Estimands and Complex Innovative Designs

Collignon, Olivier; Schiel, Anja; Burman, Carl‐Fredrik; Rufibach, Kaspar; Posch, Martin; Bretz, Frank

doi:10.1002/cpt.2575

Cited by 14 publications

(8 citation statements)

References 51 publications

(120 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the assessment of estimators in clinical trials, the estimand framework [17] has become an important tool. While the estimand, defined as the target of estimation, is derived from the trial objective and is therefore not directly affected by the use of non-concurrent controls, the properties of estimators depend on if and how nonconcurrent controls are employed in the estimation of treatment effects [18]. Time trends can affect estimators through several aspects of the data generating process in a clinical trial, such as the study population, the measurement of endpoints, or the impact of intercurrent events.…”

Section: Discussionmentioning

confidence: 99%

On model-based time trend adjustments in platform trials with non-concurrent controls

Roig

Krotka

Burman

et al. 2022

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

Background Platform trials can evaluate the efficacy of several experimental treatments compared to a control. The number of experimental treatments is not fixed, as arms may be added or removed as the trial progresses. Platform trials are more efficient than independent parallel group trials because of using shared control groups. However, for a treatment entering the trial at a later time point, the control group is divided into concurrent controls, consisting of patients randomised to control when that treatment arm is in the platform, and non-concurrent controls, patients randomised before. Using non-concurrent controls in addition to concurrent controls can improve the trial’s efficiency by increasing power and reducing the required sample size, but can introduce bias due to time trends. Methods We focus on a platform trial with two treatment arms and a common control arm. Assuming that the second treatment arm is added at a later time, we assess the robustness of recently proposed model-based approaches to adjust for time trends when utilizing non-concurrent controls. In particular, we consider approaches where time trends are modeled either as linear in time or as a step function, with steps at time points where treatments enter or leave the platform trial. For trials with continuous or binary outcomes, we investigate the type 1 error rate and power of testing the efficacy of the newly added arm, as well as the bias and root mean squared error of treatment effect estimates under a range of scenarios. In addition to scenarios where time trends are equal across arms, we investigate settings with different time trends or time trends that are not additive in the scale of the model. Results A step function model, fitted on data from all treatment arms, gives increased power while controlling the type 1 error, as long as the time trends are equal for the different arms and additive on the model scale. This holds even if the shape of the time trend deviates from a step function when patients are allocated to arms by block randomisation. However, if time trends differ between arms or are not additive to treatment effects in the scale of the model, the type 1 error rate may be inflated. Conclusions The efficiency gained by using step function models to incorporate non-concurrent controls can outweigh potential risks of biases, especially in settings with small sample sizes. Such biases may arise if the model assumptions of equality and additivity of time trends are not satisfied. However, the specifics of the trial, scientific plausibility of different time trends, and robustness of results should be carefully considered.

show abstract

Section: Discussionmentioning

confidence: 99%

On model-based time trend adjustments in platform trials with non-concurrent controls

Roig

Krotka

Burman

et al. 2022

BMC Med Res Methodol

Self Cite

View full text Add to dashboard Cite

show abstract

“…Even then, the ethical challenges posed by umbrella trials need careful consideration ( 78 ). Furthermore, the use of the estimand framework in master protocols has become an area of growing consideration ( 79 , 80 ). This may lead to substantial transformations in the way umbrella trials should be analyzed and reported over the coming years which are not discussed in our work.…”

Section: Discussionmentioning

confidence: 99%

Design and analysis of umbrella trials: Where do we stand?

Ouma

Wason²,

Zheng³

et al. 2022

Front. Med.

View full text Add to dashboard Cite

BackgroundThe efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.MethodsWe conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.FindingsWe identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.ConclusionsUmbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

show abstract

“…Early conclusions regarding antitumor activity are usually based on binary response endpoints, duration of response, and/or time to first response. In some studies, to use more information in decision making, it could be relevant to use methodology which facilitates borrowing information across different cohorts, 5 such as the exchangeability‐nonexchangeability approach (EXNEX) 6 and the multiple‐cohort expansion (MUCE, e.g., inferential basket studies) design 7 . We will restrict the scope of this paper to non‐comparative studies, binary response type endpoints, without information borrowing.…”

Section: Estimands To Assess Response In Oncology Clinical Trialsmentioning

confidence: 99%

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

Englert

Mercier

Pilling

et al. 2023

Pharmaceutical Statistics

View full text Add to dashboard Cite

The addendum of the ICH E9 guideline on the statistical principles for clinical trials introduced the estimand framework. The framework is designed to strengthen the dialog between different stakeholders, to introduce greater clarity in the clinical trial objectives and to provide alignment between the estimand and statistical analysis. Estimand framework related publications thus far have mainly focused on randomized clinical trials. The intention of the Early Development Estimand Nexus (EDEN), a task force of the cross‐industry Oncology Estimand Working Group (www.oncoestimand.org), is to apply it to single arms Phase 1b or Phase 2 trials designed to detect a treatment‐related efficacy signal, typically measured by objective response rate. Key recommendations regarding the estimand attributes include that in a single arm early clinical trial, the treatment attribute should start when the first dose is received by the participant. Focusing on the estimation of an absolute effect, the population‐level summary measure should reflect only the property used for the estimation. Another major component introduced in the ICH E9 addendum is the definition of intercurrent events and the associated possible ways to handle them. Different strategies reflect different clinical questions of interest that can be answered based on the journeys an individual subject can take during a trial. We provide detailed strategy recommendations for intercurrent events typically seen in early‐stage oncology. We highlight where implicit assumptions should be made transparent as whenever follow‐up is suspended, a while‐on‐treatment strategy is implied.

show abstract

Estimands and Complex Innovative Designs

Cited by 14 publications

References 51 publications

On model-based time trend adjustments in platform trials with non-concurrent controls

On model-based time trend adjustments in platform trials with non-concurrent controls

Design and analysis of umbrella trials: Where do we stand?

Defining estimands for efficacy assessment in single arm phase 1b or phase 2 clinical trials in oncology early development

Contact Info

Product

Resources

About