Trial PathFinder

Python library for Trial Pathfinder, an AI framework to systematically evaluate clinical trial eligibility criteria. Functions provided by TrialPathfinder: encoding eligibility criteria, emulating existing trials under combinations of eligibility rules, evaluating individual eligibility rule with Shapley value and suggesting data-driven criteria.

Reference paper: Evaluating Oncology Trial Eligibility Criteria using Real-World Data and AI.

Installation

The package TrialPathfinder is available on PyPI

pip install TrialPathfinder

We also provide the option for manual installation: download this Github repository and run

cd TrialPathfinder/
python setup.py install --user

Quick Guidance

Here we give a quick guidance of using TrialPathfinder. More details see tutorial/tutorial.ipynb.

import TrialPathfinder as tp

###### Encode Eligibility Criteria #####

# Create cohort selection object
cohort = tp.cohort_selection(patientids, name_PatientID='PatientID')
# Add the data tables needed in the eligibility criterion
cohort.add_table(name_table1, table1)
# Add individual eligibility criterion
cohort.add_rule(rule1)

###### Emulate Existing Trials and Survival Analysis ######

# Given a combination of eligibility rules names_rules (an empty list name_rules=[] indicates fully-relaxed criteria)).
HR, CI, data_cox = tp.emulate_trials(cohort, features, drug_treatment, drug_control, name_rules)

###### Evaluate Individual Criterion ######

# Return the Shapley values for each rule in names_rules
shapley_values = tp.shapley_computation(cohort, features, drug_treatment, drug_control, names_rules)

###### Criteria Relaxation - Data-driven Criteria ######

# Select all the rules with Shapley value less than 0.
names_rules_relax = names_rules[shapley_values<0.]
# Survival analysis on the data-driven criteria
HR, CI, data_cox = tp.emulate_trials(cohort, features, drug_treatment, drug_control, name_rules_relax)

Documentation

We highly recommend reading the tutorial/tutorial.ipynb.

1. Data Requirements

TrialPathfinder reads tables in Pandas dataframe structure (pd.dataframe) as default. The date information should be read as datetime (use function pd.to_datetime to convert if not).

1. Features:

• Patient ID
• Treatment Information
  • Drug name.
  • Start date.
  • Date of outcome. For example, if overall survival (OS) is used as metric, the date of outcome is the date of death. If progression-free survival (PFS) is used as metric, the date of outcome is the date of progression.
  • Date of last visit. The patient's last record date of visit, used for censoring.
• Covariates (optional): adjusted to emulate the blind assignment, used by Inverse probability of treatment weighting (IPTW) or propensity score matching (PSM). Some examples: age, gender, composite race/ethnicity, histology, smoking status, staging, ECOG, and biomarkers status.

2. Tables used by eligibility criteria.

• Use the same Patient ID as the features table.

2. Standards of encoding eligibility criteria

We built a computational workflow to encode the description of eligibility criteria in the protocols into standardized instructions which can be parsed by Trial Pathfinder for cohort selection use.

1. Basic logic.

• Name of the criteria is written in the first row.
• A new statement starts with “#inclusion” or “#exclusion” to indicate the criterion’s type. Whether to include patients who have missing entries in the criteria: “(missing include)” or “(missing exclude)”. The default choice is including patients with missing entries.
• Data name format: “Table[‘featurename’]”. For example, “demographics[‘birthdate’]” denotes column date of birth in table demographics.
• Equation: ==, !=, <, <=, >, >=.
• Logic: AND, OR.
• Other operations: MIN, MAX, ABS.
• Time is encoded as “DAYS(80)”: 80 days; “MONTHS(4)”: 4 months; “YEARS(3)”: 3 years.

Example: criteria "Age" - include patients more than 18 years old when they received the treatment.

Age
#Inclusion
features['StartDate'] >= demographics['BirthDate'] + @YEARS(18>

2. Complex rule with hierarchy.

• Each row is operated in sequential order
  • The tables are prepared before the last row.
  • The patients are selected at the last row.

Example: criteria "Platelets" - include patients whose platelet count ≥ 100 x 10^3/μL.
To encode this criterion, we follow the procedure:

1. Prepare the lab table:
   1. Pick the lab tests for platelet count
   2. The lab test date should be within a -28 to +0 window around the treatment start date
   3. Use the record closest to the treatment start date to do selection.
2. Select patients: lab value larger than 100 x 10^3/μL.

Platelets
#Inclusion
(lab['LabName'] == 'Platelet count')
(lab['TestDate'] >= features['StartDate'] - @DAYS(28) ) AND (lab['TestDate'] <= features['StartDate'])
MIN(ABS(lab['TestDate'] - features['StartDate']))
lab['LabValue'] >= 100