A Comparison of Methods for Treatment Selection in Seamless Phase II/III Clinical Trials Incorporating Information on Short-Term Endpoints

Kunz, Cornelia Ursula; Friede, Tim; Parsons, Nicholas R.; Todd, Susan; Stallard, Nigel

doi:10.1080/10543406.2013.840646

Cited by 31 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They show that the correlation between the measurements taken at different time points can be exploited in order to increase the precision of the estimate of the primary endpoint effect. Todd and Stallard (2005) describe a method for adaptive seamless phase II/III designs where a secondary endpoint is incorporated into the trial design in order to select the most promising treatment at an interim analysis (see also Stallard, 2010;Kunz et al, 2015). Furthermore, Kunz et al (2014) developed an approach to select the most promising treatment based on interim analysis data incorporating a short-term endpoint.…”

Section: Multiple Short-term Surrogate Endpointsmentioning

confidence: 99%

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

et al. 2016

Self Cite

View full text Add to dashboard Cite

Regulatory authorities require that the sample size of a confirmatory trial is calculated prior to the start of the trial. However, the sample size quite often depends on parameters that might not be known in advance of the study. Misspecification of these parameters can lead to under‐ or overestimation of the sample size. Both situations are unfavourable as the first one decreases the power and the latter one leads to a waste of resources. Hence, designs have been suggested that allow a re‐assessment of the sample size in an ongoing trial. These methods usually focus on estimating the variance. However, for some methods the performance depends not only on the variance but also on the correlation between measurements. We develop and compare different methods for blinded estimation of the correlation coefficient that are less likely to introduce operational bias when the blinding is maintained. Their performance with respect to bias and standard error is compared to the unblinded estimator. We simulated two different settings: one assuming that all group means are the same and one assuming that different groups have different means. Simulation results show that the naïve (one‐sample) estimator is only slightly biased and has a standard error comparable to that of the unblinded estimator. However, if the group means differ, other estimators have better performance depending on the sample size per group and the number of groups.

show abstract

Section: Multiple Short-term Surrogate Endpointsmentioning

confidence: 99%

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly methods for continuous data have also been developed by Friede et al. (); Kunz, Friede, Parsons, Todd, and Stallard (); Stallard (); Stallard, Kunz, Todd, Parsons, and Friede () for Phase II/III seamless trials with treatment selection. Hampson and Jennison () discussed use of short‐term data in group sequential tests for delayed responses.…”

Section: Introductionmentioning

confidence: 99%

Interim analysis incorporating short‐ and long‐term binary endpoints

2019

View full text Add to dashboard Cite

Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short‐term information in an interim analysis if the long‐term primary endpoint has not been yet observed for some of the patients. At first we consider a two‐stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long‐term endpoint only, short‐term endpoint only, and combining data from both are compared. For each approach, equivalent cut‐off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P‐values based on Z‐statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.

show abstract

“…Their simulations have shown that all four approaches have good properties regarding the power and type I error in moderate to large sample sizes. Similar methods for continuous data have also been considered in the literatures . For example, Galbraith and Marschner adapted and extended the methodology described in the work of Marschner and Becker to include continuous endpoints assessed at an arbitrary number of follow‐up times.…”

Section: Introductionmentioning

confidence: 99%

“…Similar methods for continuous data have also been considered in the literatures. [15][16][17][18][19][20] For example, Galbraith and Marschner 15 adapted and extended the methodology described in the work of Marschner and Becker 9 to include continuous endpoints assessed at an arbitrary number of follow-up times. Hampson and Jennison 17 generalized this to a group sequential design for the situation where the primary endpoint is measured with delay.…”

Section: Introductionmentioning

confidence: 99%

Evaluating futility of a binary clinical endpoint using early read‐outs

Lancker

Vandebosch

Vansteelandt

et al. 2019

Statistics in Medicine

View full text Add to dashboard Cite

Interim analyses are routinely used to monitor accumulating data in clinical trials. When the objective of the interim analysis is to stop the trial if the trial is deemed futile, it must ideally be conducted as early as possible. In trials where the clinical endpoint of interest is only observed after a long follow‐up, many enrolled patients may therefore have no information on the primary endpoint available at the time of the interim analysis. To facilitate earlier decision‐making, one may incorporate early response data that are predictive for the primary endpoint (eg, an assessment of the primary endpoint at an earlier time) in the interim analysis. Most attention so far has been given to the development of interim test statistics that include such short‐term endpoints, but not to decision procedures. Existing tests moreover perform poorly when the information is scarce, eg, due to rare events, when the cohort of patients with observed primary endpoint data is small, or when the short‐term endpoint is a strong but imperfect predictor. In view of this, we develop an interim decision procedure based on the conditional power approach that utilizes the short‐term and long‐term binary endpoints in a framework that is expected to provide reliable inferences, even when the primary endpoint is only available for a few patients, and has the added advantage that it allows the use of historical information. The operational characteristics of the proposed procedure are evaluated for the phase III clinical trial that motivated this approach, using simulation studies.

show abstract

A Comparison of Methods for Treatment Selection in Seamless Phase II/III Clinical Trials Incorporating Information on Short-Term Endpoints

Cited by 31 publications

References 35 publications

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

Blinded versus unblinded estimation of a correlation coefficient to inform interim design adaptations

Interim analysis incorporating short‐ and long‐term binary endpoints

Evaluating futility of a binary clinical endpoint using early read‐outs

Contact Info

Product

Resources

About