snu-dhc 0.1.5

SNU_DHC Package

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")