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A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints.

Kunz CU, Friede T, Parsons N, Todd S, Stallard N - J Biopharm Stat (2015)

Bottom Line: In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis.The comparison focuses on the power and the probability of correctly identifying the most promising treatment.We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

View Article: PubMed Central - PubMed

Affiliation: a Warwick Medical School , University of Warwick , Coventry , United Kingdom.

ABSTRACT
In an adaptive seamless phase II/III clinical trial interim analysis, data are used for treatment selection, enabling resources to be focused on comparison of more effective treatment(s) with a control. In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focuses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

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Probability to select treatment 1 (panels A1 and B1) and power (panels A2 and B2) for the Stallard (2010) and Friede et al. (2011) methods for different parameter settings under the fixed effects model.
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Figure 0001: Probability to select treatment 1 (panels A1 and B1) and power (panels A2 and B2) for the Stallard (2010) and Friede et al. (2011) methods for different parameter settings under the fixed effects model.

Mentions: The upper panels (panels A1 and B1) of Fig. 1 show the probability of selecting treatment T1 using the Friede et al and Stallard selection methods when three experimental treatments are included in the first stage and . Panel A1 gives the selection probability with for different stage one sample sizes for a a range of values. Panel B1 gives the selection probability with , , and for a range of values (with so that these are the standardized values), again for a range of values.Figure 1


A comparison of methods for treatment selection in seamless phase II/III clinical trials incorporating information on short-term endpoints.

Kunz CU, Friede T, Parsons N, Todd S, Stallard N - J Biopharm Stat (2015)

Probability to select treatment 1 (panels A1 and B1) and power (panels A2 and B2) for the Stallard (2010) and Friede et al. (2011) methods for different parameter settings under the fixed effects model.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4339952&req=5

Figure 0001: Probability to select treatment 1 (panels A1 and B1) and power (panels A2 and B2) for the Stallard (2010) and Friede et al. (2011) methods for different parameter settings under the fixed effects model.
Mentions: The upper panels (panels A1 and B1) of Fig. 1 show the probability of selecting treatment T1 using the Friede et al and Stallard selection methods when three experimental treatments are included in the first stage and . Panel A1 gives the selection probability with for different stage one sample sizes for a a range of values. Panel B1 gives the selection probability with , , and for a range of values (with so that these are the standardized values), again for a range of values.Figure 1

Bottom Line: In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis.The comparison focuses on the power and the probability of correctly identifying the most promising treatment.We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

View Article: PubMed Central - PubMed

Affiliation: a Warwick Medical School , University of Warwick , Coventry , United Kingdom.

ABSTRACT
In an adaptive seamless phase II/III clinical trial interim analysis, data are used for treatment selection, enabling resources to be focused on comparison of more effective treatment(s) with a control. In this paper, we compare two methods recently proposed to enable use of short-term endpoint data for decision-making at the interim analysis. The comparison focuses on the power and the probability of correctly identifying the most promising treatment. We show that the choice of method depends on how well short-term data predict the best treatment, which may be measured by the correlation between treatment effects on short- and long-term endpoints.

Show MeSH