A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules

@article{Sadatsafavi2020ATS,
  title={A threshold‐free summary index for quantifying the capacity of covariates to yield efficient treatment rules},
  author={Mohsen Sadatsafavi and Mohammad Ali Mansournia and Paul Gustafson},
  journal={Statistics in Medicine},
  year={2020},
  volume={39},
  pages={1362 - 1373}
}
When data on treatment assignment, outcomes, and covariates from a randomized trial are available, a question of interest is to what extent covariates can be used to optimize treatment decisions. Statistical hypothesis testing of covariate‐by‐treatment interaction is ill‐suited for this purpose. The application of decision theory results in treatment rules that compare the expected benefit of treatment given the patient's covariates against a treatment threshold. However, determining treatment… 
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References

SHOWING 1-10 OF 38 REFERENCES
Selecting Optimal Subgroups for Treatment Using Many Covariates
TLDR
This work considers the problem of selecting the optimal subgroup to treat when data on covariates are available from a randomized trial or observational study and shows that, in each of these cases, the optimal treatment selection rule involves treating those for whom the predicted mean difference in outcomes exceeds a certain threshold.
Effectively Selecting a Target Population for a Future Comparative Study
TLDR
This article shows a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, using the data from a current study involving similar comparator treatments, and proposes the best scoring system among all competing models.
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
TLDR
This work proposes a framework that prioritizes the analysis and reporting of multivariate risk-based HTE and suggests that other subgroup analyses should be explicitly labeled either as primary sub group analyses (well-motivated by prior evidence and intended to produce clinically actionable results) or secondary (exploratory) subgroups analyses (performed to inform future research).
Decision Curve Analysis: A Novel Method for Evaluating Prediction Models
  • A. Vickers, E. Elkin
  • Psychology
    Medical decision making : an international journal of the Society for Medical Decision Making
  • 2006
TLDR
Decision curve analysis is a suitable method for evaluating alternative diagnostic and prognostic strategies that has advantages over other commonly used measures and techniques.
Personalized evidence based medicine: predictive approaches to heterogeneous treatment effects
TLDR
Fundamental conceptual problems with the prediction of outcome risk and heterogeneity of treatment effect (HTE) are reviewed, as well as the limitations of conventional (one-variable-at-a-time) subgroup analysis.
The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies.
  • K. Claxton
  • Computer Science
    Journal of health economics
  • 1999
Subgroup analyses in confirmatory clinical trials: time to be specific about their purposes
TLDR
A clear operational definition for consistency of treatment effects across subgroups is lacking, but is needed to improve the usability of subgroup analyses in this setting, and methods to particularly explore benefit-risk systematically across sub groups need more research.
Assessing the Performance of Prediction Models: A Framework for Traditional and Novel Measures
TLDR
It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions.
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