## README.md
The objective of this package is to offer a Python-based solution for computing the Proportion of Days Covered (PDC), a widely used metric in the healthcare industry to evaluate medication adherence. As the healthcare analytics sector shifts away from SAS, there is a growing need to recreate key metrics in alternative platforms. This package aims to simplify the process and reduce the workload for business analysts in the healthcare ecosystem by providing a readily available PDC calculation tool, thereby eliminating the need to build it from scratch.
Please use as described below:
**INPUT PARAMETERS:**
**dataframe** - *A pandas dataframe containing the required columns described below.*
**patient_id_col** - *A unique patient identifier. Format = STRING or INTEGER*
**drugname_col** - *The name of the drug being filled or drug class or Generic name, per usual PDC requirements. Format = STRING*
**filldate_col** - *The date of the fill being dispensed. Format = DATE*
**supply_days_col** - *Days of supply being dispensed at fill. Format = INTEGER*
**msr_start_dt_col** - *start date of measurement period for the patient or a reference START DATE. Format = DATE*
**msr_end_dt_col** - *end date of measurement period for the patient or a reference END DATE. Format = DATE*
**overlap_adjustment** - *Set to True to accommodate early refills, otherwise set to False*
**OUTPUT DATAFRAME** - *A Pandas dataframe containing the following columns*
**patient_id_col** - *This will return a column name representing a unique patient identifier as provided in original input dataframe. FORMAT = STRING*
**drugname_col** - *The name of the drug being filled or drug class or Generic name, as provided in original input dataframe.*
**dayscovered**- *The number of unique days of drug coverage. FORMAT = INTEGER*
**totaldays** - *The total number of days in patient analysis window. Set to 0 if days of coverage is 0. FORMAT = INTEGER*
**pdc_score** - *The patient's PDC score, calculated as dayscovered / totaldays. Set to 0 if days of coverage is 0. FORMAT = FLOAT*
## USAGE EXAMPLE
```python
# Import required libraries
import pandas as pd
import numpy as np
from datetime import datetime
from pdcscore import pdcCalc
# Create a sample dataframe
df = pd.DataFrame({
'patient_id': ['A001', 'A001', 'A001', 'B001', 'B001', 'B001', 'C001', 'C001', 'C001','C001', 'C001', 'C001'],
'drugname': ['DRUG_X', 'DRUG_X', 'DRUG_X', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y',
'DRUG_Z', 'DRUG_Z', 'DRUG_Z'],
'filldate': pd.to_datetime(['2021-10-21', '2022-01-21', '2022-03-20',
'2022-01-01', '2022-02-01', '2022-03-01',
'2022-02-18', '2022-03-01', '2022-03-22',
'2021-06-18', '2022-02-11', '2022-03-05']),
'supply_days': [90, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30],
'msr_start_dt': pd.to_datetime(['2022-01-01', '2022-01-01', '2022-01-01',
'2022-01-01', '2022-01-01', '2022-01-01',
'2022-01-01', '2022-01-01', '2022-01-01',
'2022-01-01', '2022-01-01', '2022-01-01']),
'msr_end_dt': pd.to_datetime(['2022-03-31', '2022-03-31', '2022-03-31',
'2022-03-31', '2022-03-31', '2022-03-31',
'2022-03-31', '2022-03-31', '2022-03-31',
'2022-03-31', '2022-03-31', '2022-03-31'])
})
# Inspect sample data
df.head(n=len(df))
# calculate PDC scores on the input DataFrame
calcfunc = pdcCalc(dataframe=df,patient_id_col='patient_id', drugname_col='drugname', filldate_col='filldate'
, supply_days_col='supply_days', msr_start_dt_col='msr_start_dt', msr_end_dt_col='msr_end_dt',overlap_adjustment=True) # Set to True to adjust for early refills
pdc_scores_df = calcfunc.calculate_pdc()
# Inspect output
pdc_scores_df.head()
Raw data
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"description": "## README.md\n\nThe objective of this package is to offer a Python-based solution for computing the Proportion of Days Covered (PDC), a widely used metric in the healthcare industry to evaluate medication adherence. As the healthcare analytics sector shifts away from SAS, there is a growing need to recreate key metrics in alternative platforms. This package aims to simplify the process and reduce the workload for business analysts in the healthcare ecosystem by providing a readily available PDC calculation tool, thereby eliminating the need to build it from scratch.\n\n\nPlease use as described below:\n\n**INPUT PARAMETERS:**\n\n**dataframe** - *A pandas dataframe containing the required columns described below.*\n\n**patient_id_col** - *A unique patient identifier. Format = STRING or INTEGER*\n\n**drugname_col** - *The name of the drug being filled or drug class or Generic name, per usual PDC requirements. Format = STRING*\n\n**filldate_col** - *The date of the fill being dispensed. Format = DATE*\n\n**supply_days_col** - *Days of supply being dispensed at fill. Format = INTEGER*\n\n**msr_start_dt_col** - *start date of measurement period for the patient or a reference START DATE. Format = DATE*\n\n**msr_end_dt_col** - *end date of measurement period for the patient or a reference END DATE. Format = DATE*\n\n**overlap_adjustment** - *Set to True to accommodate early refills, otherwise set to False*\n\n\n\n**OUTPUT DATAFRAME** - *A Pandas dataframe containing the following columns*\n\n**patient_id_col** - *This will return a column name representing a unique patient identifier as provided in original input dataframe. FORMAT = STRING*\n\n**drugname_col** - *The name of the drug being filled or drug class or Generic name, as provided in original input dataframe.*\n\n**dayscovered**- *The number of unique days of drug coverage. FORMAT = INTEGER*\n\n**totaldays** - *The total number of days in patient analysis window. Set to 0 if days of coverage is 0. FORMAT = INTEGER*\n\n**pdc_score** - *The patient's PDC score, calculated as dayscovered / totaldays. Set to 0 if days of coverage is 0. FORMAT = FLOAT*\n\n\n\n## USAGE EXAMPLE\n```python\n\n# Import required libraries\nimport pandas as pd\nimport numpy as np\nfrom datetime import datetime\nfrom pdcscore import pdcCalc\n\n# Create a sample dataframe\ndf = pd.DataFrame({\n 'patient_id': ['A001', 'A001', 'A001', 'B001', 'B001', 'B001', 'C001', 'C001', 'C001','C001', 'C001', 'C001'],\n 'drugname': ['DRUG_X', 'DRUG_X', 'DRUG_X', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y', 'DRUG_Y',\n 'DRUG_Z', 'DRUG_Z', 'DRUG_Z'],\n 'filldate': pd.to_datetime(['2021-10-21', '2022-01-21', '2022-03-20',\n '2022-01-01', '2022-02-01', '2022-03-01',\n '2022-02-18', '2022-03-01', '2022-03-22',\n '2021-06-18', '2022-02-11', '2022-03-05']),\n 'supply_days': [90, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30],\n 'msr_start_dt': pd.to_datetime(['2022-01-01', '2022-01-01', '2022-01-01',\n '2022-01-01', '2022-01-01', '2022-01-01',\n '2022-01-01', '2022-01-01', '2022-01-01',\n '2022-01-01', '2022-01-01', '2022-01-01']),\n 'msr_end_dt': pd.to_datetime(['2022-03-31', '2022-03-31', '2022-03-31',\n '2022-03-31', '2022-03-31', '2022-03-31',\n '2022-03-31', '2022-03-31', '2022-03-31',\n '2022-03-31', '2022-03-31', '2022-03-31'])\n})\n\n# Inspect sample data\ndf.head(n=len(df))\n\n# calculate PDC scores on the input DataFrame\ncalcfunc = pdcCalc(dataframe=df,patient_id_col='patient_id', drugname_col='drugname', filldate_col='filldate'\n , supply_days_col='supply_days', msr_start_dt_col='msr_start_dt', msr_end_dt_col='msr_end_dt',overlap_adjustment=True) # Set to True to adjust for early refills\npdc_scores_df = calcfunc.calculate_pdc()\n\n# Inspect output\npdc_scores_df.head()\n\n\n",
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