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. 2016 Nov;36(8):999-1010.
doi: 10.1177/0272989X16655346. Epub 2016 Jun 27.

Quantitative Framework for Retrospective Assessment of Interim Decisions in Clinical Trials

Roger Stanev  1
Affiliations

Quantitative Framework for Retrospective Assessment of Interim Decisions in Clinical Trials

Roger Stanev. Med Decis Making. 2016 Nov.

Abstract

This article presents a quantitative way of modeling the interim decisions of clinical trials. While statistical approaches tend to focus on the epistemic aspects of statistical monitoring rules, often overlooking ethical considerations, ethical approaches tend to neglect the key epistemic dimension. The proposal is a second-order decision-analytic framework. The framework provides means for retrospective assessment of interim decisions based on a clear and consistent set of criteria that combines both ethical and epistemic considerations. The framework is broadly Bayesian and addresses a fundamental question behind many concerns about clinical trials: What does it take for an interim decision (e.g., whether to stop the trial or continue) to be a good decision? Simulations illustrating the modeling of interim decisions counterfactually are provided.

Keywords: Bayesian; DSMB; counterfactual reasoning; ethical framework; group sequential methods; interim analyses; statistical decisions; stopping rules.

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Figures

Figure 1
Figure 1
Decision rule R1 as a function of possible values of (y2–y1), with (y2–y1) at the end of the trial plotted as a function of (y2–y1) at interim. Actions: a1 (black circles), a2 (red squares), a1.f (purple triangles), and a2.f (white squares).
Figure 2
Figure 2
Decision rule R2 as a function of possible values of y2, with y2 at the end of the trial plotted as a function of y2 at interim. Actions: a1 (black circles), a2 (red squares), a1.f (purple triangles), and a2.f (white squares).
Figure 3
Figure 3
Weighed losses of stopping (action a1) and continuing (averaging actions a1.f and a2.f) for stopping rules R1 and R2, when H1 is true.
Figure 4
Figure 4
Stopping rule R1 v. stopping rule R2 compared with respect to Bayes risk and different loss functions (ethical loss v. scientific loss).
See this image and copyright information in PMC

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