1 gtsummary

https://www.danieldsjoberg.com/gtsummary/index.html

if(!require(gtsummary)) install.packages("gtsmmary")

library(gtsummary)
head(trial)
#> # A tibble: 6 × 8
#>   trt      age marker stage grade response death ttdeath
#>   <chr>  <dbl>  <dbl> <fct> <fct>    <int> <int>   <dbl>
#> 1 Drug A    23  0.16  T1    II           0     0    24  
#> 2 Drug B     9  1.11  T2    I            1     0    24  
#> 3 Drug A    31  0.277 T1    II           0     0    24  
#> 4 Drug A    NA  2.07  T3    III          1     1    17.6
#> 5 Drug A    51  2.77  T4    III          1     1    16.4
#> 6 Drug B    39  0.613 T4    I            0     1    15.6
data("trial")

数据框中的每个变量都可以分配一个 "label" 属性

attr(trial$trt,"label") <- '治疗'
attr(trial$trt,"label")
#> [1] "治疗"

• Label attributes from the data set are automatically printed.

• Missing values are listed as “Unknown” in the table.

argument	描述
label=	指定表格中打印的变量标签
type=	指定变量类型（例如continuous, categorical等
statistic=	更改显示的汇总统计数据
digits=	汇总统计数据将四舍五入的位数
missing=	是否显示包含缺失观测值数的行
missing_text=	缺失数字行的文本标签
sort=	按频率更改分类级别的排序
percent=	打印列、行或单元格百分比
include=	要包含在汇总表中的变量列表

1.1 基线特征表

1.1.1 描述分布

trial2 <- trial %>% select(-response,-death)
trial2 %>%
  tbl_summary(
    by = trt,
    statistic = list(
      all_continuous() ~ c("{mean}± {sd} "),  # {median} ({p25},{p75})
      all_categorical() ~ "{n} / {N} ({p}%)"
    ),
   # digits = list(starts_with("age") ~ 3,
    #              grade ~ 2),
    label = list(age ~ "Patient age",
                 grade ~ "Tumor Grade"),
    missing_text = "(NA)",
  )

Characteristic	Drug A N = 98 1	Drug B N = 102 1
Patient age	47± 15	47± 14
(NA)	7	4
Marker Level (ng/mL)	1.02± 0.89	0.82± 0.83
(NA)	6	4
T Stage
T1	28 / 98 (29%)	25 / 102 (25%)
T2	25 / 98 (26%)	29 / 102 (28%)
T3	22 / 98 (22%)	21 / 102 (21%)
T4	23 / 98 (23%)	27 / 102 (26%)
Tumor Grade
I	35 / 98 (36%)	33 / 102 (32%)
II	32 / 98 (33%)	36 / 102 (35%)
III	31 / 98 (32%)	33 / 102 (32%)
Months to Death/Censor	20.2± 5.0	19.0± 5.5
1 Mean± SD ; n / N (%)

trial2 %>%
    tbl_summary(by = trt) %>%
    add_overall() |>   # t添加总和
    add_n() |>
    # 修改注释
    modify_header(label ~ "**变量**") %>%
    modify_spanning_header(c("stat_1", "stat_2") ~ "**药物**") %>%
    modify_footnote(all_stat_cols() ~ "Median (25%, 75%) or Frequency (%)") %>%
    modify_caption("**Table 1. Patient Characteristics**") %>%
    bold_labels()

Table 1. Patient Characteristics

变量	N	Overall N = 200 1	药物
			Drug A N = 98 1	Drug B N = 102 1
Age	189	47 (38, 57)	46 (37, 60)	48 (39, 56)
Unknown		11	7	4
Marker Level (ng/mL)	190	0.64 (0.22, 1.41)	0.84 (0.23, 1.60)	0.52 (0.18, 1.21)
Unknown		10	6	4
T Stage	200
T1		53 (27%)	28 (29%)	25 (25%)
T2		54 (27%)	25 (26%)	29 (28%)
T3		43 (22%)	22 (22%)	21 (21%)
T4		50 (25%)	23 (23%)	27 (26%)
Grade	200
I		68 (34%)	35 (36%)	33 (32%)
II		68 (34%)	32 (33%)	36 (35%)
III		64 (32%)	31 (32%)	33 (32%)
Months to Death/Censor	200	22.4 (15.9, 24.0)	23.5 (17.4, 24.0)	21.2 (14.5, 24.0)
1 Median (25%, 75%) or Frequency (%)

函数	描述
add_p()	将 p 值添加到输出中，比较各组的值
add_overall()	添加包含总体汇总统计数据的列
add_n()	为每个变量添加一个包含 N（或缺少 N）的列
add_difference()	为两组之间的差值、置信区间和 p 值添加列
add_stat_label()	为每行中显示的汇总统计数据添加标签
add_stat()	泛型函数，用于添加具有用户定义值的列
add_q()	添加一列 Q 值以控制多个比较
modify_header()	更新列标题
modify_footnote()	更新列脚注
modify_spanning_header()	更新跨标头
modify_caption()	更新表格标题/标题
bold_labels()	粗体变量标签
bold_levels()	粗体可变级别
italicize_labels()	将变量标签斜体化
italicize_levels()	斜体变量级别
bold_p()	粗体显著性 p 值

1.1.2 统计检验

1.1.2.1 p值

trial2 |> 
    tbl_summary(by = trt) |> 
    add_p(
        
        test=list(all_continuous() ~ "t.test", 
                  all_categorical() ~ "chisq.test.no.correct"),
         pvalue_fun = label_style_pvalue(digits = 3),
    ) 

Characteristic	Drug A N = 98 1	Drug B N = 102 1	p-value 2
Age	46 (37, 60)	48 (39, 56)	0.834
Unknown	7	4
Marker Level (ng/mL)	0.84 (0.23, 1.60)	0.52 (0.18, 1.21)	0.116
Unknown	6	4
T Stage			0.866
T1	28 (29%)	25 (25%)
T2	25 (26%)	29 (28%)
T3	22 (22%)	21 (21%)
T4	23 (23%)	27 (26%)
Grade			0.871
I	35 (36%)	33 (32%)
II	32 (33%)	36 (35%)
III	31 (32%)	33 (32%)
Months to Death/Censor	23.5 (17.4, 24.0)	21.2 (14.5, 24.0)	0.108
1 Median (Q1, Q3); n (%)
2 Welch Two Sample t-test; Pearson’s Chi-squared test

# ?tests

wilcox.test(age~trt,data = trial2)
#> 
#>  Wilcoxon rank sum test with continuity correction
#> 
#> data:  age by trt
#> W = 4323, p-value = 0.7183
#> alternative hypothesis: true location shift is not equal to 0
table(trial2$grade,trial2$trt)|> chisq.test()
#> 
#>  Pearson's Chi-squared test
#> 
#> data:  table(trial2$grade, trial2$trt)
#> X-squared = 0.27673, df = 2, p-value = 0.8708

1.1.2.2 统计量

my_ttest3 <- function(data, variable, by, ...) {
  t.test(data[[variable]] ~ as.factor(data[[by]])) %>%
    broom::tidy() %>%
        mutate(
            statistic = sprintf("%.3f", statistic),
            p.value = case_when(
            p.value >=0.05 ~ sprintf("%.3f (ns)",p.value),
            p.value >=0.01 ~ sprintf("%.3f (*)",p.value),
            p.value >=0.001 ~sprintf("%.3f (**)",p.value),
            p.value <0.001 ~ sprintf("%.3f (***)",p.value),
            )
        ) %>% 
    select(statistic, p.value)
}

trial |>
  tbl_summary(
    by = trt,
    include = c(trt, age, marker),
    missing = "ifany"
  ) |>
  add_stat(fns = everything() ~ my_ttest3) %>% 
  modify_header(statistic = "**t-statistic**", p.value = "**p-value**") 

Characteristic	Drug A N = 98 1	Drug B N = 102 1	t-statistic	p-value
Age	46 (37, 60)	48 (39, 56)	-0.209	0.834 (ns)
Unknown	7	4
Marker Level (ng/mL)	0.84 (0.23, 1.60)	0.52 (0.18, 1.21)	1.578	0.116 (ns)
Unknown	6	4
1 Median (Q1, Q3)

1.1.2.3 置信区间

mean_ci <- function(data, variable, ...) {
  test <- t.test(data[[variable]])
  dplyr::tibble(
    mean = test$estimate,
    conf.low = test$conf.int[1],
    conf.high = test$conf.int[2]
  )
}

trial |>
  tbl_custom_summary(
    include = c("marker", "ttdeath"),
    by = "trt",
    stat_fns = ~ mean_ci,
    statistic = ~ "{mean} [{conf.low}; {conf.high}]"
  ) |>
  add_overall(last = TRUE) |>
  modify_footnote(
    all_stat_cols() ~ "mean [95% CI]"
  )

Characteristic	Drug A N = 98 1	Drug B N = 102 1	Overall N = 200 1
Marker Level (ng/mL)	1.02 [0.83; 1.20]	0.82 [0.65; 0.99]	0.92 [0.79; 1.04]
Unknown	6	4	10
Months to Death/Censor	20.2 [19.2; 21.2]	19.0 [18.0; 20.1]	19.6 [18.9; 20.4]
1 mean [95% CI]

1.2 主题

theme_gtsummary_journal(journal = "jama")
#> Setting theme "JAMA"

trial %>%
  select(trt, age, grade) %>%
  tbl_summary(by = trt) %>%
  add_p()

Characteristic	Drug A N = 98	Drug B N = 102	p-value 1
Age, Median (IQR)	46 (37 – 60)	48 (39 – 56)	0.72
Unknown	7	4
Grade, n (%)			0.87
I	35 (36)	33 (32)
II	32 (33)	36 (35)
III	31 (32)	33 (32)
1 Wilcoxon rank sum test; Pearson’s Chi-squared test

#  清除所有先前设置的主题
reset_gtsummary_theme()

1.3 模型结果

# load dataset
data(Titanic)
df = as.data.frame(Titanic)

# load library
library(gtsummary)

# create the model
model = glm(Survived ~ Age + Class + Sex + Freq, family=binomial, data=df)

# generate table 
model %>%
  tbl_regression() %>% # regression summary function
  add_global_p() %>% # add p-values
  bold_labels() %>% # make label in bold
  italicize_levels() # make categories in label in italic

Characteristic	log(OR) 1	95% CI 1	p-value
Age			0.5
Child	—	—
Adult	0.62	-1.0, 2.4
Class			>0.9
1st	—	—
2nd	-0.03	-2.0, 2.0
3rd	0.25	-1.8, 2.4
Crew	0.27	-1.8, 2.4
Sex			0.6
Male	—	—
Female	-0.37	-1.9, 1.1
Freq	-0.01	-0.02, 0.00	0.2
1 OR = Odds Ratio, CI = Confidence Interval

# generate table 
model %>%
  tbl_regression(intercept=TRUE, conf.level=0.9) %>%
  add_glance_source_note() %>%
  add_global_p() %>%
  add_q() 

Characteristic	log(OR) 1	90% CI 1	p-value	q-value 2
(Intercept)	0.10	-1.4, 1.6	>0.9	>0.9
Age			0.5	>0.9
Child	—	—
Adult	0.62	-0.78, 2.1
Class			>0.9	>0.9
1st	—	—
2nd	-0.03	-1.7, 1.7
3rd	0.25	-1.5, 2.0
Crew	0.27	-1.5, 2.0
Sex			0.6	>0.9
Male	—	—
Female	-0.37	-1.7, 0.89
Freq	-0.01	-0.02, 0.00	0.2	0.9
Null deviance = 44.4; Null df = 31; Log-likelihood = -21.3; AIC = 56.5; BIC = 66.8; Deviance = 42.5; Residual df = 25; No. Obs. = 32
1 OR = Odds Ratio, CI = Confidence Interval
2 False discovery rate correction for multiple testing

1.4 比较模型结果

data(trial)
library(survival)

model_reglog = glm(response ~ trt + grade, data=trial, family = binomial) %>% tbl_regression()
model_cox = coxph(Surv(ttdeath, death) ~ trt + grade, data=trial) %>% tbl_regression()

tbl_merge(
  list(model_reglog, model_cox),
  tab_spanner = c("**Tumor Response**", "**Time to Death**")
)

Characteristic	Tumor Response			Time to Death
	log(OR) 1	95% CI 1	p-value	log(HR) 1	95% CI 1	p-value
Chemotherapy Treatment
Drug A	—	—		—	—
Drug B	0.19	-0.41, 0.81	0.5	0.22	-0.15, 0.59	0.2
Grade
I	—	—		—	—
II	-0.06	-0.82, 0.68	0.9	0.25	-0.22, 0.72	0.3
III	0.08	-0.66, 0.82	0.8	0.52	0.07, 0.98	0.024
1 OR = Odds Ratio, CI = Confidence Interval, HR = Hazard Ratio

gt

library(gt)
tab <- mtcars |>
  rownames_to_column() |>
  arrange(factor(cyl), mpg) |>
  group_by(cyl) |>
  slice(1:3) |>
  gt()
tab

	mpg	disp	hp	drat	wt	qsec	vs	am	gear	carb
4
Volvo 142E	21.4	121.0	109	4.11	2.780	18.60	1	1	4	2
Toyota Corona	21.5	120.1	97	3.70	2.465	20.01	1	0	3	1
Datsun 710	22.8	108.0	93	3.85	2.320	18.61	1	1	4	1
6
Merc 280C	17.8	167.6	123	3.92	3.440	18.90	1	0	4	4
Valiant	18.1	225.0	105	2.76	3.460	20.22	1	0	3	1
Merc 280	19.2	167.6	123	3.92	3.440	18.30	1	0	4	4
8
Cadillac Fleetwood	10.4	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	460.0	215	3.00	5.424	17.82	0	0	3	4
Camaro Z28	13.3	350.0	245	3.73	3.840	15.41	0	0	3	4

tab <- tab |>
  tab_spanner(
    label = "Performance",
    columns = c(mpg, disp, hp, drat, wt, qsec)
  )

tab <- tab |>
  tab_spanner(
    label = "Specs",
    columns = c(vs, am, gear, carb)
  )
tab 

	Performance						Specs
	mpg	disp	hp	drat	wt	qsec	vs	am	gear	carb
4
Volvo 142E	21.4	121.0	109	4.11	2.780	18.60	1	1	4	2
Toyota Corona	21.5	120.1	97	3.70	2.465	20.01	1	0	3	1
Datsun 710	22.8	108.0	93	3.85	2.320	18.61	1	1	4	1
6
Merc 280C	17.8	167.6	123	3.92	3.440	18.90	1	0	4	4
Valiant	18.1	225.0	105	2.76	3.460	20.22	1	0	3	1
Merc 280	19.2	167.6	123	3.92	3.440	18.30	1	0	4	4
8
Cadillac Fleetwood	10.4	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	460.0	215	3.00	5.424	17.82	0	0	3	4
Camaro Z28	13.3	350.0	245	3.73	3.840	15.41	0	0	3	4

tab <- tab |>
  tab_header(
    title = md("The Cars of **mtcars**"),
    subtitle = "These are some fine automobiles"
  )
tab

The Cars of mtcars
These are some fine automobiles
	Performance						Specs
	mpg	disp	hp	drat	wt	qsec	vs	am	gear	carb
4
Volvo 142E	21.4	121.0	109	4.11	2.780	18.60	1	1	4	2
Toyota Corona	21.5	120.1	97	3.70	2.465	20.01	1	0	3	1
Datsun 710	22.8	108.0	93	3.85	2.320	18.61	1	1	4	1
6
Merc 280C	17.8	167.6	123	3.92	3.440	18.90	1	0	4	4
Valiant	18.1	225.0	105	2.76	3.460	20.22	1	0	3	1
Merc 280	19.2	167.6	123	3.92	3.440	18.30	1	0	4	4
8
Cadillac Fleetwood	10.4	472.0	205	2.93	5.250	17.98	0	0	3	4
Lincoln Continental	10.4	460.0	215	3.00	5.424	17.82	0	0	3	4
Camaro Z28	13.3	350.0	245	3.73	3.840	15.41	0	0	3	4