Y H Joshua Chen1, Caiyan Li2, K K Gordon Lan3. 1. Sanofi Pasteur, Swiftwater, PA, USA. 2. Takeda, Chicago, IL, USA caiyan07@gmail.com. 3. Janssen Pharmaceutical Companies of Johnson and Johnson, Raritan, NJ, USA.
Abstract
BACKGROUND: For a carefully planned and well-designed Phase 3 confirmatory trial, there is still a potential risk of failing to meet the study objective due to possible differences between Phase 2 and Phase 3 studies. As illustrated by the ENGAGE trial, potential sample size increase at an interim analysis can mitigate the risk for an otherwise underpowered study. Many approaches for sample size adjustment (SSA) require certain modifications to the conventional statistical method, such as changing critical values or using a weighted Z-statistic for final hypothesis testing. Without modification, the type I error rate can be inflated, primarily caused by sample size increase for nonpromising interim observation that is close to null or no treatment effect. As illustrated by the TOPICAL trial, increasing sample size for nonpromising interim result could waste limited resource on ineffective treatment. The modifications in these approaches are therefore unnecessary costs of flexibility/interpretability for unnecessary scenarios of sample size increase. PURPOSE: To discuss and illustrate the appropriateness of SSA based on promising interim results, that is, conditional power being greater than 50% (or CDL approach), in a carefully planned and well-designed Phase 3 confirmatory trial. METHODS: Two clinical trials are used to illustrate the clinical setting for the CDL approach and appropriateness of its application. Operating characteristics are assessed and compared to other methods using numeric computation. Hypothetical trials based on real clinical data are used to illustrate the approach. RESULTS: The CDL approach for SSA leads to a small increase in expected sample size resulting in a small power gain versus the fixed design. This indicates that adding SSA will not on average substantially affect the budget at the portfolio level. However, when the interim result is promising, the CDL approach can dramatically increase the conditional power therefore mitigating the risk of an otherwise underpowered study. LIMITATIONS: Implementation challenges of the SSA methods are not in the scope of this paper. SSA is not intended to replace careful design of a confirmatory trial; instead, it can mitigate the risk for a well-designed trial. CONCLUSIONS: The CDL approach for SSA based on promising interim results, that is, conditional power being greater than 50%, is particularly useful in mitigating the risk for a carefully planned and well-designed Phase 3 confirmatory trial. No modification to the conventional statistical procedure is necessary while the type I error rate is controlled. Such a feature of ''no interference,'' or no change to the conventional statistical procedure with or without sample size adjustment, is important for the interpretation of a confirmatory trial. Similar to the fixed design, carefully planned and well-designed group sequential studies can also benefit from SSA to mitigate the risk of failing to meet the study objective.
BACKGROUND: For a carefully planned and well-designed Phase 3 confirmatory trial, there is still a potential risk of failing to meet the study objective due to possible differences between Phase 2 and Phase 3 studies. As illustrated by the ENGAGE trial, potential sample size increase at an interim analysis can mitigate the risk for an otherwise underpowered study. Many approaches for sample size adjustment (SSA) require certain modifications to the conventional statistical method, such as changing critical values or using a weighted Z-statistic for final hypothesis testing. Without modification, the type I error rate can be inflated, primarily caused by sample size increase for nonpromising interim observation that is close to null or no treatment effect. As illustrated by the TOPICAL trial, increasing sample size for nonpromising interim result could waste limited resource on ineffective treatment. The modifications in these approaches are therefore unnecessary costs of flexibility/interpretability for unnecessary scenarios of sample size increase. PURPOSE: To discuss and illustrate the appropriateness of SSA based on promising interim results, that is, conditional power being greater than 50% (or CDL approach), in a carefully planned and well-designed Phase 3 confirmatory trial. METHODS: Two clinical trials are used to illustrate the clinical setting for the CDL approach and appropriateness of its application. Operating characteristics are assessed and compared to other methods using numeric computation. Hypothetical trials based on real clinical data are used to illustrate the approach. RESULTS: The CDL approach for SSA leads to a small increase in expected sample size resulting in a small power gain versus the fixed design. This indicates that adding SSA will not on average substantially affect the budget at the portfolio level. However, when the interim result is promising, the CDL approach can dramatically increase the conditional power therefore mitigating the risk of an otherwise underpowered study. LIMITATIONS: Implementation challenges of the SSA methods are not in the scope of this paper. SSA is not intended to replace careful design of a confirmatory trial; instead, it can mitigate the risk for a well-designed trial. CONCLUSIONS: The CDL approach for SSA based on promising interim results, that is, conditional power being greater than 50%, is particularly useful in mitigating the risk for a carefully planned and well-designed Phase 3 confirmatory trial. No modification to the conventional statistical procedure is necessary while the type I error rate is controlled. Such a feature of ''no interference,'' or no change to the conventional statistical procedure with or without sample size adjustment, is important for the interpretation of a confirmatory trial. Similar to the fixed design, carefully planned and well-designed group sequential studies can also benefit from SSA to mitigate the risk of failing to meet the study objective.
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: BMJ Date: 2020-06-17
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: Trials Date: 2020-06-17 Impact factor: 2.279
Authors: Joseph C C Chen; Rachael L Sumner; Venkat Krishnamurthy Naga; Nicholas Hoeh; Hafis Adetokunbo Ayeni; Vikrant Singh; Frederick Sundram; Douglas Campbell; Suresh Muthukumaraswamy Journal: Trials Date: 2020-02-10 Impact factor: 2.279