Creating Baseline Characteristics Tables

Introduction

Baseline characteristics tables (Table 1) summarize patient demographics and clinical features at study entry. The clinpubr package automates the key decisions: variable type classification, normality assessment, statistical test selection, and missing data reporting.

Loading and Preparing Data

We’ll use the NCCTG Lung Cancer dataset from the survival package:

data(cancer, package = "survival")
str(cancer)
#> 'data.frame':    228 obs. of  10 variables:
#>  $ inst     : num  3 3 3 5 1 12 7 11 1 7 ...
#>  $ time     : num  306 455 1010 210 883 ...
#>  $ status   : num  2 2 1 2 2 1 2 2 2 2 ...
#>  $ age      : num  74 68 56 57 60 74 68 71 53 61 ...
#>  $ sex      : num  1 1 1 1 1 1 2 2 1 1 ...
#>  $ ph.ecog  : num  1 0 0 1 0 1 2 2 1 2 ...
#>  $ ph.karno : num  90 90 90 90 100 50 70 60 70 70 ...
#>  $ pat.karno: num  100 90 90 60 90 80 60 80 80 70 ...
#>  $ meal.cal : num  1175 1225 NA 1150 NA ...
#>  $ wt.loss  : num  NA 15 15 11 0 0 10 1 16 34 ...
knitr::kable(head(cancer), caption = "Raw Data Preview")

Raw Data Preview
inst	time	status	age	sex	ph.ecog	ph.karno	pat.karno	meal.cal	wt.loss
3	306	2	74	1	1	90	100	1175	NA
3	455	2	68	1	0	90	90	1225	15
3	1010	1	56	1	0	90	90	NA	15
5	210	2	57	1	1	90	60	1150	11
1	883	2	60	1	0	100	90	NA	0
12	1022	1	74	1	1	50	80	513	0

Create derived variables for demonstration:

cancer$age_group <- cut(cancer$age,
  breaks = c(0, 50, 60, 70, 100),
  labels = c("<50", "50-60", "60-70", ">70")
)

# Combine sparse ECOG categories
cancer$ph.ecog_cat <- factor(cancer$ph.ecog,
  levels = c(0:3),
  labels = c("0", "1", ">=2", ">=2")
)

# Add missing values for demonstration
set.seed(123)
cancer$meal.cal[sample(1:nrow(cancer), 30)] <- NA
cancer$wt.loss[sample(1:nrow(cancer), 20)] <- NA

knitr::kable(head(cancer), caption = "Data After Preparation")

Data After Preparation
inst	time	status	age	sex	ph.ecog	ph.karno	pat.karno	meal.cal	wt.loss	age_group	ph.ecog_cat
3	306	2	74	1	1	90	100	1175	NA	>70	1
3	455	2	68	1	0	90	90	1225	15	60-70	0
3	1010	1	56	1	0	90	90	NA	15	50-60	0
5	210	2	57	1	1	90	60	1150	11	50-60	1
1	883	2	60	1	0	100	90	NA	0	50-60	0
12	1022	1	74	1	1	50	80	513	0	>70	1

Automatic Variable Type Determination

Before creating a baseline table, get_var_types() classifies each variable as:

Factor variables: Categorical (factors or numeric with few unique values)
Non-normal variables: Continuous variables failing normality tests
Exact test variables: Categorical variables with small cell counts (Fisher’s exact test)
Omitted variables: Variables with too many levels

var_types <- get_var_types(cancer, strata = "sex")

var_types
#> $factor_vars
#> [1] "status"      "sex"         "ph.ecog"     "age_group"   "ph.ecog_cat"
#> 
#> $exact_vars
#> [1] "ph.ecog"
#> 
#> $nonnormal_vars
#> [1] "inst"      "time"      "ph.karno"  "pat.karno" "meal.cal"  "wt.loss"  
#> 
#> $omit_vars
#> NULL
#> 
#> $strata
#> [1] "sex"
#> 
#> attr(,"class")
#> [1] "var_types"

Customizing Classification

Adjust thresholds for automatic classification:

var_types_custom <- get_var_types(
  cancer,
  strata = "sex",
  num_to_factor = 10, # Numeric vars with <=10 unique values treated as factor
  omit_factor_above = 15, # Omit factors with >15 levels
  norm_test_by_group = TRUE # Test normality within each stratum
)

var_types_custom
#> $factor_vars
#> [1] "status"      "sex"         "ph.ecog"     "ph.karno"    "pat.karno"  
#> [6] "age_group"   "ph.ecog_cat"
#> 
#> $exact_vars
#> [1] "ph.ecog"   "ph.karno"  "pat.karno"
#> 
#> $nonnormal_vars
#> [1] "inst"     "time"     "meal.cal" "wt.loss" 
#> 
#> $omit_vars
#> NULL
#> 
#> $strata
#> [1] "sex"
#> 
#> attr(,"class")
#> [1] "var_types"

Save QQ plots for manual review of normality tests (optional):

# var_types_with_plots <- get_var_types(
#   cancer, strata = "sex",
#   save_qqplots = TRUE, folder_name = "qqplots_review"
# )

Creating Baseline Tables

Basic Baseline Table

baseline_table() automatically selects summary statistics (mean/SD vs median/IQR) and statistical tests (t-test vs Mann-Whitney vs Chi-square vs Fisher):

baseline_result <- baseline_table(
  cancer,
  var_types = var_types,
  save_table = FALSE
)

knitr::kable(baseline_result$baseline, caption = "Baseline Characteristics by Sex")

Baseline Characteristics by Sex
	Overall	sex: 1	sex: 2	p	test
n	228	138	90
inst (median [IQR])	11.00 [3.00, 16.00]	11.00 [3.00, 15.00]	11.00 [3.25, 16.00]	0.416	nonnorm
time (median [IQR])	255.50 [166.75, 396.50]	224.00 [144.75, 369.25]	292.50 [195.25, 448.50]	0.013	nonnorm
status = 2 (%)	165 (72.4)	112 (81.2)	53 (58.9)	<0.001
age (mean (SD))	62.45 (9.07)	63.34 (9.14)	61.08 (8.85)	0.064
ph.ecog (%)				0.823	exact
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
2	50 (22.0)	29 (21.2)	21 (23.3)
3	1 (0.4)	1 (0.7)	0 (0.0)
ph.karno (median [IQR])	80.00 [75.00, 90.00]	80.00 [70.00, 90.00]	80.00 [80.00, 90.00]	0.882	nonnorm
pat.karno (median [IQR])	80.00 [70.00, 90.00]	80.00 [70.00, 90.00]	80.00 [70.00, 90.00]	0.332	nonnorm
meal.cal (median [IQR])	1025.00 [630.00, 1137.50]	1025.00 [730.25, 1175.00]	925.00 [588.00, 1060.00]	0.062	nonnorm
wt.loss (median [IQR])	6.50 [0.00, 15.00]	9.00 [0.25, 20.00]	4.00 [0.00, 11.00]	0.031	nonnorm
age_group (%)				0.188
<50	26 (11.4)	14 (10.1)	12 (13.3)
50-60	68 (29.8)	35 (25.4)	33 (36.7)
60-70	88 (38.6)	58 (42.0)	30 (33.3)
>70	46 (20.2)	31 (22.5)	15 (16.7)
ph.ecog_cat (%)				0.737
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
>=2	51 (22.5)	30 (21.9)	21 (23.3)

Multi-Group Comparisons

With more than 2 groups, pairwise comparisons are automatically generated with optional multiple testing correction:

data(cancer, package = "survival")
cancer$ph.ecog_cat <- factor(cancer$ph.ecog,
  levels = c(0:3),
  labels = c("0", "1", ">=2", ">=2")
)

var_types_ecog <- get_var_types(cancer, strata = "ph.ecog_cat")

baseline_multi <- baseline_table(
  cancer,
  var_types = var_types_ecog,
  save_table = FALSE,
  multiple_comparison_test = TRUE,
  p_adjust_method = "BH"
)

knitr::kable(baseline_multi$baseline, caption = "Baseline Characteristics by ECOG Status")

Baseline Characteristics by ECOG Status
	Overall	ph.ecog_cat: 0	ph.ecog_cat: 1	ph.ecog_cat: >=2	p	test
n	228	63	113	51
inst (median [IQR])	11.00 [3.00, 16.00]	7.00 [3.00, 13.00]	11.00 [5.00, 15.00]	11.50 [3.25, 16.00]	0.254	nonnorm
time (median [IQR])	255.50 [166.75, 396.50]	303.00 [224.50, 437.50]	243.00 [177.00, 426.00]	180.00 [100.00, 301.00]	0.001	nonnorm
status = 2 (%)	165 (72.4)	37 (58.7)	82 (72.6)	45 (88.2)	0.002
age (median [IQR])	63.00 [56.00, 69.00]	61.00 [56.50, 68.00]	63.00 [55.00, 68.00]	68.00 [60.50, 73.00]	0.002	nonnorm
sex = 2 (%)	90 (39.5)	27 (42.9)	42 (37.2)	21 (41.2)	0.737
ph.ecog (%)					<0.001	exact
0	63 (27.8)	63 (100.0)	0 (0.0)	0 (0.0)
1	113 (49.8)	0 (0.0)	113 (100.0)	0 (0.0)
2	50 (22.0)	0 (0.0)	0 (0.0)	50 (98.0)
3	1 (0.4)	0 (0.0)	0 (0.0)	1 (2.0)
ph.karno (median [IQR])	80.00 [75.00, 90.00]	90.00 [90.00, 100.00]	80.00 [80.00, 90.00]	70.00 [60.00, 70.00]	<0.001	nonnorm
pat.karno (median [IQR])	80.00 [70.00, 90.00]	90.00 [80.00, 90.00]	80.00 [70.00, 90.00]	60.00 [60.00, 70.00]	<0.001	nonnorm
meal.cal (median [IQR])	975.00 [635.00, 1150.00]	1000.00 [653.75, 1175.00]	1025.00 [825.00, 1150.00]	796.50 [472.00, 1075.00]	0.037	nonnorm
wt.loss (median [IQR])	7.00 [0.00, 15.75]	4.00 [0.00, 10.00]	6.00 [0.00, 15.00]	10.50 [3.50, 22.75]	0.009	nonnorm

knitr::kable(baseline_multi$pairwise, caption = "Pairwise Comparison P-values")

Pairwise Comparison P-values
	ph.ecog_cat: 0_ph.ecog_cat: 1	ph.ecog_cat: 0_ph.ecog_cat: >=2	ph.ecog_cat: 1_ph.ecog_cat: >=2
inst	0.2225074	0.2225074	0.7774247
time	0.1457843	0.0008158	0.0101101
status	0.0868062	0.0031545	0.0650124
age	0.8454807	0.0029606	0.0029606
sex	1.0000000	1.0000000	1.0000000
ph.ecog	0.0001000	0.0001000	0.0001000
ph.karno	0.0000000	0.0000000	0.0000000
pat.karno	0.0136468	0.0000000	0.0000000
meal.cal	0.5094785	0.1265793	0.0316249
wt.loss	0.0907856	0.0062612	0.0907856

Customizing the Table

Select specific variables, add SMD, handle missing strata:

baseline_custom <- baseline_table(
  cancer,
  var_types = var_types,
  vars = c("age", "wt.loss", "meal.cal", "ph.ecog"),
  smd = TRUE,
  omit_missing_strata = TRUE,
  seed = 123
)

knitr::kable(baseline_custom$baseline, caption = "Customized Baseline Table")

Customized Baseline Table
	Overall	sex: 1	sex: 2	p	test	SMD
n	228	138	90
age (mean (SD))	62.45 (9.07)	63.34 (9.14)	61.08 (8.85)	0.064		0.252
wt.loss (median [IQR])	7.00 [0.00, 15.75]	8.00 [0.75, 18.50]	4.00 [0.00, 11.00]	0.029	nonnorm	0.264
meal.cal (median [IQR])	975.00 [635.00, 1150.00]	1025.00 [768.00, 1175.00]	925.00 [588.00, 1067.50]	0.022	nonnorm	0.357
ph.ecog (%)				0.826	exact	0.165
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
2	50 (22.0)	29 (21.2)	21 (23.3)
3	1 (0.4)	1 (0.7)	0 (0.0)

Missing Data Summary

knitr::kable(baseline_result$missing, caption = "Missing Data Summary")

Missing Data Summary
	Overall	sex: 1	sex: 2	p
n	228	138	90
inst = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000
time = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN
status = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN
age = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN
ph.ecog = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000
ph.karno = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000
pat.karno = TRUE (%)	3 (1.3)	2 (1.4)	1 (1.1)	1.000
meal.cal = TRUE (%)	69 (30.3)	36 (26.1)	33 (36.7)	0.121
wt.loss = TRUE (%)	34 (14.9)	24 (17.4)	10 (11.1)	0.267
age_group = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN
ph.ecog_cat = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000

Manual Override

Override automatic classification based on clinical knowledge or manual review: :

baseline_manual <- baseline_table(
  cancer,
  strata = "sex",
  factor_vars = c("ph.ecog", "pat.karno"),
  nonnormal_vars = c("age"),
  exact_vars = c("ph.ecog")
)

knitr::kable(baseline_manual$baseline, caption = "Baseline Table with Manual Overrides")

Baseline Table with Manual Overrides
	Overall	sex: 1	sex: 2	p	test
n	228	138	90
inst (mean (SD))	11.09 (8.30)	10.56 (7.80)	11.89 (9.00)	0.254
time (mean (SD))	305.23 (210.65)	283.23 (213.05)	338.97 (203.47)	0.049
status (mean (SD))	1.72 (0.45)	1.81 (0.39)	1.59 (0.49)	<0.001
age (median [IQR])	63.00 [56.00, 69.00]	64.00 [57.00, 70.00]	61.00 [55.00, 68.00]	0.057	nonnorm
ph.ecog (%)				0.831	exact
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
2	50 (22.0)	29 (21.2)	21 (23.3)
3	1 (0.4)	1 (0.7)	0 (0.0)
ph.karno (mean (SD))	81.94 (12.33)	81.82 (12.38)	82.11 (12.32)	0.864
pat.karno (%)				0.636
30	2 (0.9)	1 (0.7)	1 (1.1)
40	2 (0.9)	1 (0.7)	1 (1.1)
50	4 (1.8)	2 (1.5)	2 (2.2)
60	30 (13.3)	18 (13.2)	12 (13.5)
70	41 (18.2)	30 (22.1)	11 (12.4)
80	51 (22.7)	32 (23.5)	19 (21.3)
90	60 (26.7)	31 (22.8)	29 (32.6)
100	35 (15.6)	21 (15.4)	14 (15.7)
meal.cal (mean (SD))	928.78 (402.17)	980.54 (413.26)	840.70 (369.08)	0.020
wt.loss (mean (SD))	9.83 (13.14)	11.22 (12.98)	7.77 (13.18)	0.060
ph.ecog_cat (%)				0.737
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
>=2	51 (22.5)	30 (21.9)	21 (23.3)

Saving Results

Save all tables to CSV files:

# baseline_saved <- baseline_table(
#   cancer, var_types = var_types,
#   save_table = TRUE, filename = "baseline_characteristics.csv"
# )

Complete Workflow

A streamlined 5-step workflow from data preparation to final table:

# Step 1: Prepare data
data(cancer, package = "survival")
cancer_clean <- cancer %>%
  mutate(
    age_group = cut(age,
      breaks = c(0, 50, 60, 70, 100),
      labels = c("<50", "50-60", "60-70", ">70")
    ),
    ph.ecog_cat = factor(ph.ecog,
      levels = c(0:3),
      labels = c("0", "1", ">=2", ">=2")
    ),
    sex = factor(sex, labels = c("Male", "Female"))
  )

# Step 2: Determine variable types
var_types <- get_var_types(cancer_clean, strata = "sex", num_to_factor = 5)

# Step 3: Review classification
knitr::kable(data.frame(
  Variable_Type = c("Factor", "Non-normal", "Exact"),
  Variables = c(
    paste(var_types$factor_vars, collapse = ", "),
    paste(var_types$nonnormal_vars, collapse = ", "),
    paste(var_types$exact_vars, collapse = ", ")
  )
), caption = "Variable Type Review")

Variable Type Review
Variable_Type	Variables
Factor	status, sex, ph.ecog, age_group, ph.ecog_cat
Non-normal	inst, time, ph.karno, pat.karno, meal.cal, wt.loss
Exact	ph.ecog


# Step 4: Create baseline table
baseline_final <- baseline_table(
  cancer_clean,
  var_types = var_types,
  smd = TRUE
)

# Step 5: Review results
knitr::kable(baseline_final$baseline, caption = "Final Baseline Characteristics Table")

Final Baseline Characteristics Table
	Overall	sex: Male	sex: Female	p	test	SMD
n	228	138	90
inst (median [IQR])	11.00 [3.00, 16.00]	11.00 [3.00, 15.00]	11.00 [3.25, 16.00]	0.416	nonnorm	0.158
time (median [IQR])	255.50 [166.75, 396.50]	224.00 [144.75, 369.25]	292.50 [195.25, 448.50]	0.013	nonnorm	0.268
status = 2 (%)	165 (72.4)	112 (81.2)	53 (58.9)	<0.001		0.501
age (mean (SD))	62.45 (9.07)	63.34 (9.14)	61.08 (8.85)	0.064		0.252
ph.ecog (%)				0.826	exact	0.165
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
2	50 (22.0)	29 (21.2)	21 (23.3)
3	1 (0.4)	1 (0.7)	0 (0.0)
ph.karno (median [IQR])	80.00 [75.00, 90.00]	80.00 [70.00, 90.00]	80.00 [80.00, 90.00]	0.882	nonnorm	0.023
pat.karno (median [IQR])	80.00 [70.00, 90.00]	80.00 [70.00, 90.00]	80.00 [70.00, 90.00]	0.332	nonnorm	0.093
meal.cal (median [IQR])	975.00 [635.00, 1150.00]	1025.00 [768.00, 1175.00]	925.00 [588.00, 1067.50]	0.022	nonnorm	0.357
wt.loss (median [IQR])	7.00 [0.00, 15.75]	8.00 [0.75, 18.50]	4.00 [0.00, 11.00]	0.029	nonnorm	0.264
age_group (%)				0.188		0.298
<50	26 (11.4)	14 (10.1)	12 (13.3)
50-60	68 (29.8)	35 (25.4)	33 (36.7)
60-70	88 (38.6)	58 (42.0)	30 (33.3)
>70	46 (20.2)	31 (22.5)	15 (16.7)
ph.ecog_cat (%)				0.737		0.106
0	63 (27.8)	36 (26.3)	27 (30.0)
1	113 (49.8)	71 (51.8)	42 (46.7)
>=2	51 (22.5)	30 (21.9)	21 (23.3)

knitr::kable(baseline_final$missing, caption = "Final Missing Data Summary")

Final Missing Data Summary
	Overall	sex: Male	sex: Female	p	SMD
n	228	138	90
inst = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000	0.121
time = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN	<0.001
status = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN	<0.001
age = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN	<0.001
ph.ecog = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000	0.121
ph.karno = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000	0.121
pat.karno = TRUE (%)	3 (1.3)	2 (1.4)	1 (1.1)	1.000	0.030
meal.cal = TRUE (%)	47 (20.6)	24 (17.4)	23 (25.6)	0.186	0.200
wt.loss = TRUE (%)	14 (6.1)	10 (7.2)	4 (4.4)	0.562	0.120
age_group = TRUE (%)	0 (0.0)	0 (0.0)	0 (0.0)	NaN	<0.001
ph.ecog_cat = TRUE (%)	1 (0.4)	1 (0.7)	0 (0.0)	1.000	0.121

Summary

Key Functions

get_var_types(): Automatic variable type determination with customizable thresholds
baseline_table(): Create comprehensive baseline tables with automatic test selection

Best Practices

Review automatic classifications — clinical knowledge should override statistical defaults when appropriate
Include SMD for observational studies — standardized mean differences help assess group balance
Handle missing data transparently — report missing patterns in your tables
Use BH correction for multi-group pairwise comparisons