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ISCB2014_abstract_book

56 ISCB 2014 Vienna, Austria • Abstracts - Oral PresentationsTuesday, 26th August 2014 • 11:00-12:30 Monday25thAugustTuesday26thAugustThursday28thAugustAuthorIndexPostersWednesday27thAugustSunday24thAugust C30.2 A two-stage adaptive design for small clinical trials S Nikolakopoulos1 , KCB Roes1 , I van der Tweel1 , C Jennison2 1 UMC Utrecht, Utrecht, The Netherlands, 2 University of Bath, Bath, United Kingdom   Patients suffering from rare conditions should be entitled to the same quality of treatment as other patients. Clinical trials for orphan drugs are conducted with moderate to very small sample sizes since the pool of pos- sible study participants is by definition small. In addition, such trials may be conducted with little to no prior information on expected effects and their variation, thus making the assumptions made at the design stage prone to misjudgment. Therefore, flexible designs that have the ability to respond to information gathered within the trial seem an appealing alter- native to traditional trial designs.We propose a two-stage adaptive design for the situation where the total sample size has an assumed maximum which cannot be exceeded. In combination with the lack of information about the parameters of interest at the design stage, standard sample size calculations fall short in such cases. To optimize the trade-off between sample units and information gathered, a utility-based approach is sug- gested. In such a way, success of the clinical trial is taken under consider- ation as a frequentist concept, as it is most of the times required by drug regulators. In addition, utilities are defined for the treatment effect to be detected on the patient level, as well for the cost of sampling. C30.3 Adaptive designs for time-to-event trials D Magirr1 , T Jaki1 , F König2 , M Posch2 1 Lancaster University, Lancaster, United Kingdom, 2 Medical University of Vienna, Vienna, Austria   Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the im- portant case of time-to-event endpoints is not easily handled by the stan- dard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event.We show that the final test statistic may ignore a substantial subset of the observed event times. Since it is the data corresponding to the earliest recruited patients that is ignored, this neglect becomes egre- gious when there is specific interest in learning about long-term survival. An alternative test incorporating all event times is proposed, where a con- servative assumption is made in order to guarantee type I error control. We examine the properties of our proposed approach using the example of a clinical trial comparing two cancer therapies. C30.4 Adaptive designs for two candidate primary time-to-event endpoints G Rauch1 , M Kieser1 1 University of Heidelberg, Heidelberg, Germany   Composite endpoints combine several time-to-event variables of interest within a single time-to-first-event analysis. The motivation for the use of a composite endpoint is to increase power by increasing the number of expected events. However, in some situations a particular component that was exclusively added to the composite in order to increase the effect in fact decreased the composite effect. The CAPRICORN Trial [1] is a very illustrative exam- ple for this situation. Another possible scenario would be that the main component which is the most relevant for the patient (e.g. time-to-death) shows a higher effect than originally anticipated. In this situation it might be feasible to base sample size recalculation on the main component in order to improve the interpretation of the trial. In both situations, an adaptive design that allows sample size recalcula- tion during the interim analysis based on the larger observed effect of two candidate endpoints would be helpful. We propose different adaptive design strategies to face the above prob- lems and evaluate and compare them in terms of power and type I error using Monte-Carlo simulations. Applications are illustrated by a clinical study example. References: [1] The CAPRICORN Investigators. Effect of carvedilol on outcome after myocardial infarction inpatients with left-ventricular dysfunction: the CAPRICORN randomised trial. Lancet 2001; 357: 1385-1390. C30.5 Backward image confidence intervals for adaptive group sequential trials S Solanki1 , N Deshpande1 1 Cytel Statistical Software and Services Pvt. Ltd., Pune, India   An adaptive trial is defined as any clinical trial which uses accumulating data, possibly combined with external information, to modify aspects of the design without undermining the validity and integrity of the trial. Müller and Schäfer provided a methodology for conducting an adaptive trial which guaranteed control of type 1 error while providing maximum flexibility. However, corresponding solutions for the equally important and related problem of parameter estimation at the end of the adaptive trial have not been completely satisfactory. In their paper “Exact inference for adaptive group sequential designs” (April 2013), Ping Gao, Lingyun Liu and Cyrus Mehta introduce a method called Backward Image Confidence Intervals (BWCI) which is based on mapping the final test statistic obtained in the modified trial into a corresponding backward image in the original trial. It computes a two-sided confidence interval having exact coverage, along with a point estimate that is median unbiased for the primary efficacy pa- rameter in a two-arm adaptive group sequential design. This method will be discussed here with the help of several examples. Along with it, we will discuss advantages of this procedure over previously available methods, which either produced conservative coverage or no point estimates or provided exact coverage for one-sided intervals only. We will use simulation results generated by Cytel’s software East® for this purpose.

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