PyTMLE
PyTMLE is a flexible Python implementation of the Targeted Maximum Likelihood Estimation (TMLE) framework for survival and competing risks outcomes.
The package can be installed from PyPI, for example using pip:
pip install pytmle
It is designed to be easy to use with default models for initial estimates of nuisance functions which are applied in a super learner framework. With a pandas dataframe containing event times, indicators, and (binary) treatment group information in specified columns, it is straight-forward to fit a main PyTMLE class object and get predictions and plots for selected target_times:
from pytmle import PyTMLE
tmle = PyTMLE(df,
col_event_times="time",
col_event_indicator="status",
col_group="group",
target_times=target_times)
tmle.plot(type="risks") # get estimated counterfactual CIF, or set to "rr" or "rd" for ATE estimates based on RR or RD
pred = tmle.predict(type="risks") # store estimates in a data frame
However, it also allows for custom models to be used for the initial estimates or even passing initial estimates directly to the second TMLE stage.
Have a look at the package's Read the Docs page for the detailed API reference and tutorial notebooks.
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Release files for pytmle 0.6.0
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