pySEQTarget 0.12.2

Sequentially Nested Target Trial Emulation

pySEQTarget - Sequentially Nested Target Trial Emulation

Implementation of sequential trial emulation for the analysis of observational databases. The SEQTaRget software accommodates time-varying treatments and confounders, as well as binary and failure time outcomes. SEQTaRget allows to compare both static and dynamic strategies, can be used to estimate observational analogs of intention-to-treat and per-protocol effects, and can adjust for potential selection bias.

Installation

You can install the development version of pySEQTarget from github with:

pip install git+https://github.com/CausalInference/pySEQTarget

Or from pypi iwth

pip install pySEQTarget

Setting up your Analysis

The primary API, SEQuential uses a dataclass system to handle function input. You can then recover elements as they are built by interacting with the SEQuential object you create.

From the user side, this amounts to creating a dataclass, SEQopts, and then feeding this into SEQuential. If you forgot to add something at class instantiation, you can, in some cases, add them when you call their respective class method.

import polars as pl
from pySEQTarget import SEQuential, SEQopts
from pySEQTarget.data import load_data

data = load_data("SEQdata")
options = SEQopts(km_curves = True)

# Initiate the class
model = SEQuential(data, 
                   id_col = "ID",
                   time_col = "time",
                   eligible_col = "eligible",
                   treatment_col = "tx_init",
                   outcome_col = "outcome",
                   time_varying_cols = ["N", "L", "P"],
                   fixed_cols = ["sex"],
                   method = "ITT",
                   parameters = options)
model.expand()  # Construct the nested structure
model.bootstrap(bootstrap_nboot = 20) # Run 20 bootstrap samples
model.fit() # Fit the model
model.survival() # Create survival curves
model.plot() # Create and show a plot of the survival curves
model.collect() # Collection of important information

Assumptions

There are several key assumptions in this package -

1. User provided time_col begins at 0 per unique id_col, we also assume this column contains only integers and continues by 1 for every time step, e.g. (0, 1, 2, 3, 4, ...) is allowed and (0, 1, 2, 2.5, ...) or (0, 1, 4, 5) are not
   1. Provided time_col entries may be out of order at intake as a sort is enforced at expansion.
2. eligible_col and elements of excused_colnames are once 1, only 1 (with respect to time_col) flag variables.