Joint Model#

This notebook contains the code for a simple implementation of the Leaspy Joint model on synthetic data.

The following imports are required libraries for numerical computation and data manipulation.

import os

import pandas as pd

import leaspy
from leaspy.io.data import Data

leaspy_root = os.path.dirname(leaspy.__file__)

data_path = os.path.join(leaspy_root, "datasets/data/simulated_data_for_joint.csv")

df = pd.read_csv(data_path, dtype={"ID": str}, sep=";")
print(df.head())

    ID    TIME  EVENT_TIME  EVENT_BOOL       Y0    Y1   Y2   Y3
0  116  78.461        85.5           1  0.44444  0.04  0.0  0.0
1  116  78.936        85.5           1  0.60000  0.00  0.0  0.2
2  116  79.482        85.5           1  0.39267  0.04  0.0  0.2
3  116  79.939        85.5           1  0.58511  0.00  0.0  0.0
4  116  80.491        85.5           1  0.57044  0.00  0.0  0.0

To use the Joint Model in Leaspy, your dataset must include the following columns:

  1. ID : Patient identifier

  2. TIME : Time of measurement

  3. EVENT_TIME : Time of the event

  4. EVENT_BOOL : Event indicator:

    • 1 if the event occurred

    • 0 if censored

    • 2 if a competing event occurred

For one patient, the event time and event bool are the same for each row.

We load the Joint Model from the leaspy.models and transform the dataset in a leaspy-compatible form with the built-in functions.

from leaspy.models import JointModel

data = Data.from_dataframe(df, "joint")
model = JointModel(name="test_model", nb_events=1)

The parameter nb_events should match the number of distinct event types present in the EVENT_BOOL column:

  • If EVENT_BOOL contains values {0, 1}, then nb_events=1.

  • If it contains values {0, 1, 2}, then nb_events=2.

Once the model is initialized, we can fit it to the data.

model.fit(data, "mcmc_saem", seed=1312, n_iter=100, progress_bar=False)

/home/docs/checkouts/readthedocs.org/user_builds/leaspy/checkouts/v2.0.0/src/leaspy/models/time_reparametrized.py:288: UserWarning: You did not provide `source_dimension` hyperparameter for multivariate model, setting it to ⌊√dimension⌋ = 2.
  warnings.warn(

 ==> Setting seed to 1312

/home/docs/checkouts/readthedocs.org/user_builds/leaspy/envs/v2.0.0/lib/python3.11/site-packages/torch/__init__.py:1240: UserWarning: torch.set_default_tensor_type() is deprecated as of PyTorch 2.1, please use torch.set_default_dtype() and torch.set_default_device() as alternatives. (Triggered internally at /pytorch/torch/csrc/tensor/python_tensor.cpp:434.)
  _C._set_default_tensor_type(t)

Fit with `AlgorithmName.FIT_MCMC_SAEM` took: 1s

The Joint Model includes specific parameters such as log_rho_mean and zeta_mean.

print(model.parameters)

{'betas_mean': tensor([[-0.0163, -0.0908],
        [-0.0079, -0.0469],
        [-0.1058, -0.0088]]), 'log_g_mean': tensor([0.1157, 2.8874, 2.5624, 1.3001]), 'log_rho_mean': tensor([1.8040]), 'log_v0_mean': tensor([-3.0789, -3.8272, -3.8023, -2.7624]), 'n_log_nu_mean': tensor([-1.9862]), 'noise_std': tensor(0.0947, dtype=torch.float64), 'tau_mean': tensor([78.4523], dtype=torch.float64), 'tau_std': tensor([5.7890], dtype=torch.float64), 'xi_std': tensor([0.4379], dtype=torch.float64), 'zeta_mean': tensor([[0.0421],
        [0.0660]])}

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