Get Started with pseudostate

This is a reproducible example that shows how to use pseudostate.

Background

The theoretical foundation for pseudo-observations was laid out in a 2003 paper by Andersen, Klein, and Rosthøj:

• Andersen, P. K., Klein, J. P., & Rosthøj, S. (2003). Generalised linear models for correlated pseudo-observations, with applications to multi-state models. Biometrika, 90(1), 15–27.

A popular implementation of this technique is the pseudo R package, which is available on CRAN:

• https://cran.r-project.org/package=pseudo

import polars as pl
from pseudostate import calculate_pseudostates

times_and_reals = pl.DataFrame({
        "times": [1, 2, 2, 3, 4, 5, 6],
        "reals": [1, 0, 2, 1, 0, 2, 1]
})

fixed_time_horizon = 5

calculate_pseudostates(times_and_reals, fixed_time_horizon)

shape: (7, 6)

estimate_origin	times	state_occupancy_probability_0	state_occupancy_probability_1	state_occupancy_probability_2	row_id
enum	f64	f64	f64	f64	i32
"fixed_time_horizons"	5.0	0.0	1.0	4.4409e-16	0
"fixed_time_horizons"	5.0	0.375	0.25	0.375	1
"fixed_time_horizons"	5.0	0.0	0.0	1.0	2
"fixed_time_horizons"	5.0	-0.125	1.25	-0.125	3
"fixed_time_horizons"	5.0	0.541667	-0.083333	0.541667	4
"fixed_time_horizons"	5.0	-0.791667	-0.083333	1.875	5
"fixed_time_horizons"	5.0	1.875	-0.083333	-0.791667	6