SNU_DHC Package
Project description
SNU-DHC
Description
DemoTable generates baseline characteristic tables for medical studies and exports them to Excel.
It supports continuous and categorical variables, grouping, and automatic p-value calculation.
Parameters:
df: Input pandas DataFrame.table_name: Title for the Excel sheet.variables: List of dictionaries defining the variables to display.group_variable: Optional column to group comparisons.group_labels: Display labels for group values.show_total: Whether to show the total column.show_missing: Whether to show missing counts for categorical variables.percent_decimals: Decimal places for percentages.thousands_sep: Use comma separator for each thousand numbers.show_p_values: Whether to include a p-value column.p_value_decimals: Fixed or automatic formatting for p-values.
Test selection logic (for p-values):
- Continuous (mean):
- 2 groups: Welch's t-test (with checks and warnings for normality)
- 3+ groups: ANOVA (with checks and warnings for normality and variance)
- Continuous (median):
- 2 groups: Mann-Whitney U test
- 3+ groups: Kruskal-Wallis test
- Categorical:
- Chi-square test by default
- Fisher's exact test if 2x2 and <5 cell counts
Usage Example
from snu_dhc.tables import DemoTable
variables_config = [
{"var": "age", "name": "Age", "type": "continuous", "stat": "median", "decimals": 0},
{"var": "sex", "name": "Sex", "type": "categorical", "class_labels": {1: "Male", 0: "Female"}},
{"var": "dm", "name": "Diabetes mellitus", "type": "categorical", "class_labels": {1: ""}},
{"var": "init_rhythm", "name": "Initial rhythm", "type": "categorical", "class_labels": {1: "VF/VT", 2: "PEA", 3: "Asystole"}},
{"var": "rti", "name": "RTI", "type": "continuous", "stat": "median", "decimals": 0},
]
table = DemoTable(
df=ohca_group,
table_name="Table 1. Baseline characteristics of study patients",
variables=variables_config,
group_variable="group",
group_labels={"train": "Train", "val": "Validation", "test": "Test"},
show_total=True,
show_missing=True,
percent_decimals=1,
thousands_sep=True,
show_p_values=True,
p_value_decimals="auto"
)
table.save("table1.xlsx")
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