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An investigation into unblinded sample size re-estimation methodology in clinical trials

An investigation into unblinded sample size re-estimation methodology in clinical trials

Julia Edwards

The University of Sheffield

29 November 2019

Clinical trials aim to assess the effect of healthcare technologies on health outcomes. However, with the cost of clinical trials increasing, and challenges with recruitment and retention of participants, there is a need to improve the efficiency of trials.Clinical trials require a sample size calculation prior to the start of the trial. Prior knowledge of the treatment effect and its variability is required for these calculations, although these are often estimated. Therefore, a large number of studies have insufficient power at the end of a trial; meaning the trial may be unable to detect a difference between the treatments, even if one exists.An adaptive design is a type of trial that allows researchers to look at the data whilst the trial is ongoing at one or more specified time points (interim time points), and make modifications to the remainder of the trial if necessary. One potential modification is a sample size re-estimation (SSR).If possible, the treatment allocation is not revealed and the SSR is based on the variability of observed data (known as blinded SSR). However, sometimes it is necessary to reveal the treatment information (known as unblinded SSR). The knowledge of treatment group complicates the design of the trial, as knowing the treatment of patients increases the chance of seeing a treatment difference when in fact no such difference exists (false positive rate).One key methodology to counteract this effect is the “promising zone design” for unblinded SSR (Mehta & Pocock). In this situation, a statistician may calculate a measure called conditional power at the interim time point, indicating the projected power at the end of the study given the data observed so far. Conditional power (ranging from 0-100%) can fall into one of three zones:1.    `unfavourable' zone - the interim treatment effect is disappointing, and it is not worth the increase in sample size;2.   `favourable' zone - the interim treatment effect is sufficiently favourable and no increase is necessary to maintain power; 3.   `promising zone', - falling between unfavourable and favourable, indicates the interim effect is not too disappointing, but power is unlikely to be as good as planned in the design stage. If the sample size is increased according to a formula and a restriction of a maximum possible sample size, power can be maintained in the trial.There are, however, issues with this methodology, with criticisms that it is sub-optimal. An alternative approach recommends an alternative sample size increase over a larger boundary (Jennison & Turnbull), chosen such that the expected sample size is minimised. Finally, some researchers have proposed a stepwise rule for SSR (Liu & Hu). Whilst this design avoids the ability to back-calculate the interim treatment effect, the stepwise procedure could decrease overall final power.The proposed research will investigate three key elements.(1)   The conditional power calculation relies on an assumption of the future treatment effect: common choices assume the treatment effect originally planned in the design stage, or that the current trend will continue and is based on the observed data so far. It is currently unknown which assumption to use, or if indeed there is an alternative assumption. (2)   Operational factors in the planned SSR: elements such as the timing of the interim analysis, length of time required to collect data, recruitment rates and incorporation of additional decisions at the interim such as stopping a trial early could play a huge role in the decision of SSR design choice. There is no current recommendation any of these factors, and indeed the full impact each could have on the interim decisions and final power.(3)   An investigation of SSR rules: including a comparison of rules within the three frameworks; promising zone, Jennison & Turnbull, and stepwise increase. This will include investigating the impact of the decision at the interim analysis.

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Statistical Analysis Plan

Edwards, Julia (2020) Unblinded Sample Size Re-estimation in Randomised Controlled Trials. PhD thesis, University of Sheffield.