Incorporating intermediate binary responses into interim analyses of clinical trials: A comparison of four methods

Whitehead, Anne; Sooriyarachchi, M. R.; Whitehead, John; Bolland, Kim

doi:10.1002/sim.3046

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…in terms of statistical power) on the phase III outcomes at interim looks is then improved by utilizing the information contained in the additionally available phase II outcomes. Outside the context of treatment selection, similar strategies have been pursued to increase information about phase III type outcomes 16–18.…”

Section: Introductionmentioning

confidence: 99%

Designing a seamless phase II/III clinical trial using early outcomes for treatment selection: An application in multiple sclerosis

Friede

Parsons

Stallard

et al. 2011

Statistics in Medicine

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In recent years adaptive seamless phase II/III designs (ASDs) allowing treatment or dose selection at an interim analysis have gained much attention because of their potential to save development costs and to shorten time-to-market of a new compound compared to conventional drug development programmes with separate trials for individual phases. In this paper, we describe an ASD with treatment selection based on early outcome data, specifically considering the situation where no final outcomes are observed at the time of the interim analysis. Bringing together combination tests for adaptive designs and the closure principle for multiple testing, control of the familywise type I error rate in the strong sense is achieved. Furthermore, a simulation model is proposed based on standardized test statistics that allows the generation of virtual trials for a variety of outcomes. We use this simulation model to investigate the actual type I error rate of the proposed testing procedure and find that the familywise type I error rate is controlled as expected. The method is often conservative, with the degree of conservatism depending on the correlation between early and late outcome, the true mean values of the early outcome in the different treatment groups and the selection rule. The investigations are motivated and illustrated by an application of the proposed design and simulation model to progressive multiple sclerosis.

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Section: Introductionmentioning

confidence: 99%

Designing a seamless phase II/III clinical trial using early outcomes for treatment selection: An application in multiple sclerosis

Friede

Parsons

Stallard

et al. 2011

Statistics in Medicine

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show abstract

“…The desire to do this arises when the primary endpoint of interest for each patient is only available after a number of months or even years and yet there are more immediately measured endpoints available, building on earlier work on incorporation of early endpoints in sequential clinical trials comparing a single experimental treatment with a control (Cook and Farewell, 1996;Marschner and Becker, 2001;Galbraith and Marschner, 2003;Sooriyarachchi et al, 2006;Whitehead et al, 2008). An example can be found in secondary progressive multiple sclerosis, where long-term changes in disability scales are the main goal, but early evidence of treatment effect may be observed as changes to lesions in the brain detected using magnetic resonance imaging scanning technology.…”

Section: Introductionmentioning

confidence: 99%

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

Kunz

Friede

Parsons

et al. 2015

Journal of Biopharmaceutical Statistics

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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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“…The C language was selected since it is efficient in doing large scale simulations. The first step of the simulation study was to set some practically plausible values for the parameters required (Sooriyarachchi & Whitehead, 1998 ;Whitehead et al, 2008). Usually crossover trials are associated with a small sample size, due to comparison of treatments being within patient rather than between patient, and variances within patient being usually smaller than the between variances.…”

Section: Then Define the Following Notationmentioning

confidence: 99%

Guidelines for calculating sample size in 2x2 crossover trials : a simulation study

Siyasinghe

Sooriyarachchi

2011

J. Natn. Sci. Foundation Sri Lanka

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Abstract:In crossover trials, patients receive two or more treatments in a random order in different periods. The sample size determination is often an important step in planning a crossover study. This paper concerns sample size calculations in 2x2 crossover trials, with random patient effects and no interaction between the treatment and the patient under two scenarios, namely the exact and the large sample size approaches. Simulation was carried out for determining the sample size for both scenarios. For varying parameter values, simulation was used for generating samples of the required size and examining whether the significance level and power of the tests are maintained. The results indicate that when the sample size was ≤ 5, neither method maintained error rates and when the sample size was >5 and < 12 only the exact approach maintained error rates. However, when the sample size is approximately > 12 both methods maintained error rates. In addition it was found that a saving in sample size can be achieved depending on the extent of the correlation between the observations on the same patient. The simulation results indicate that crossover studies should not be conducted when the anticipated sample size is ≤ 5 and when a sample size of >5 and < 12 is anticipated, the exact method of determining sample size should be used. When larger sample sizes are anticipated either method can be used but the method based on large sample size approximation is simpler.

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Incorporating intermediate binary responses into interim analyses of clinical trials: A comparison of four methods

Cited by 7 publications

References 13 publications

Designing a seamless phase II/III clinical trial using early outcomes for treatment selection: An application in multiple sclerosis

Designing a seamless phase II/III clinical trial using early outcomes for treatment selection: An application in multiple sclerosis

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

Guidelines for calculating sample size in 2x2 crossover trials : a simulation study

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