Exact inference for adaptive group sequential designs

Gao, Ping; Liu, Lingyun; Mehta, Cyrus R.

doi:10.1002/sim.5847

Cited by 32 publications

(31 citation statements)

References 13 publications

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“…Bias reduction methods were investigated by Posch et al for stage‐wise MAMS designs with dose selection but no sample size adaptation. For two‐arm group sequential designs with adaptive sample size reestimation, methods have been developed by Gao et al, Brannath et al, and Mehta et al There has been some recent work on unbiased point estimates in phase 2‐3 trials by Bowden and Glimm, Robertson et al, and Stallard and Kimani . Magirr et al have proposed simultaneous confidence intervals that are compatible with closed testing in adaptive designs.…”

Section: Discussionmentioning

confidence: 99%

Adaptive multiarm multistage clinical trials

Ghosh

Liu

Mehta

2020

Statistics in Medicine

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Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage‐by‐stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family‐wise error rate. The stage‐wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2‐3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P‐value. These stage‐wise P‐values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two‐arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage‐wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage‐wise MAMS designs.

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Section: Discussionmentioning

confidence: 99%

Adaptive multiarm multistage clinical trials

Ghosh

Liu

Mehta

2020

Statistics in Medicine

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“…Methods for evaluation of sensitivity to informative dropouts is another potential topic. Gao et al [19], Emerson and Fleming [20], and Kim [21] introduced methods for unbiased estimation following sequential testing, and these methods could be incorporated when reporting results from any group sequential trial.…”

Section: Discussionmentioning

confidence: 99%

Information‐based sample size re‐estimation in group sequential design for longitudinal trials

Zhou

Adewale

Shentu

et al. 2014

Statistics in Medicine

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Group sequential design has become more popular in clinical trials because it allows for trials to stop early for futility or efficacy to save time and resources. However, this approach is less well-known for longitudinal analysis. We have observed repeated cases of studies with longitudinal data where there is an interest in early stopping for a lack of treatment effect or in adapting sample size to correct for inappropriate variance assumptions. We propose an information-based group sequential design as a method to deal with both of these issues. Updating the sample size at each interim analysis makes it possible to maintain the target power while controlling the type I error rate. We will illustrate our strategy with examples and simulations and compare the results with those obtained using fixed design and group sequential design without sample size re-estimation.

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“…Outcomes are ordered according to the value of the signed likelihood ratio statistic for testing against a particular hypothesized parameter value

θ^{'}

\begin{matrix} right (j^{'}, t^{'}, k^{'}) ≻_{θ^{'}} (j, t, k) & center if & left sign (t^{'} - θ^{'}) \frac{p_{M, T, K} (j^{'}, t^{'}, k^{'}; θ = t^{'})}{p_{M, T, K} (j^{'}, t^{'}, k^{'}; θ = θ^{'})} \\ right & center > & left sign (t - θ^{'}) \frac{p_{M, T, K} (j, t, k; θ = t)}{p_{M, T, K} (j, t, k; θ = θ^{'})} . \end{matrix}

This ordering simplifies to:

false(j^{'}, t^{'}, k^{'} false) ≻_{θ^{'}} false(j, t, k false) if \sqrt{n_{j^{'}}^{k^{'}}} false(t^{'} - θ^{'} false) > \sqrt{n_{j}^{k}} false(t - θ^{'} false) .

Note that there is a different likelihood ratio ordering for each hypothesized value of the parameter of interest. Conditional error ordering (BMP). This approach was defined by Gao et al () to compute one‐sided CIs, and then generalized to two‐sided intervals under a different framework by Brannath et al (). Analysis time ordering‐based CIs are extended to the adaptive setting by inverting adaptive hypothesis tests based on preserving the conditional type I error rate.…”

Section: Complete Inference After An Adaptive Hypothesis Testmentioning

confidence: 99%

“…• Conditional error ordering (BMP). This approach was defined by Brannath et al (2009) to compute one-sided CIs, and then generalized to two-sided intervals under a different framework by Gao et al (2013). Analysis time ordering-based CIs are extended to the adaptive setting by inverting adaptive hypothesis tests based on preserving the conditional type I error rate.…”

Section: Exact Confidence Sets and Orderings Of The Outcome Spacementioning

confidence: 99%

“…A single repeated CI is only guaranteed to provide conservative coverage. Brannath, Mehta, and Posch (2009) and Gao, Liu, and Mehta (2013) extended analysis time ordering-based confidence intervals to the adaptive setting by inverting adaptive hypothesis tests based on preserving the conditional type I error rate (Müller and Schäfer, 2001). Liu and Anderson (2008) introduced a general family of orderings of the outcome space for an adaptive test analogous to the family of orderings discussed by Emerson and Fleming (1990) for a group sequential test statistic.…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of inferential procedures for adaptive clinical trial designs with pre‐specified rules for modifying the sample size

Levin

Emerson

2014

Biometrics

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SUMMARYMany papers have introduced adaptive clinical trial methods that allow modifications to the sample size based on interim estimates of treatment effect. There has been extensive commentary on type I error control and efficiency considerations, but little research on estimation after an adaptive hypothesis test. We evaluate the reliability and precision of different inferential procedures in the presence of an adaptive design with prespecified rules for modifying the sampling plan. We extend group sequential orderings of the outcome space based on the stage at stopping, likelihood ratio test statistic, and sample mean to the adaptive setting in order to compute median-unbiased point estimates, exact confidence intervals, and P-values uniformly distributed under the null hypothesis. The likelihood ratio ordering is found to average shorter confidence intervals and produce higher probabilities of P-values below important thresholds than alternative approaches. The bias adjusted mean demonstrates the lowest mean squared error among candidate point estimates. A conditional error-based approach in the literature has the benefit of being the only method that accommodates unplanned adaptations. We compare the performance of this and other methods in order to quantify the cost of failing to plan ahead in settings where adaptations could realistically be pre-specified at the design stage. We find the cost to be meaningful for all designs and treatment effects considered, and to be substantial for designs frequently proposed in the literature.

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Exact inference for adaptive group sequential designs

Cited by 32 publications

References 13 publications

Adaptive multiarm multistage clinical trials

Adaptive multiarm multistage clinical trials

Information‐based sample size re‐estimation in group sequential design for longitudinal trials

An evaluation of inferential procedures for adaptive clinical trial designs with pre‐specified rules for modifying the sample size

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