Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

Luo, Xiaolong; Li, Mingyu; Shih, Weichung Joe; Ouyang, Peter

doi:10.1080/10543406.2012.676534

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Luo et al 118 used the method of conditional moments to derive a bias-adjusted estimator of the response rate following a two-stage drop-the-loser design with a binary endpoint. Adopting a stochastic process framework (as opposed to the conventional random variable viewpoint) for clinical trials with adaptive elements, Luo et al 119 proposed approximating treatment effects based on a general estimating equation to match the first conditional moment. This generic approach can be used for a variety of adaptive designs with endpoints following any probability distribution.…”

Section: Bias-reduced Estimationmentioning

confidence: 99%

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Point estimation for adaptive trial designs I: A methodological review

Robertson

Choodari‐Oskooei

Dimairo

et al. 2022

Statistics in Medicine

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In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This paper is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realisation they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy pre-specified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.

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Section: Bias-reduced Estimationmentioning

confidence: 99%

“…Two‐stage designs : Coad, 116 Shen, 117 Stallard et al, 118 Pepe et al, 47 Luo et al, 119,120 Bebu et al, 121,122 Koopmeiners et al, 107 Brückner et al 123 …”

Section: Comparisons Of Estimators Trial Examples and Softwarementioning

confidence: 99%

Point estimation for adaptive trial designs I: A methodological review

Robertson

Choodari‐Oskooei

Dimairo

et al. 2022

Statistics in Medicine

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“…In contrast to the methods introduced so far, the conditional moment estimator (CME) proposed by Luo et al in the context of treatment selection is given by the implicit solution of an estimation equation. The basic idea is to exploit the fact that the conditional expectation of X g given the observed interim decision and the observed interim outcome of X ¬ g is a function in the unknown parameter Δ g .…”

Section: Alternative Estimation Of Treatment Effectmentioning

confidence: 99%

Point estimation in adaptive enrichment designs

Kunzmann

Benner

Kieser

2017

Statistics in Medicine

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Adaptive enrichment designs are an attractive option for clinical trials that aim at demonstrating efficacy of therapies, which may show different benefit for the full patient population and a prespecified subgroup. In these designs, based on interim data, either the subgroup or the full population is selected for further exploration. When selection is based on efficacy data, this introduces bias to the commonly used maximum likelihood estimator. For the situation of two-stage designs with a single prespecified subgroup, we present six alternative estimators and investigate their performance in a simulation study. The most consistent reduction of bias over the range of scenarios considered was achieved by a method combining the uniformly minimum variance conditionally unbiased estimator with a conditional moment estimator. Application of the methods is illustrated by a clinical trial example.

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“…However, most of the estimation methods for GSD are not applicable to adaptive designs. Unfortunately, the literature on estimation in adaptive GSD is mainly on Phase III clinical trials [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] .…”

Section: Introductionmentioning

confidence: 99%

“…Unfortunately, the literature on estimation in adaptive GSD is mainly on Phase III clinical trials. [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] To the best of our knowledge, estimation in oncology Phase II adaptive single-arm GSD with a binary endpoint has only been discussed recently by Kunzmann and Kieser, 36 who proposed a point estimator that can be interpreted as a constrained posterior mean estimate based on the non-informative Jeffreys prior. Their method is computationally intensive, and they have implemented it in the Julia 37 programming language using the JuMP 38 package and the Julia interface 38 to the commercial solver Gurobi.…”

Section: Introductionmentioning

confidence: 99%

Interval and point estimation in adaptive Phase II trials with binary endpoint

Nhacolo

Brannath

2018

Stat Methods Med Res

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Phase II clinical trials are concerned with making decision of whether a treatment is sufficiently efficacious to be worth further investigations in late large scale Phase III trials. In oncology Phase II trials, frequentist single-arm two-stage group-sequential designs with a binary endpoint are commonly used. To allow for more flexibility, adaptive versions of these designs have been proposed. In this paper, we propose point and interval estimation for adaptive designs in which the second stage sample size is a pre-specified function of first stage's number of responses. Our approach is based on sample space orderings, from which we derive p-values, and point and interval estimates. Simulation studies show that our proposed methods perform better, in terms of bias and root mean square error, than the fixed-sample maximum likelihood estimator.

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Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

Cited by 6 publications

References 26 publications

Point estimation for adaptive trial designs I: A methodological review

Point estimation for adaptive trial designs I: A methodological review

Point estimation in adaptive enrichment designs

Interval and point estimation in adaptive Phase II trials with binary endpoint

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