Abstract
This paper reviews two types of geometric methods proposed in recent years for defining statistical decision rules based on two-dimensional parameters that characterize treatment effect in a medical setting. A common example is that of making decisions, such as comparing treatments or selecting a best dose, based on both the probability of efficacy and the probability of toxicity. In most applications, the two-dimensional parameter is defined in terms of a model parameter of higher dimension including effects of treatment and possibly covariates. Each method uses a geometric construct in the two-dimensional parameter space based on a set of elicited parameter pairs as a basis for defining decision rules. The first construct is a family of contours that partitions the parameter space, with the contours constructed so that all parameter pairs on a given contour are equally desirable. The partition is used to define statistical decision rules that discriminate between parameter pairs in term of their desirabilities. The second construct is a convex two-dimensional set of desirable parameter pairs, with decisions based on posterior probabilities of this set for given combinations of treatments and covariates under a Bayesian formulation. A general framework for all of these methods is provided, and each method is illustrated by one or more applications.
Original language | English (US) |
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Pages (from-to) | 516-527 |
Number of pages | 12 |
Journal | Journal of Statistical Planning and Inference |
Volume | 138 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2008 |
Keywords
- Bayesian statistics
- Clinical trials
- Dose-finding
- Indifference set
- Medical decision making
- Phase II clinical trial
- Trade-offs
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Applied Mathematics