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Trials

, 7:30

First Online: 05 October 2006Received: 08 June 2006Accepted: 05 October 2006DOI: 10.1186-1745-6215-7-30

Cite this article as: Vickers, A.J., Kramer, B.S. & Baker, S.G. Trials 2006 7: 30. doi:10.1186-1745-6215-7-30

Abstract

BackgroundA key aspect of randomized trial design is the choice of risk group. Some trials include patients from the entire at-risk population, others accrue only patients deemed to be at increased risk. We present a simple statistical approach for choosing between these approaches. The method is easily adapted to determine which of several competing definitions of high risk is optimal.

MethodWe treat eligibility criteria for a trial, such as a smoking history, as a prediction rule associated with a certain sensitivity the number of patients who have the event and who are classified as high risk divided by the total number patients who have an event and specificity the number of patients who do not have an event and who do not meet criteria for high risk divided by the total number of patients who do not have an event. We then derive simple formulae to determine the proportion of patients receiving intervention, and the proportion who experience an event, where either all patients or only those at high risk are treated. We assume that the relative risk associated with intervention is the same over all choices of risk group. The proportion of events and interventions are combined using a net benefit approach and net benefit compared between strategies.

ResultsWe applied our method to design a trial of adjuvant therapy after prostatectomy. We were able to demonstrate that treating a high risk group was superior to treating all patients; choose the optimal definition of high risk; test the robustness of our results by sensitivity analysis. Our results had a ready clinical interpretation that could immediately aid trial design.

ConclusionThe choice of risk group in randomized trials is usually based on rather informal methods. Our simple method demonstrates that this decision can be informed by simple statistical analyses.

Electronic supplementary materialThe online version of this article doi:10.1186-1745-6215-7-30 contains supplementary material, which is available to authorized users.

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Author: Andrew J Vickers - Barry S Kramer - Stuart G Baker

Source: https://link.springer.com/







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