Selection of the treatment effect for sample size determination in a superiority clinical trial using a hybrid classical and Bayesian procedure

Ciarleglio, Maria; Arendt, Christopher D.; Makuch, Robert W.; Peduzzi, Peter

doi:10.1016/j.cct.2015.01.002

Cited by 11 publications

(12 citation statements)

References 29 publications

(47 reference statements)

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“…This result corresponds closely to the values of c reported for ( ν θ = 1 . 9 , ν σ = 2 . 7 ) in Tables 2 and A.2, although for the example μ θ ≠ μ σ . If one were to assume no uncertainty in σ (i.e., assume σ is known), 5 CEP evaluates to 70.2%. In this case, the value of θ A that provides 80% CEP is 4.103.…”

Section: Examplesupporting

confidence: 84%

“…CEP is an alternative to traditional power that uses the distribution of prior beliefs about the unknown parameters contained in their prior distributions. When σ is known, CEP is given by averaging the traditional power curve with respect to the conditional prior distribution for θ , p ( θ | θ > 0 ) , 5,7 or…”

Section: Methodsmentioning

confidence: 99%

“…The difficulty surrounding the choice of θ A has been discussed previously. 5 In Ciarleglio et al, 5 we present a method for formally using prior information on both the magnitude and the uncertainty in the treatment effect θ to choose a value of θ A when σ is assumed known or when a separate parameter does not need to be specified (e.g., θ = log hazard ratio). Here, we extend that method for a continuous endpoint to the case when there is uncertainty in both θ and σ .…”

Section: Introductionmentioning

confidence: 99%

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Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure

2016

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Through this method, we are able to formally integrate prior information on the uncertainty and variability of both the treatment effect and the common standard deviation into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the effect size which the study has a high probability of correctly detecting based on the available prior information on the difference [Formula: see text] and the standard deviation [Formula: see text] provides a valuable, substantiated estimate that can form the basis for discussion about the study's feasibility during the design phase.

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Section: Examplesupporting

confidence: 84%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure

2016

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“…1,2 Although traditional power calculations require the selection of specific values of θ and σ2, there is often uncertainty in these hypothesized parameters and, thus, a distribution of plausible values that should be considered when determining sample size. We have previously presented methods that formally incorporate available prior information on the distribution of study design parameters into pre-trial sample size determination 3–5 using a “hybrid classical and Bayesian” 6 technique based on conditional expected power (CEP). 7 CEP is a measure of prior-adjusted power that accounts for the uncertainty in the study parameters by averaging the traditional power curve using the conditional prior distribution of θ and the prior distribution of σ2 as the averaging weight.…”

Section: Introductionmentioning

confidence: 99%

Sample size re-estimation in a superiority clinical trial using a hybrid classical and Bayesian procedure

Ciarleglio

Arendt²

2018

Stat Methods Med Res

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When designing studies involving a continuous endpoint, the hypothesized difference in means ([Formula: see text]) and the assumed variability of the endpoint ([Formula: see text]) play an important role in sample size and power calculations. Traditional methods of sample size re-estimation often update one or both of these parameters using statistics observed from an internal pilot study. However, the uncertainty in these estimates is rarely addressed. We propose a hybrid classical and Bayesian method to formally integrate prior beliefs about the study parameters and the results observed from an internal pilot study into the sample size re-estimation of a two-stage study design. The proposed method is based on a measure of power called conditional expected power (CEP), which averages the traditional power curve using the prior distributions of θ and [Formula: see text] as the averaging weight, conditional on the presence of a positive treatment effect. The proposed sample size re-estimation procedure finds the second stage per-group sample size necessary to achieve the desired level of conditional expected interim power, an updated CEP calculation that conditions on the observed first-stage results. The CEP re-estimation method retains the assumption that the parameters are not known with certainty at an interim point in the trial. Notional scenarios are evaluated to compare the behavior of the proposed method of sample size re-estimation to three traditional methods.

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“…Similar difficulty surrounding the choice of study parameters for a continuous endpoint with known variance [ 5 ] and for a continuous endpoint with unknown variance [ 6 ] has been discussed previously. We have presented methods that formally incorporate the distribution of prior information on both the treatment effect and the variability of the endpoint into sample size determination.…”

Section: Introductionmentioning

confidence: 99%

Sample size determination for a binary response in a superiority clinical trial using a hybrid classical and Bayesian procedure

Ciarleglio

Arendt

2017

Trials

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BackgroundWhen designing studies that have a binary outcome as the primary endpoint, the hypothesized proportion of patients in each population experiencing the endpoint of interest (i.e., π 1,π 2) plays an important role in sample size and power calculations. Point estimates for π 1 and π 2 are often calculated using historical data. However, the uncertainty in these estimates is rarely addressed.MethodsThis paper presents a hybrid classical and Bayesian procedure that formally integrates prior information on the distributions of π 1 and π 2 into the study’s power calculation. Conditional expected power (CEP), which averages the traditional power curve using the prior distributions of π 1 and π 2 as the averaging weight conditional on the presence of a positive treatment effect (i.e., π 2>π 1), is used, and the sample size is found that equates the pre-specified frequentist power (1−β) and the conditional expected power of the trial.ResultsNotional scenarios are evaluated to compare the probability of achieving a target value of power with a trial design based on traditional power and a design based on CEP. We show that if there is uncertainty in the study parameters and a distribution of plausible values for π 1 and π 2, the performance of the CEP design is more consistent and robust than traditional designs based on point estimates for the study parameters. Traditional sample size calculations based on point estimates for the hypothesized study parameters tend to underestimate the required sample size needed to account for the uncertainty in the parameters. The greatest marginal benefit of the proposed method is achieved when the uncertainty in the parameters is not large.ConclusionsThrough this procedure, we are able to formally integrate prior information on the uncertainty and variability of the study parameters into the design of the study while maintaining a frequentist framework for the final analysis. Solving for the sample size that is necessary to achieve a high level of CEP given the available prior information helps protect against misspecification of hypothesized treatment effect and provides a substantiated estimate that forms the basis for discussion about the study’s feasibility during the design phase.

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Selection of the treatment effect for sample size determination in a superiority clinical trial using a hybrid classical and Bayesian procedure

Cited by 11 publications

References 29 publications

Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure

Selection of the effect size for sample size determination for a continuous response in a superiority clinical trial using a hybrid classical and Bayesian procedure

Sample size re-estimation in a superiority clinical trial using a hybrid classical and Bayesian procedure

Sample size determination for a binary response in a superiority clinical trial using a hybrid classical and Bayesian procedure

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