get_lb_score

get_lb_score(lb_tk_recovery_filtered_armcd, lbtestcd_groups)

Calculate Z-scores and assign toxicity scores for multiple LBTESTCD groups.

This function processes lab data to compute Z-scores based on the deviation of values in the HD group compared to the vehicle group. It assigns toxicity scores and organizes the results into separate DataFrames for each group.

Parameters:

• lb_tk_recovery_filtered_armcd (pandas.DataFrame) – A pandas DataFrame containing lab data. Must include columns: STUDYID, LBTESTCD, LBSTRESN, and ARMCD.

• lbtestcd_groups (dict) – A dictionary where keys are group names (e.g., “ALT”, “AST”) and values are lists of corresponding LBTESTCD codes.

Returns:

A dictionary with group names as keys and their respective DataFrames as values. Each DataFrame includes the columns:

• STUDYID: The study ID.

• avg_<group_name>_zscore: The average Z-score for the group, with toxicity scores applied.

Return type:

dict

Example Usage:

import pandas as pd

# Sample lab data
data = {
    "STUDYID": ["S1", "S1", "S1", "S2", "S2"],
    "LBTESTCD": ["SERUM | ALT", "PLASMA | ALT", "WHOLE BLOOD | ALT", "SERUM | ALT", "PLASMA | ALT"],
    "LBSTRESN": [1.2, 1.5, 2.0, 1.8, 2.2],
    "ARMCD": ["vehicle", "HD", "HD", "vehicle", "HD"]
}
df = pd.DataFrame(data)

# Define LBTESTCD groups
lbtestcd_groups = {
    "ALT": ["SERUM | ALT", "PLASMA | ALT", "WHOLE BLOOD | ALT"],
    "AST": ["SERUM | AST", "PLASMA | AST", "WHOLE BLOOD | AST"]
}

# Calculate Z-scores
results = get_lb_score(df, lbtestcd_groups)

# Access results for the ALT group
alt_results = results["ALT"]
print(alt_results)

Notes:

• The function assumes the presence of a “vehicle” group in the ARMCD column for baseline calculations.

• Z-scores are calculated as the absolute deviation from the mean, scaled by the standard deviation.

• Toxicity scores are assigned as follows:

  • 3 for Z-scores >= 3

  • 2 for Z-scores >= 2

  • 1 for Z-scores >= 1

  • 0 for Z-scores < 1