Including long- and short-term data in blinded sample size recalculation for binary endpoints

Wüst, Kirsten; Kieser, Meinhard

doi:10.1016/j.csda.2004.04.006

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…The approach makes use only of the fact that the short-and long-term outcome may be correlated but does not assume that the association can be characterised by a specific model. The method was given by Wçst and Kieser (2003) who also proposed a related procedure for binary endpoints (Wçst and Kieser, 2005). Maschner and Becker (2001) and Galbraith and Maschner (2003) described an analogous approach for interim analyses in group sequential trials.…”

Section: Including Short-term Datamentioning

confidence: 99%

Sample Size Recalculation in Internal Pilot Study Designs: A Review

2006

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The adequacy of sample size is important to clinical trials. In the planning phase of a trial, however, the investigators are often quite uncertain about the sizes of parameters which are needed for sample size calculations. A solution to this problem is mid-course recalculation of the sample size during the ongoing trial. In internal pilot study designs, nuisance parameters are estimated on the basis of interim data and the sample size is adjusted accordingly. This review attempts to give an overview on the available methods. It is written not only for biometricians who are already familar with the the topic and wish to update their knowledge but also for users new to the subject.

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Section: Including Short-term Datamentioning

confidence: 99%

Sample Size Recalculation in Internal Pilot Study Designs: A Review

2006

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“…So for practical purposes, the internal pilot study would need to consist of a fairly small proportion of the initial target sample size. Wçst and Kieser (2005) proposed a procedure for blinded sample size reestimation adopting an idea by Marschner and Becker (2001) that utilizes early readouts of the endpoints and their correlation with the final readout.…”

Section: Discussionmentioning

confidence: 99%

Blinded Sample Size Reestimation in Non‐Inferiority Trials with Binary Endpoints

2007

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Sample size calculations in the planning of clinical trials depend on good estimates of the model parameters involved. When the estimates of these parameters have a high degree of uncertainty attached to them, it is advantageous to reestimate the sample size after an internal pilot study. For non-inferiority trials with binary outcome we compare the performance of Type I error rate and power between fixed-size designs and designs with sample size reestimation. The latter design shows itself to be effective in correcting sample size and power of the tests when misspecification of nuisance parameters occurs with the former design.

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“…Kunz, Wason, and Kieser () applied the estimator to single‐arm phase II oncology trials. Blinded sample size reassessment techniques were discussed by Wüst and Kieser (). Whitehead, Sooriyarachchi, Whitehead, and Bolland () compared four methods for incorporating intermediate binary responses into interim analyses for group sequential trials using score and Wald approaches.…”

Section: Introductionmentioning

confidence: 99%

Interim analysis incorporating short‐ and long‐term binary endpoints

2019

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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.

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Including long- and short-term data in blinded sample size recalculation for binary endpoints

Cited by 7 publications

References 9 publications

Sample Size Recalculation in Internal Pilot Study Designs: A Review

Sample Size Recalculation in Internal Pilot Study Designs: A Review

Blinded Sample Size Reestimation in Non‐Inferiority Trials with Binary Endpoints

Interim analysis incorporating short‐ and long‐term binary endpoints

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