Metadata-Version: 2.1
Name: propdayscov
Version: 1.0.4
Summary: A package for calculating medication PDC.
Home-page: https://github.com/REPPCS/propdayscov
Author: Andrew Repp
Author-email: ajrepp1@gmail.com
License: UNKNOWN
Description: The purpose of this package is to provide a python-native means to calculate a common industry metric for
        medication adherence, Proportion of Days Covered (PDC).  Much of the healthcare analytics industry is 
        transitioning from SAS and are working to replicate such fundametal metrics in new environments.
        The goal is to offer one less thing that needs to be rebuilt from scratch, and hopefully smooth the path of 
        both better healthcare and the FOSS movement.
        
        The most comprehensive FOSS package for medication adherence is currently AdhereR, and anyone looking for a 
        broader coverage of the topic would be well served to give them a look.  They can be found at
        https://www.adherer.eu/ and offer a variety of adherence metrics and visualizatons.  The propdayscov package
        is designed to be simpler to use, python-native, and offers a stricter focus on PDC.
        
        A popular implementation of PDC in SAS, and my original introduction to the topic, can be found at
        http://support.sas.com/resources/papers/proceedings13/168-2013.pdf
        This paper describes the nuances of the metric well, and will serve as a good primer for any analyst new
        to its use.
        
        As of right now, this package consists of a single public function, calc_pdc.  Usage is described below:
          
        Parameters:  
          
         - indata - A pandas dataframe containing the required columns described below.
         - druglevel - Accepts the values of "Y" or "N" to indicate whether you want to
            additionally output drug-level PDC values
         - mprocmode - Accepts the values of "Y" or "N" to indicate whether you want to run the
            analysis in multiprocessing mode or not.  Defaults to "N"
         - workers - The number of worker processes to be instantiated for multiprocessing.  If you
            aren't sure, a decent 'best guess' can be found using multiprocessing.cpu_count()
          
        Input - A Pandas dataframe containing the following column:  
         - P_ID - A unique patient identifier. Format = STRING  
         - DRUGNAME - The name of the drug being filled.  Generic name, per usual PDC requirements.  
            Format = STRING  
         - FILLDATE - The date of the fill being dispensed.  Format = DATE  
         - DAYSSUPPLY - Days of supply being dispensed at fill.  Format = INTEGER  
         - MBRELIGSTART - First date of coverage eligiblity for patient.  Per URAC, can be set to  
            first known date of fill if eligibility records are not available. Format = DATE  
         - MBRELIGEND - Last date of coverage eligiblity for patient.  Per URAC, can be set to  
            last known date of fill if eligibility records are not available. Format = DATE  
          
        Returns - A Pandas dataframe containing the following columns  
         - P_ID - A unique patient identifier, as provided in input. FORMAT = STRING  
         - *DRUGNAME - The name of the drug being filled, as provided in input.  Optional  
            column, only output if druglevel parameter is set to "Y".  FORMAT = STRING  
         - COV_DAYS - The number of unique days of drug coverage, after shifting coverage  
            to accommodate early refills. FORMAT = INTEGER  
         - TOT_DAYS - The total number of days in patient analysis window.  Set to 0  
            if days of coverage is 0.  FORMAT = INTEGER  
         - PDC_RATIO - The patient's PDC ratio, calculated as COV_DAYS / TOT_DAYS.  
            Set to 0 if days of coverage is 0.  FORMAT = FLOAT  
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
