Optimal adaptive sequential designs for crossover bioequivalence studies

Xu, Jialin; Audet, Charles; DiLiberti, Charles E.; Montague, Timothy H.; Parr, Alan; Potvin, Diane; Schuirmann, Donald J.

doi:10.1002/pst.1721

Cited by 23 publications

(39 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main features that distinguish the adaptive SSR and testing methods presented in this paper from methods that demonstrate type I error control via simulations (eg, as in Potvin et al and Xu et al) is the use of a novel combination test that is robust against possibly wrong assumptions regarding the true variance. We have proposed an SSR method that is based on conditional power given the results of the first stage combined with an exact method to compute the sample size of the second stage to achieve the required power.…”

Section: Discussionmentioning

confidence: 99%

“…The performance of the maximum combination test, together with the proposed conditional power approach for SSR, is about the same as for Method B if the increase in sample size is moderate. The targeted power is achieved over a wide range of true variances, especially when a futility rule like the one proposed by Xu et al is applied. As done here, the futility rule can be applied in a nonbinding manner, ie, it can be used as guidance but must not necessarily be followed.…”

Section: Discussionmentioning

confidence: 99%

“…In other words, we are stopping trials when the estimated ratio at the interim is some distance from 1, the value for exact ABE. This is a variation of the futility rule suggested by Xu et al. The addition of any futility rule can only decrease the type I error rate.…”

Section: Flow Diagram Of Proposed Ssr Algorithmmentioning

confidence: 99%

“…We noted earlier that our additional futility rule was a variation of one proposed by Xu et al, in their extension of the methods described in Potvin et al Their futility rule is based on an optimization algorithm that depends on the size of the assumed CV and which of their Methods E and F is used (see Xu et al for details). We note that, for the same reasons that apply to Potvin et al, neither of Methods E and F can guarantee control of the type I error rate in all situations.…”

Section: Operating Characteristics Of the New Ssr Algorithmmentioning

confidence: 99%

“…We note that, for the same reasons that apply to Potvin et al, neither of Methods E and F can guarantee control of the type I error rate in all situations. That they have properties acceptable to the authors of Xu et al is only the result of an extensive simulation exercise.…”

Section: Operating Characteristics Of the New Ssr Algorithmmentioning

confidence: 99%

See 4 more Smart Citations

Controlling the type I error rate in two‐stage sequential adaptive designs when testing for average bioequivalence

Maurer

Jones

Ying

2018

Statistics in Medicine

View full text Add to dashboard Cite

In a 2×2 crossover trial for establishing average bioequivalence (ABE) of a generic agent and a currently marketed drug, the recommended approach to hypothesis testing is the two one-sided test (TOST) procedure, which depends, among other things, on the estimated within-subject variability. The power of this procedure, and therefore the sample size required to achieve a minimum power, depends on having a good estimate of this variability. When there is uncertainty, it is advisable to plan the design in two stages, with an interim sample size reestimation after the first stage, using an interim estimate of the within-subject variability. One method and 3 variations of doing this were proposed by Potvin et al. Using simulation, the operating characteristics, including the empirical type I error rate, of the 4 variations (called Methods A, B, C, and D) were assessed by Potvin et al and Methods B and C were recommended. However, none of these 4 variations formally controls the type I error rate of falsely claiming ABE, even though the amount of inflation produced by Method C was considered acceptable. A major disadvantage of assessing type I error rate inflation using simulation is that unless all possible scenarios for the intended design and analysis are investigated, it is impossible to be sure that the type I error rate is controlled. Here, we propose an alternative, principled method of sample size reestimation that is guaranteed to control the type I error rate at any given significance level. This method uses a new version of the inverse-normal combination of p-values test, in conjunction with standard group sequential techniques, that is more robust to large deviations in initial assumptions regarding the variability of the pharmacokinetic endpoints. The sample size reestimation step is based on significance levels and power requirements that are conditional on the first-stage results. This necessitates a discussion and exploitation of the peculiar properties of the power curve of the TOST testing procedure. We illustrate our approach with an example based on a real ABE study and compare the operating characteristics of our proposed method with those of Method B of Povin et al.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Flow Diagram Of Proposed Ssr Algorithmmentioning

confidence: 99%

Section: Operating Characteristics Of the New Ssr Algorithmmentioning

confidence: 99%

Section: Operating Characteristics Of the New Ssr Algorithmmentioning

confidence: 99%

See 3 more Smart Citations

Controlling the type I error rate in two‐stage sequential adaptive designs when testing for average bioequivalence

Maurer

Jones

Ying

2018

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

Blinded and unblinded sample size reestimation in crossover trials balanced for period

2018

View full text Add to dashboard Cite

The determination of the sample size required by a crossover trial typically depends on the specification of one or more variance components. Uncertainty about the value of these parameters at the design stage means that there is often a risk a trial may be under‐ or overpowered. For many study designs, this problem has been addressed by considering adaptive design methodology that allows for the re‐estimation of the required sample size during a trial. Here, we propose and compare several approaches for this in multitreatment crossover trials. Specifically, regulators favor reestimation procedures to maintain the blinding of the treatment allocations. We therefore develop blinded estimators for the within and between person variances, following simple or block randomization. We demonstrate that, provided an equal number of patients are allocated to sequences that are balanced for period, the proposed estimators following block randomization are unbiased. We further provide a formula for the bias of the estimators following simple randomization. The performance of these procedures, along with that of an unblinded approach, is then examined utilizing three motivating examples, including one based on a recently completed four‐treatment four‐period crossover trial. Simulation results show that the performance of the proposed blinded procedures is in many cases similar to that of the unblinded approach, and thus they are an attractive alternative.

show abstract

An iterative method to protect the type I error rate in bioequivalence studies under two‐stage adaptive 2×2 crossover designs

Molins

Labes²,

Schütz³

et al. 2020

Biometrical J

View full text Add to dashboard Cite

Bioequivalence studies are the pivotal clinical trials submitted to regulatory agencies to support the marketing applications of generic drug products. Average bioequivalence (ABE) is used to determine whether the mean values for the pharmacokinetic measures determined after administration of the test and reference products are comparable. Two‐stage 2×2 crossover adaptive designs (TSDs) are becoming increasingly popular because they allow making assumptions on the clinically meaningful treatment effect and a reliable guess for the unknown within‐subject variability. At an interim look, if ABE is not declared with an initial sample size, they allow to increase it depending on the estimated variability and to enroll additional subjects at a second stage, or to stop for futility in case of poor likelihood of bioequivalence. This is crucial because both parameters must clearly be prespecified in protocols, and the strategy agreed with regulatory agencies in advance with emphasis on controlling the overall type I error.We present an iterative method to adjust the significance levels at each stage which preserves the overall type I error for a wide set of scenarios which should include the true unknown variability value. Simulations showed adjusted significance levels higher than 0.0300 in most cases with type I error always below 5%, and with a power of at least 80%. TSDs work particularly well for coefficients of variation below 0.3 which are especially useful due to the balance between the power and the percentage of studies proceeding to stage 2. Our approach might support discussions with regulatory agencies.

show abstract

Optimal adaptive sequential designs for crossover bioequivalence studies

Cited by 23 publications

References 16 publications

Controlling the type I error rate in two‐stage sequential adaptive designs when testing for average bioequivalence

Controlling the type I error rate in two‐stage sequential adaptive designs when testing for average bioequivalence

Blinded and unblinded sample size reestimation in crossover trials balanced for period

An iterative method to protect the type I error rate in bioequivalence studies under two‐stage adaptive 2×2 crossover designs

Contact Info

Product

Resources

About