Empowering phase II clinical trials to reduce phase III failures

Martini, Daniele De

doi:10.1002/pst.1980

Cited by 24 publications

(24 citation statements)

References 55 publications

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“…, it can be seen that with the framework in the unadjusted and additive case one ends up in the "desirable" (according to De Martini [4,25]) range of 2/3 and also in the multiplicative case with lower d Ã 2 / d Ã 3 , one still exceeds the often used 1/4. However, it should be noted that the total optimal sample size is highest for the multiplicative case.…”

Section: Discussionmentioning

confidence: 96%

“…Based on our results, we highly recommend using (multiplicatively) adjusted phase II treatment effect estimates for calculation of the phase III number of events in a phase II/III drug development program with go/no-go decision rule (compare Chuang-Stein & Kirby [14], Kirby et al [15] and De Martini [4,25]). However, as our results also show that the optimal design parameters of each method depend on the cost and benefit parameters as well as on the applied prior distribution, no general rule exists.…”

Section: Discussionmentioning

confidence: 99%

“…As proposed by De Martini [4,25], the ratio of the number of events in phase II and III will also be calculated. The program is considered to be successful, if the onesided null hypothesis H 0 : θ ≤ 0 is rejected in favour of H 1 : θ > 0 at a one-sided significance level α.…”

Section: Go/no-go Criteria Calculation Of Expected Number Of Events mentioning

confidence: 99%

“…De Martini [4,25] reports that the phase II sample size should be almost as large as the ideal phase III sample size (at least 2/3 of the latter) in order to have a sufficiently good information basis for phase III planning. He criticizes that in practice this ratio is only 1/4 on average and that an increase in sponsorship gains from drug development through larger phase II studies has not yet been well investigated.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the costs of phase III studies have increased remarkably in recent years [ 2 , 3 ], while failure rates are quite high (approx. 45%, see [ 4 ] and the reference mentioned therein). Therefore, the availability and application of quantitative methods for decision making, which should be data-driven and objective, is desirable [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

Erdmann

Kirchner

Götte

et al. 2020

BMC Med Res Methodol

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Background Go/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%. Methods We integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the “right amount of adjustment”, the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies. Results In general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the “naïve” program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design. Conclusion Individual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

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Section: Discussionmentioning

confidence: 96%

Section: Discussionmentioning

confidence: 99%

Section: Go/no-go Criteria Calculation Of Expected Number Of Events mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

Erdmann

Kirchner

Götte

et al. 2020

BMC Med Res Methodol

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Treatment de‐escalation for HPV+ oropharyngeal cancer: A systematic review and meta‐analysis

et al. 2022

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Human Papillomavirus (HPV) related oropharyngeal carcinoma (OPC) carries a better prognosis compared with HPV‐counterparts, thereby pushing the adoption of de‐intensification treatment approaches as new strategies to preserve superior oncologic outcomes while minimizing toxicity. We evaluated the effect of treatment de‐intensification in terms of overall survival (OS), progression‐free survival (PFS), locoregional and distant control (LRC and DM) by selecting prospective or retrospective studies, providing outcome data with reduced intensification versus standard curative treatment in HPV+ OPC patients, with a systematic analysis till September 2020. The primary outcome of interest was OS. Secondary endpoints were PFS, LRC, and DM expressed as HR. A total of 55 studies (from 1393 screened references) were employed for quantitative synthesis for 38 929 patients. Among n = 48 studies with data available, de‐intensified treatments reduced OS in HPV+ OPCs (HR = 1.33, 95% CI 1.17–1.52; p < 0.01). In de‐escalated treatments, PFS was also decreased (HR = 2.11, 95% CI 1.65–2.69; p < 0.01). Compared with standard treatments, reduced intensity approaches were associated with reduced locoregional and distant disease control (HR = 2.51, 95% CI 1.75–3.59; p < 0.01; and HR = 1.9, 95% CI 1.25–2.9; p < 0.01). Chemoradiation improved survival in a definitive curative setting compared with radiotherapy alone (HR = 1.42, 95% CI 1.16–1.75; p < 0.01). When adjuvant treatments were compared, standard and de‐escalation strategies provided similar OS. In conclusion, in patients with HPV+ OPC, de‐escalation treatments should not be widely and agnostically adopted in clinical practice, as therein lies a concrete risk of offering a sub‐optimal treatment to patients.

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An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

Götte

Kirchner

Krisam

et al. 2022

Pharmaceutical Statistics

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In early clinical development, randomized controlled trials (RCT) or single‐arm trials with external controls (SATwEC) are design options, which allow adjustment for confounding: RCT via design, SATwEC via analysis using propensity score methods. SATwEC requires less investment than RCT. However, if the confounder space substantially differs between the experimental and external control group, the SATwEC might lead to inappropriate decisions for further development. We develop an adaptive two‐stage design (ATD) for early clinical development that reduces the risk of unreliable decision‐making at the end of a SATwEC. In Stage I, subjects are solely assigned to the experimental group. If at the interim the propensity score distributions of internal and external data are comparable based on the preference score, the subjects in stage II will again be solely assigned to the experimental arm; if not, a randomized stage II will be conducted. In a simulation study guided by a motivating example, data is generated using a time‐to‐event model with observable and unobservable confounders. The confounder space is varied to investigate the impact on false go/stop probabilities as well as a loss function, which reflects the quality of treatment effect estimates and decision‐making. The proposed ATD provides a compromise between optimizing quality (as expressed by false go/stop probabilities and the loss function) and investment (defined by sample size and trial duration).

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Empowering phase II clinical trials to reduce phase III failures

Cited by 24 publications

References 55 publications

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

Optimal designs for phase II/III drug development programs including methods for discounting of phase II results

Treatment de‐escalation for HPV+ oropharyngeal cancer: A systematic review and meta‐analysis

An adaptive design for early clinical development including interim decision for single‐arm trial with external controls or randomized trial

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