Users' Guide to the Medical Literature

This Users’ Guide to the Medical Literature discusses the use of machine learning models as a diagnostic tool, and it explains the important steps needed for making these models and the outcomes they derive clinically effective.

This Users’ Guide to the Medical Literature discusses discrimination and calibration, 2 primary ways to measure and compare the accuracy of clinical risk prediction models.

This Users’ Guide to the Medical Literature discusses strategies for adjusting analyses as a way of addressing prognostic imbalance in studies of therapy and harm.

Sun and coauthors provide 5 criteria to help clinicians distinguish credible subgroup analyses from spurious subgroup analyses.