FastEHR.dataloader.utils.study_criteria
=======================================

.. py:module:: FastEHR.dataloader.utils.study_criteria


Classes
-------

.. autoapisummary::

   FastEHR.dataloader.utils.study_criteria.index_inclusion_method


Module Contents
---------------

.. py:class:: index_inclusion_method(index_on, outcomes, require_outcome=False, include_on_events_prior_to_index=None, exclude_on_events_prior_to_index=None, exclude_on_events=None, study_period=['1998-01-01', '2019-12-31'], age_at_entry_range=[25, 85], min_registered_years=1, min_events=None)

   Filters a dataset based on indexing criteria, outcomes, and study constraints.

   Parameters
   ----------
   index_on (Union[str, List[str], float, int]):
       Defines the indexing criteria:
       - If indexing on an event, provide the event name as a string.
       - If indexing on multiple events, provide a list of event name strings.
       - If indexing by age, provide a float or integer representing age in days.
       - When multiple index events exist, the first valid index date is taken.

   outcomes (Union[List[str], Callable]):
       Specifies the outcomes of interest:
       - A list of event names to be used as outcomes.
       - A callable object that filters the event column of the dataframe for the desired outcomes.

   require_outcome (bool, optional):
       Whether the outcome must be observed within the study period constraints.
       - If `False`, includes patients who have not yet seen the outcome (e.g., survival analysis).
       - If `True`, includes only patients who have observed the outcome, though its value may still be missing.

   include_on_events_prior_to_index (Tuple[str, int], optional):
       Filters patients based on prior events before the index date.
       - The first element is a string indicating the event token.
       - The second element is an integer representing the number of days before the index event.
       - Example: If studying medication effects post-diagnosis, you may include only those diagnosed 60 days before medication.

   exclude_on_events_prior_to_index (List[str], optional):
       Excludes patients based on events occurring before the index date.
       - Example 1: If studying the initiation of a medication, patients already on the medication may be excluded.

   exclude_on_events (List[str], optional):
       Excludes patients based on whether they have experienced an event at any time.
       - Example: If studying Type II diabetes, patients with a Type I diabetes diagnosis may be excluded.

   study_period (List[str], optional):
       Defines the study period in the format `["yyyy-mm-dd", "yyyy-mm-dd"]` in chronological order.
       - The start of the study period does not determine the start of observations but contributes to defining the indexing period.
       - The study end date marks the end of observations.

   age_at_entry_range (List[int], optional):
       Defines the allowable age range for cohort entry in years `[min_age, max_age]`.

   min_registered_years (int, optional):
       The minimum number of years a patient must be registered at the practice for inclusion at cohort entry.

   min_events (int, optional):
       The minimum number of events a patient must have experienced (up to and including the index event) to be included in the study.

   Notes:
   - This function is executed on a per-practice basis, so there is no concern about overlapping `PATIENT_ID` values.



   .. py:method:: fit(lazy_static, lazy_combined_frame)