medmodels.treatment_effect.report#

This module contains functions to generate reports of the treatment effect class.

Classes

`ContinuousReport`	A dictionary containing the results of continuous treatment effect estimators.
`FullReport`	A dictionary containing the results of all estimation methods.
`Report`(treatment_effect)	Class to generate reports of the treatment effect class.

class ContinuousReport[source]#

Bases: TypedDict

A dictionary containing the results of continuous treatment effect estimators.

average_treatment_effect: float#

clear() → None. Remove all items from D.#

cohens_d: float#

copy() → a shallow copy of D#

fromkeys(value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

hedges_g: float#

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#

class FullReport[source]#

Bases: TypedDict

A dictionary containing the results of all estimation methods.

absolute_risk_reduction: float#

clear() → None. Remove all items from D.#

confounding_bias: float#

copy() → a shallow copy of D#

fromkeys(value=None, /)#: Create a new dictionary with keys from iterable and values set to value.

get(key, default=None, /)#: Return the value for key if key is in the dictionary, else default.

hazard_ratio: float#

items() → a set-like object providing a view on D's items#

keys() → a set-like object providing a view on D's keys#

number_needed_to_treat: float#

odds_ratio: float#

pop(k[, d]) → v, remove specified key and return the corresponding value.#: If the key is not found, return the default if given; otherwise, raise a KeyError.

popitem()#

Remove and return a (key, value) pair as a 2-tuple.

Pairs are returned in LIFO (last-in, first-out) order. Raises KeyError if the dict is empty.

relative_risk: float#

setdefault(key, default=None, /)#

Insert key with a value of default if key is not in the dictionary.

Return the value for key if key is in the dictionary, else default.

update([E, ]**F) → None. Update D from dict/iterable E and F.#: If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]

values() → an object providing a view on D's values#

class Report(treatment_effect)[source]#

Bases: object

Class to generate reports of the treatment effect class.

continuous_estimators_report(medrecord, outcome_variable, reference='last')[source]#

Generates a report of continuous treatment effect estimators.

Parameters:

medrecord (MedRecord) – An instance of the MedRecord class containing medical data.
outcome_variable (MedRecordAttribute) – The attribute in the edge that contains the outcome variable.
reference (Literal["first", "last"], optional) – The reference point for the exposure time. Options include “first” and “last”. If “first”, the function returns the earliest exposure edge. If “last”, the function returns the latest exposure edge. Defaults to “last”.

Return type:

ContinuousReport

Returns:

ContinuousReport –

A dictionary containing the results of continuous: treatment effect estimators: average treatment effect, Cohen’s d and Hedges’ g.

full_report(medrecord)[source]#

Generates a full report of the treatment effect estimation.

Parameters:

medrecord (MedRecord) – An instance of the MedRecord class containing medical data.

Return type:

FullReport

Returns:

FullReport –

A dictionary containing the results of all estimation: methods: relative risk, odds ratio, confounding bias, absolute risk reduction, number needed to treat, and hazard ratio.