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A Python implementation of minimisation for clinical trials

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A Python implementation of minimisation for clinical trials


  • Implements minimization as described in Pocock + Simon (1975): Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial
  • Tested using pytest to ensure the results match the original implementation.
  • Pure Python module with no dependencies (pandas is useful when conducting simulations but is optional)
  • Includes all functions described in the article: range, standard deviation, variance, etc.
  • Also implements the biased-coin minimization method described in Han et al. (2009): Randomization by minimization for unbalanced treatment allocation, to allow for unequal allocation ratios.
  • Allows pure random assignment for comparison
  • Simulation module to allow simulating the effects of different assignment schemes.


Comparing minimization to purely random assignment by simulation:

Simulation results

See the example notebook for details of the simulation.


This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.


0.5.0 (2019-05-08)

  • Improved performance with large numbers of factors/factor levels by making the count table sparse.

0.4.0 (2019-02-02)

  • Switched to Mozilla Public License.

0.3.0 (2019-01-02)

  • Make it easier to check the valid total imbalance and probability methods.

0.2.0 (2018-10-22)

  • Add the biased coin minimization method to allow for unequal treatment allocations.

0.1.0 (2018-10-07)

  • First release on PyPI.

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