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. 2014 Apr 15;110(8):1950-7.
doi: 10.1038/bjc.2014.156. Epub 2014 Mar 25.

Adaptive Designs for Clinical Trials Assessing Biomarker-Guided Treatment Strategies

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Free PMC article

Adaptive Designs for Clinical Trials Assessing Biomarker-Guided Treatment Strategies

J Wason et al. Br J Cancer. .
Free PMC article

Abstract

Background: The Biomarker Strategy Design has been proposed for trials assessing the value of a biomarker in guiding treatment in oncology. In such trials, patients are randomised to either receive the standard chemotherapy treatment or a biomarker-directed treatment arm, in which biomarker status is used to guide treatment.

Methods: Motivated by a current trial, we consider an adaptive design in which two biomarkers are assessed. The trial is conducted in two stages. In the first stage, patients in the biomarker-guided arm are assessed using a standard and an alternative cheaper biomarker, with the standard biomarker guiding treatment. An analysis comparing biomarker results is then used to choose the biomarker to use for the remainder of the trial. The new biomarker is used if the results for the two biomarkers are sufficiently similar.

Results: We show that in practical situations the first-stage results can be used to adapt the trial without type I error rate inflation. We also show that there can be considerable cost gains with only a small loss in power in the case where the alternative biomarker is highly concordant with the standard one.

Conclusions: Adaptive designs have an important role in reducing the cost and increasing the clinical utility of trials evaluating biomarker-guided treatment strategies.

Figures

Figure 1
Figure 1
Schema of adaptive design with selection between a gold standard (biomarker 1) and cheaper alternative (biomarker 2).
Figure 2
Figure 2
Plots showing the power of the two-stage procedure to declare non-inferiority as the kappa threshold, at which biomarker 2 is selected, changes. (AH) Scenarios 1–8 in Table 1. The eight scenarios use different probabilities of an event for the four patient groups (i.e., positive/treated, positive/untreated, negative/treated, negative/untreated). These are listed in Table 1. In scenario 6, the null hypotheses are true, so the lines give the type I error rate. Curves are shown for three possible performance characteristics of biomarker.
Figure 3
Figure 3
Plots showing the expected cost of the two-stage procedure as the kappa threshold, at which biomarker 2 is selected, changes for 150 patients per arm in the first stage. The four panels assume different costs of the cheaper biomarker: (A) $1000; (B) $2000; (C) $3000; (D) $4000. The gold-standard biomarker is assumed to cost $4000. The black dashed line in the figures shows the cost of a trial that only used biomarker 1 and did not have an interim analysis.
Figure 4
Figure 4
Plots showing the power of the two-stage procedure to declare non-inferiority for a time-to-event end point as the kappa threshold, at which biomarker 2 is selected, changes. The four scenarios use different hazard rates for the four patient groups (i.e., positive/treated, positive/untreated, negative/treated, negative/untreated). These are: (A) (0.045, 0.139, 0.045, 0.045), (B) (0.045, 0.322, 0.045, 0.045), (C) (0.045, 0.072, 0.045, 0.045), (D) (0.045, 0.139, 0.045, 0.047). Power at each value of the kappa threshold is estimated from 25 000 simulated replicates.

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