
Designing efficient randomized trials: power and sample size calculation when using semiparametric efficient estimators
Trials enroll a large number of subjects in order to attain power, makin...
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Sample size derivation for composite binary endpoints
Composite binary endpoints are increasingly used as primary endpoints in...
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The DURATIONS randomised trial design: estimation targets, analysis methods and operating characteristics
Background. Designing trials to reduce treatment duration is important i...
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Sample Size Calculation for ActiveArm Trial with Counterfactual Incidence Based on Recency Assay
The past decade has seen tremendous progress in the development of biome...
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Rethinking noninferiority: a practical trial design for optimising treatment duration
Background: trials to identify the minimal effective treatment duration ...
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Conditional Power and Friends: The Why and How of (Un)planned, Unblinded Sample Size Recalculations in Confirmatory Trials
Adapting the final sample size of a trial to the evidence accruing durin...
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Nonconstant hazard ratios in randomized controlled trials with composite endpoints
The hazard ratio is routinely used as a summary measure to assess the tr...
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Handling an uncertain control group event risk in noninferiority trials: noninferiority frontiers and the powerstabilising transformation
Background. Noninferiority (NI) trials are increasingly used to evaluate new treatments expected to have secondary advantages over standard of care, but similar efficacy on the primary outcome. When designing a NI trial with a binary primary outcome, the choice of effect measure for the NI margin has an important effect on sample size calculations; furthermore, if the control event risk observed is markedly different from that assumed, the trial can quickly lose power or the results become difficult to interpret. Methods. We propose a new way of designing NI trials to overcome the issues raised by unexpected control event risks by specifying a NI frontier, i.e. a curve defining the most appropriate noninferiority margin for each possible value of control event risk. We propose a fixed arcsine difference frontier, the powerstabilising transformation for binary outcomes. We propose and compare three ways of designing a trial using this frontier. Results. Testing and reporting on the arcsine scale leads to results which are challenging to interpret clinically. Working on the arcsine scale generally requires a larger sample size compared to the risk difference scale. Therefore, working on the risk difference scale, modifying the margin after observing the control event risk, might be preferable, as it requires a smaller sample size. However, this approach tends to slightly inflate type I error rate; a solution is to use a lower significance level for testing. When working on the risk ratio scale, the same approach leads to power levels above the nominal one, maintaining type I error under control. Conclusions. Our proposed methods of designing NI trials using powerstabilising frontiers make trial design more resilient to unexpected values of the control event risk, at the only cost of requiring larger sample sizes when the goal is to report results on the risk difference scale.
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