %>% 
    select(treatment, dbp_m0, dbp_m12) %>% 
    pivot_longer(
        cols = c(dbp_m0, dbp_m12), names_to = "mth", values_to = "dbp") %>% 
    mutate(mth = str_extract(mth, "m\\d+$")) %>%
    tbl_strata(
    strata = treatment,
    .tbl_fun =
      ~ .x |>
        tbl_summary(
            by = mth, 
            missing = "no",
            type = dbp ~ "continuous",
            statistic = dbp ~ "{mean}({sd})",
            digits = dbp ~ c(1,1)) |>
        add_p(
            dbp ~ "t.test", 
            pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)),
    bold_p(),
    .header = "**{strata}**, N = {n}"
    )

tbl_hamd <- 
    df_avert %>% 
    select(treatment, hamd_m0, hamd_m12) %>% 
    pivot_longer(
        cols = c(hamd_m0, hamd_m12), names_to = "mth", values_to = "hamd") %>% 
    mutate(
      mth = str_extract(mth, "m\\d+$")) %>%
    tbl_strata(
    strata = treatment,
    .tbl_fun =
      ~ .x |>
        tbl_summary(
            by = mth, 
            missing = "no",
            type = hamd ~ "continuous",
            statistic = hamd ~ "{mean}({sd})",
            digits = hamd ~ c(1,1)) |>
        add_p(
            hamd ~ "t.test", 
            pvalue_fun = function(x) gtsummary::style_pvalue(x, digits = 3)),
    bold_p(),
    .header = "**{strata}**, N = {n}"
    )

tbl_stack(tbls = list(tbl_mrs, tbl_moca, tbl_sbp, tbl_dbp, tbl_hamd))
Characteristic Statin 0mg + Aspirin 0mg, N = 20 Statin 0mg + Aspirin 75mg, N = 20 Statin 20mg + Aspirin 0mg, N = 20 Statin 20mg + Aspirin 75mg, N = 22 Statin 80mg + Aspirin 0mg, N = 20 Statin 80mg + Aspirin 75mg, N = 22
m0, N = 101 m12, N = 101 p-value2 m0, N = 101 m12, N = 101 p-value2 m0, N = 101 m12, N = 101 p-value2 m0, N = 111 m12, N = 111 p-value2 m0, N = 101 m12, N = 101 p-value2 m0, N = 111 m12, N = 111 p-value2
mrs 2.4(1.4) 2.0(1.8) 0.593 2.3(1.1) 1.5(1.1) 0.112 2.4(1.2) 2.0(1.7) 0.569 2.5(0.9) 1.8(1.0) 0.135 2.5(1.2) 1.9(2.0) 0.422 2.4(0.9) 1.5(1.0) 0.051
moca 17.6(9.3) 25.8(3.2) 0.023 14.5(6.3) 25.0(2.7) <0.001 15.0(7.5) 24.0(2.7) 0.004 16.9(6.0) 22.3(5.9) 0.048 15.9(6.8) 23.6(3.5) 0.007 16.0(5.2) 24.1(3.7) <0.001
sbp 151.3(16.0) 137.0(14.9) 0.060 154.1(22.7) 140.9(16.8) 0.158 154.2(32.6) 138.9(14.2) 0.205 156.5(22.0) 138.6(8.7) 0.026 165.1(32.2) 143.3(13.8) 0.074 162.7(18.5) 156.9(22.6) 0.528
dbp 98.2(12.2) 85.6(10.7) 0.028 97.1(15.5) 87.1(8.8) 0.097 97.8(19.0) 85.0(9.9) 0.087 94.8(18.4) 84.1(8.8) 0.102 102.9(27.9) 86.2(12.8) 0.113 96.3(11.6) 98.7(16.7) 0.706
hamd 6.5(4.2) 0.9(1.4) 0.002 4.6(3.0) 1.4(1.2) 0.009 5.3(3.8) 2.0(1.4) 0.026 4.9(2.2) 2.3(2.5) 0.016 5.3(3.1) 1.4(1.6) 0.004 5.7(3.0) 1.8(1.5) 0.001
1 Mean(SD)
2 Welch Two Sample t-test