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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 based on the methods by Stallard (2010) for a range of  values and by Friede et al. (2011) for a range of  values under the random effects model (given that treatment 1 is the most effective).
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Figure 0003: Probability to select treatment 1 based on the methods by Stallard (2010) for a range of values and by Friede et al. (2011) for a range of values under the random effects model (given that treatment 1 is the most effective).

Mentions: The latter point is illustrated more clearly in Fig. 3. This shows the probability under the random effects model of correctly selecting treatment T1 given that this is the most effective for , , , , for the Stallard method for a range of values and for the Friede et al. method for a range of values. Since the selection probabilities for the Stallard method do not depend on and for the Friede et al. method do not depend on , the two lines are shown on the same graph. Comparing the two lines, we see that the Stallard method always has a higher selection probability than the Friede et al. method if except when , when both probabilities are the same. The three horizontal lines represent selection probabilities for the Friede et al. method where is fixed to either 1 (short dash), 0.95 (dash dot), or 0.9 (long dash). Comparing these lines with those for the Stallard method, we observe that if the Friede et al. method is always better than the Stallard method regardless of (with the exception of , when the selection probabilities for the two methods are again equal). If , the Friede et al. method is only better than the Stallard method if is small. While if , the Stallard method is always better than the Friede et al. method regardless of .Figure 3


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 based on the methods by Stallard (2010) for a range of  values and by Friede et al. (2011) for a range of  values under the random effects model (given that treatment 1 is the most effective).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 0003: Probability to select treatment 1 based on the methods by Stallard (2010) for a range of values and by Friede et al. (2011) for a range of values under the random effects model (given that treatment 1 is the most effective).
Mentions: The latter point is illustrated more clearly in Fig. 3. This shows the probability under the random effects model of correctly selecting treatment T1 given that this is the most effective for , , , , for the Stallard method for a range of values and for the Friede et al. method for a range of values. Since the selection probabilities for the Stallard method do not depend on and for the Friede et al. method do not depend on , the two lines are shown on the same graph. Comparing the two lines, we see that the Stallard method always has a higher selection probability than the Friede et al. method if except when , when both probabilities are the same. The three horizontal lines represent selection probabilities for the Friede et al. method where is fixed to either 1 (short dash), 0.95 (dash dot), or 0.9 (long dash). Comparing these lines with those for the Stallard method, we observe that if the Friede et al. method is always better than the Stallard method regardless of (with the exception of , when the selection probabilities for the two methods are again equal). If , the Friede et al. method is only better than the Stallard method if is small. While if , the Stallard method is always better than the Friede et al. method regardless of .Figure 3

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