Superiority of combining two independent trials in interim futility analysis

Deng, Qi; Zhang, Ying-Ying; Roy, Dooti; Chen, Ming‐Hui

doi:10.1177/0962280219840383

Cited by 7 publications

(11 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conditional power for each study was calculated, with a future estimate calculated based on pooled data from EMERGE and ENGAGE. The use of pooled data for the future estimate was based on the assumption that the pooling approach had better operating characteristics than the approach based on single-trial data, in the event that small to moderate heterogeneity with regard to treatment effects existed between the two studies (24). As the two phase 3 studies were identically designed, large heterogeneity was not anticipated.…”

Section: Futility Analysismentioning

confidence: 99%

Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease

et al. 2022

View full text Add to dashboard Cite

BACKGROUND: Alzheimer 's disease is a progressive, irreversible, and fatal disease for which accumulation of amyloid beta is thought to play a key role in pathogenesis. Aducanumab is a human monoclonal antibody directed against aggregated soluble and insoluble forms of amyloid beta. OBJECTIVES: We evaluated the efficacy and safety of aducanumab in early Alzheimer's disease. DESIGN: EMERGE and ENGAGE were two randomized, double-blind, placebo-controlled, global, phase 3 studies of aducanumab in patients with early Alzheimer's disease. SETTING: These studies involved 348 sites in 20 countries. PARTICIPANTS: Participants included 1638 (EMERGE) and 1647 (ENGAGE) patients (aged 50-85 years, confirmed amyloid pathology) who met clinical criteria for mild cognitive impairment due to Alzheimer's disease or mild Alzheimer's disease dementia, of which 1812 (55.2%) completed the study. INTERVENTION: Participants were randomly assigned 1:1:1 to receive aducanumab low dose (3 or 6 mg/kg target dose), high dose (10 mg/kg target dose), or placebo via IV infusion once every 4 weeks over 76 weeks. MEASUREMENTS: The primary outcome measure was change from baseline to week 78 on the Clinical Dementia Rating Sum of Boxes (CDR-SB), an integrated scale that assesses both function and cognition. Other measures included safety assessments; secondary and tertiary clinical outcomes that assessed cognition, function, and behavior; and biomarker endpoints. RESULTS: EMERGE and ENGAGE were halted based on futility analysis of data pooled from the first approximately 50% of enrolled patients; subsequent efficacy analyses included data from a larger data set collected up to futility declaration and followed prespecified statistical analyses. The primary endpoint was met in EMERGE (difference of −0.39 for highdose aducanumab vs placebo [95% CI, −0.69 to −0.09; P=.012; 22% decrease]) but not in ENGAGE (difference of 0.03, [95% CI, −0.26 to 0.33; P=.833; 2% increase]). Results of biomarker substudies confirmed target engagement and dose-dependent reduction in markers of Alzheimer's disease pathophysiology. The most common adverse event was amyloid-related imaging abnormalities-edema. CONCLUSIONS: Data from EMERGE demonstrated a statistically significant change across all four primary and secondary clinical endpoints. ENGAGE did not meet its primary or secondary endpoints. A dose-and time-dependent reduction in pathophysiological markers of Alzheimer 's disease was observed in both trials.

show abstract

Section: Futility Analysismentioning

confidence: 99%

Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In an effort to limit risk or increase efficiency, many Phase IIb/III AD trials include interim analyses to arrive at earlier answers, as well as stopping rules that use formal statistical methods for evaluating interim data and determining whether a study may or should be stopped early 49 . If interim data provide compelling evidence of treatment efficacy or if a significant difference between experimental and control groups is unlikely to be obtained, pre‐planned rules may guide early stopping of a trial 50 .…”

Section: Efficient Design and Analysesmentioning

confidence: 99%

Decision making in clinical trials: Interim analyses, innovative design, and biomarkers

Welsh‐Bohmer,

Kerchner,

Dhadda

et al. 2023

A&D Transl Res & Clin Interv

View full text Add to dashboard Cite

The efficient and accurate execution of clinical trials testing novel treatments for Alzheimer's disease (AD) is a critical component of the field's collective efforts to develop effective disease‐modifying treatments for AD. The lengthy and heterogeneous nature of clinical progression in AD contributes to the challenges inherent in demonstrating a clinically meaningful benefit of any potential new AD therapy. The failure of many large and expensive clinical trials to date has prompted a focus on optimizing all aspects of decision making, to not only expedite the development of new treatments, but also maximize the value of the information that each clinical trial yields, so that all future clinical trials (including those that are negative) will contribute toward advancing the field. To address this important topic the Alzheimer's Association Research Roundtable convened December 1–2, 2020. The goals focused around identifying new directions and actionable steps to enhance clinical trial decision making in planned future studies.

show abstract

“…, which has been derived in Deng et al 6 From Gallo et al 18 and Spiegelhalter et al, 19 the predictive power for Study 1, PP 1 M (Y 1 , Y 3 ), can be derived as follows:…”

Section: Interim Analysis Based On Predictive Powermentioning

confidence: 99%

“…This probability may be measured using conditional power under frequentist framework, predictive power in a semi-Bayesian approach and predictive probability in a fully Bayesian approach. 5,6 Outcome of an interim futility analysis is determined by a comparison of this probability with a predefined threshold. If the probability is less than the threshold, then the analysis outcome implies a "No-Go" and the trial could be terminated; otherwise, a "Go" outcome is implied and the trial will proceed until the next milestone.…”

Section: Introductionmentioning

confidence: 99%

“…Under the familiar setting of two parallel studies, there have been proposals for interim decision-making about the individual studies by incorporating information from both studies. 8,6 One fundamental assumption that needs to hold in order to gain from such combined interim analyses is that the studies with similar designs would lead to similar study results. In practice, however, investigators would not know the true distribution of the treatment effects.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian hierarchical modeling in interim futility analysis for two parallel clinical trials

Li,

Roy,

Deng

2023

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

Incorporating interim analysis into a trial design is gaining popularity in the field of confirmatory clinical trials, where two studies may be conducted in parallel (ie, twin studies) in order to provide substantial evidence per the requirement of FDA guidance. Interim futility analysis provides a chance to check for the "disaster" scenario when the treatment has a high probability to be not more efficacious than the control. Therefore, it is an efficient tool to mitigate risk of running a complete and expansive trial under such scenario. There is no agreement among trial designers that interim analysis should be based on individual study data or pooled data under the twin study scenario. In fact, it is a dilemma for most scientists when specifying the interim analysis strategy at the design stage as the true treatment effects of the twin studies are unknown no matter how similar they are intended to be. To address the issue, we developed a Bayesian hierarchical modeling method to allow dynamic data borrowing between twin studies and demonstrated a favorable characteristic of the new method over the separate and pooled analyses. We evaluated a wide spectrum of the heterogeneity hyperparameters and visualized its critical impact on the Bayesian model's characteristic. Based on the evaluation, we made a suggestion on the heterogeneity hyperparameter selection independent of any a priori knowledge. We also applied our method to a case study where predictive powers of different methods are compared.

show abstract

Superiority of combining two independent trials in interim futility analysis

Cited by 7 publications

References 27 publications

Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease

Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer’s Disease

Decision making in clinical trials: Interim analyses, innovative design, and biomarkers

Bayesian hierarchical modeling in interim futility analysis for two parallel clinical trials

Contact Info

Product

Resources

About