Power Calculations in Clinical Trials With Complex Clinical Objectives

Dmitrienko, Alex; Paux, Gautier; Brechenmacher, Thomas

doi:10.5183/jjscs.1411001_213

Cited by 5 publications

(6 citation statements)

References 32 publications

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“…The former is an example of expectation criteria and the latter is an example of exceedance criteria that are often considered when designing a study that has multiple clinical objectives. 16 We can find the expected values of these utilities by evaluating the probability of rejecting some number of hypotheses. For example, the probability of rejecting K − 2 hypothesis out of K hypotheses is the sum of (1) over all choices of r and u.…”

Section: Elements Of Decision-theoretic Frameworkmentioning

confidence: 99%

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To add or not to add a new treatment arm to a multiarm study: A decision‐theoretic framework

Lee

Wason

Stallard

2019

Statistics in Medicine

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Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on‐going clinical trials using a “platform” trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two‐stage trial setting, we propose a decision‐theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis.

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Section: Elements Of Decision-theoretic Frameworkmentioning

confidence: 99%

“…They are examples of design criteria for designing a trial that has multiple clinical objectives. 16 A utility function reflects the point of view of a decision marker. For ease of exposition, we do not include other trial aspects such as monetary values in our illustration.…”

Section: Introductionmentioning

confidence: 99%

To add or not to add a new treatment arm to a multiarm study: A decision‐theoretic framework

Lee

Wason

Stallard

2019

Statistics in Medicine

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“…20,21 Examples of optimal weights using grid search and simulation are discussed for graphical approaches 22 and chain procedures. 23,24 More recently, optimization using deep learning has been proposed to optimize the weighted power for the graphical approach. 25 Although the weighted (or average) power is easier to optimize numerically, it introduces another set of weights for power that may not be easy to determine or interpret.…”

Section: Introductionmentioning

confidence: 99%

“…26 For more hypotheses or more flexible approaches such as the graphical approach, simulation over a grid of possible-but-often-limited choices is adopted to search for the optimal solution. [22][23][24] In general, there are three challenges to optimize the disjunctive and conjunctive power. The first challenge is that evaluating these objective functions requires the knowledge of multivariate distributions and hence the correlation structure among test statistics.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, analytical formulae are derived for weighted Bonferroni tests to obtain the optimal average power 20,21 . Examples of optimal weights using grid search and simulation are discussed for graphical approaches 22 and chain procedures 23,24 . More recently, optimization using deep learning has been proposed to optimize the weighted power for the graphical approach 25 …”

Section: Introductionmentioning

confidence: 99%

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Optimal weighted Bonferroni tests and their graphical extensions

Xi,

Chen

2023

Statistics in Medicine

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Regulatory guidelines mandate the strong control of the familywise error rate in confirmatory clinical trials with primary and secondary objectives. Bonferroni tests are one of the popular choices for multiple comparison procedures and are building blocks of more advanced procedures. It is usually of interest to find the optimal weighted Bonferroni split for multiple hypotheses. We consider two popular quantities as the optimization objectives, which are the disjunctive power and the conjunctive power. The former is the probability to reject at least one false hypothesis and the latter is the probability to reject all false hypotheses. We investigate the behavior of each of them as a function of different Bonferroni splits, given assumptions about the alternative hypotheses and correlations between test statistics. Under independent tests, unique optimal Bonferroni weights exist; under dependence, optimal Bonferroni weights may not be unique based on a fine grid search. In general, we propose an optimization algorithm based on constrained nonlinear optimization and multiple starting points. The proposed algorithm efficiently identifies optimal Bonferroni weights to maximize the disjunctive or conjunctive power. In addition, we apply the proposed algorithm to graphical approaches, which include many Bonferroni‐based multiple comparison procedures. Utilizing the closed testing principle, we adopt a two‐step approach to find optimal graphs using the disjunctive power. We also identify a class of closed test procedures that optimize the conjunctive power. We apply the proposed algorithm to a case study to illustrate the utility of optimal graphical approaches that reflect study objectives.

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