Article

# Application of statistical decision theory to treatment choices: Implications for the design and analysis of clinical trials

Statistics in Medicine (Impact Factor: 1.83). 09/1986; 5(5):411-20. DOI: 10.1002/sim.4780050505

Source: PubMed

**ABSTRACT**

This paper explores the application of statistical decision theory to treatment choices in cancer which involve difficult value judgements in weighing beneficial and deleterious outcomes of treatment. Strengths and weaknesses of using decision theory are illustrated by considering the problem of selecting chemotherapy in advanced ovarian cancer. The paper includes an assessment of individual preferences in 27 volunteers and a discussion of some problems in utility assessment. An alternative approach, using threshold analysis, is presented in which the results of the decision analysis are expressed as a function of utility parameters. By knowing what sets of utilities favour each treatment, the assessment of patient preferences can then be focused on important differences of treatment options. The implications of these results for the design and analysis of clinical trials are discussed.

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**ABSTRACT:**Those who report a clinical trial should acknowledge the right of the ‘consumer’ to make decisions based on his own valuation of the beneficial and adverse effects which rival treatments may have. Suppose a new patient is inclined to trade one unit of benefit for c units of complication. Then he should (should not) be given the treatment if his estimated utility gain, χ1 -cχ2, is positive (negative) and statistically significant according to the data of the trial: here χ1 (χ2) denotes the observed average benefit (complication level). If the estimated gain is not statistically significant, the data do not allow any firm recommendation. This c-dependent recommendation in general cannot be determined from inspection of a joint confidence region for the two means concerned. Therefore investigators should present the outcome of the significance test as a function of c (inverted inference). Typically there are several types of adverse effect or benefit, in which case the quantity c must be generalized into a vector of personal relative utility weights.Statistics in Medicine 10/1987; 6(7):745 - 752. DOI:10.1002/sim.4780060705 · 1.83 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**We have derived the variance of an expected utility for a probability tree in medical decision analysis based on a Taylor series approximation of the expected utility as a function of the probability and utility values used in the decision tree. The resulting variance estimate is an algebraic expression of the variances associated with the probability and utility estimates used. We also derive expressions for the case where the input parameter estimates are not independent. We discuss the choice of input parameters and their variance estimates and give an example that compares two protocols for the treatment of chlamydial infection.Statistics in Medicine 02/1989; 8(2):229-41. DOI:10.1002/sim.4780080209 · 1.83 Impact Factor - [Show abstract] [Hide abstract]

**ABSTRACT:**In clinical trials endpoints other than total and/or disease-free survival are gaining more and more interest. In particular, quality of life (QOL) or the well-being of patients has emerged as a synonym for variables describing the subjective reactions of patients towards their disease and its treatment. The statistical analysis of such QOL data is complicated firstly by the large number of variables measured and their obvious lack of objectivity. The construction of suitable aggregate measures allowing a reduction of the measurements into a (preferably) unidimensional index are discussed in the context of an analysis at a fixed time point during the course of treatment. A second problem arises from the consideration that a patient's well-being is subject to changes over time. We discuss the modelling of QOL by suitable stochastic processes which are extensions of a multistate disease process. This allows QOL events to be incorporated into methods of survival analysis by either estimating the relevant transition probabilities between states or calculating quality-adjusted survival times. Finally, some brief guidelines for the planning of clinical trials including QOL measurements will be proposed.Statistics in Medicine 07/1990; 9(7):749-63. DOI:10.1002/sim.4780090705 · 1.83 Impact Factor

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