Estimation of Offspring Production from a Limited Number of Stage‐Structured Censuses

Bakke, Øyvind; Lorentsen, Svein‐Håkon

doi:10.1111/j.0006-341x.1999.00321.x

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…Group sequential designs1,2 introduced the idea of multiple analyses at fixed time points during its course, thus enabling early stopping when definitive evidence exists. Adaptive designs3–5 have added additional flexibility to the way in which mid-course data can be used to influence a study’s future scope. However, the above approaches still share a common characteristic: whilst it is ongoing, a fixed allocation probability is used for all arms still active in the trial.…”

Section: Introductionmentioning

confidence: 99%

Unbiased estimation for response adaptive clinical trials

Bowden

Trippa

2015

Stat Methods Med Res

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Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative – causing one’s estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao–Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature.

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Section: Introductionmentioning

confidence: 99%

Unbiased estimation for response adaptive clinical trials

Bowden

Trippa

2015

Stat Methods Med Res

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“…For example, the trial sample size (e.g. the number of participants in a trial), treatment dose or frequency, or the duration of the trial may be increased or decreased depending on the accumulated evidence [ 11 – 13 ].…”

Section: Introductionmentioning

confidence: 99%

Clinical Trial Adaptation by Matching Evidence in Complementary Patient Sub-groups of Auxiliary Blinding Questionnaire Responses

Arandjelović

2015

PLoS ONE

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Clinical trial adaptation refers to any adjustment of the trial protocol after the onset of the trial. Such adjustment may take on various forms, including the change in the dose of administered medicines, the frequency of administering an intervention, the number of trial participants, or the duration of the trial, to name just some possibilities. The main goal is to make the process of introducing new medical interventions to patients more efficient, either by reducing the cost or the time associated with evaluating their safety and efficacy. The principal challenge, which is an outstanding research problem, is to be found in the question of how adaptation should be performed so as to minimize the chance of distorting the outcome of the trial. In this paper we propose a novel method for achieving this. Unlike most of the previously published work, our approach focuses on trial adaptation by sample size adjustment i.e. by reducing the number of trial participants in a statistically informed manner. We adopt a stratification framework recently proposed for the analysis of trial outcomes in the presence of imperfect blinding and based on the administration of a generic auxiliary questionnaire that allows the participants to express their belief concerning the assigned intervention (treatment or control). We show that this data, together with the primary measured variables, can be used to make the probabilistically optimal choice of the particular sub-group a participant should be removed from if trial size reduction is desired. Extensive experiments on a series of simulated trials are used to illustrate the effectiveness of our method.

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“…This concept was considered by Box and Jenkins (1962) and studied in earlier literature report, including those on group sequential design (Amitage, 1958; Jennison and Turnbull, 1999; Stein, 1945) and adaptive randomization (Pocock and Simon, 1975; Zelen, 1969). Recently, there has been a great deal of renewed interest in this area, and numerous papers were published in sample size reestimation (Bauer and Köhne, 1994; Cui et al, 1999; Chow and Chang, 2007; Fisher, 1998; Liu and Chi, 2001; Proschan and Hunsberger, 1995), adaptive treatment selection (Li et al, 2009; Luo et al, 2010; Stallard and Friede, 2008), population enrichment (Wang et al, 2007) and covariate adjustment and response adaptive designs (Hu and Rosenberger, 2003; Zhang et al, 2007). A comprehensive publications can be found in the draft guidance on adaptive design recently published by the U.S. Food and Drug Administration (FDA).…”

Section: Introductionmentioning

confidence: 99%

“…The median unbiased estimates for two-stage adaptive designs are generalized to sequential median unbiased estimates for (adaptive) group sequential designs by Liu and Anderson (2008) following the repeated confidence interval approach of Jennison and Turnbull (1999). With sample size adjustments, these median unbiased estimates do not use sufficient statistics, which, along with combination tests of Cui et al (1999), is at the center of controversy with many existing adaptive design procedures today. In a single-arm setting with maximum selection design, Shen (2001) identified bias from naive estimate and Luo et al (2010) provided estimation based on conditional moments.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

Luo¹,

Li²,

Shih³

et al. 2012

Journal of Biopharmaceutical Statistics

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Parameter estimation following an adaptive design or group sequential design has been extremely challenging due to potential random high from its face value estimate. In this paper, we introduce a new framework to model clinical trial data flow based on a marked point process (MPP). The MPP model allows us to use methods of stochastic calculus for analyses of any adaptive clinical trial. As an example, we apply this method to a two stage treatment selection design and derive a procedure to estimate the treatment effect. Numerical examples will be used to evaluate the performance of the proposed procedure.

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Estimation of Offspring Production from a Limited Number of Stage‐Structured Censuses

Cited by 18 publications

References 12 publications

Unbiased estimation for response adaptive clinical trials

Unbiased estimation for response adaptive clinical trials

Clinical Trial Adaptation by Matching Evidence in Complementary Patient Sub-groups of Auxiliary Blinding Questionnaire Responses

Estimation of Treatment Effect Following a Clinical Trial with Adaptive Design

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